Small and medium-sized enterprises (SMEs) contribute significantly to global economies, in both advanced and emerging markets. For instance, in the European Union (EU), SMEs represent 99% of all businesses, employ two thirds of the work force and account for more than half of the region’s GDP. These numbers can be even higher in emerging markets.

In addition to these impressive numbers, SMEs, as they are generally run by entrepreneurs, contribute significantly to innovation and shaping future economies. They are present in all industries where barriers to entry can be relatively low and do not require a large group of people at the beginning. Recently, the SMEs that have drawn the most attention are Fintech firms.

Historically, SMEs face significantly more difficulties in obtaining bank loans when compared with their larger counterparts. They have to rely more on internal funding or funding from family and friends. This is because (1) SMEs generally possess higher default risk than individuals or large corporates due to their higher failure rate and (2) there is generally a lack of quality data for banks to assess the creditworthiness of SMEs to support their lending decisions.

However, thanks to Fintechs and government support, the availability of both higher quality traditional data and alternative data is now growing. Banks are therefore reconsidering SME lending. In this paper, we will focus on discussing SME credit scoring, as a critical tool for SME lending.

KEY TRENDS IN SME LENDING MARKET

SMEs account for the vast majority of business establishments globally, and most of them seek loans from banks for business development. They represent a unique segment in the lending market, as they require very different services from retail customers or large corporations. For instance, an SME may not have a large finance team to provide comprehensive financial records and business data for banks to make credit decisions. It may not have the time or the resources to go through a long loan application process. As a result, they have become a tough nut to crack and therefore a somewhat underserved segment.

However, in an era of technology enablement and financial inclusion, things are changing fast. With technology that enables fast customer onboarding and screening and the availability of alternative data for risk assessment and monitoring, SMEs are coming into the spotlight.

We have observed five key trends in the SME lending market, including changing SME business models, faster banking processes, greater regulatory support on Fintech adoption, rising competition from newcomers and growing service offerings.

Figure 1: Key trends in SME lending market

Source: Accuracy

Changing SME business models

SMEs across various industries are being forced to alter their business models drastically in order to stay in business. As technology adoption and digitisation have become global trends, there is no exception for SMEs. The development of new business models (i.e. asset-light, online-based) makes it difficult for banks to assess their credit quality from a traditional perspective.

Faster banking processes

With the help of technology and alternative data, Fintechs and some traditional banks are now able to process SME loan applications much more quickly. For instance, Liberis, a UK Fintech that provides finance for small businesses, can interface with a company’s bank account and dashboards to enable access to instant funding, which may be retrieved in a matter of minutes for SMEs that need it.

Regulatory support on Fintech

Financial technologies have driven global innovation in financial services. At the same time, they are altering the nature of commerce and end-user expectations for financial services. Regulatory bodies are increasingly open to innovations and supportive of the adoption of Fintech solutions. As such, we have seen a number of Fintech companies specifically targeting SME finance in the past few years.

Rising competition from newcomers

Alternative lending providers such as tech giants are entering the battlefield. Big players like Google, Amazon and Tencent, as well as their more regional counterparts, have been putting pressure on banks for some time. There is a good probability that this pressure will increase as Techfins increasingly use their potent consumer franchises and advanced digital capabilities to outbid banks, especially in SME lending. Competition in the field will be increasingly intense.

Increase in service offerings

The SME banking sector has transformed as a result of changes brought by Fintechs, Techfins, government and regulatory support, and challenger banks. SME clients now have more options than ever to obtain access to financing. Banks must modify their SME offerings to compete in an environment where SMEs are looking for a suite of services (e.g. invoicing, corporate credit cards, payroll management) and one-stop-shop experiences.

TREDING THEMES IN SME CREDIT SCORING

To tap into and expand their business in SME lending, banks needs a series of tools, ranging from fast customer onboarding platforms to accurate risk assessment and monitoring tools. In this whitepaper, we dedicate our discussion to the use of credit scoring for risk assessment and customer acquisition.

Credit scoring is a statistical method for determining a borrower’s creditworthiness by combining a number of risk factors into a single score. We have observed several trends in SME credit scoring: (1) the use of alternative data and the adoption of data sharing platforms; (2) the adoption of advanced analytic solutions; and (3) the streamlining and automation of credit approval processes.

Figure 2: Key trends in SME credit scoring

Source: Accuracy

Alternative data and data sharing platforms

Traditionally, banks use a limited set of data to perform credit decisioning. These can be grouped into financial variables and non-financial variables. In recent years, the banking industry has seen a rise in the use of alternative data, which adds value throughout the customer banking lifecycle, particularly during the credit evaluation process. As the value of alternative data has gradually attracted more attention from banks and regulators, so has the concept of Open Banking and data sharing platforms. For example, the Commercial Data Interchange platform of the Hong Kong Monetary Authority (HKMA) aims to connect numerous SME data owners and providers with financial institutions for easier, faster and better credit assessment.

Figure 3: Types of credit scoring data

Source: Accuracy

Alternative data

Up until now, the majority of banks still assess potential borrowers’ creditworthiness using traditional credit data and methods. However, traditional data only captures the tip of the iceberg when it comes to the borrower’s information. The use of alternative data presents two attractive opportunities for banks. Firstly, alternative data help banks enhance model performance. Secondly, they help banks to expand the total addressable market (TAM) as the data make sound credit assessments possible. Meanwhile, the growth in computational power has effectively lifted the barrier of collecting and processing big data.

Alternative data are generated everywhere in the digital footprint of a company. The following figure describes typical examples of alternative data.

Figure 4: Alternative data providers and sources

Source: Accuracy

Data Sharing and Open Data

Governments and industries across the world are promoting the concept of open data and data sharing. The idea is to facilitate data transmission among various stakeholders to enhance overall efficiency. In some cases, banks are being urged to exchange customer data in a machine-readable format so that customers can access and securely transmit their banking information to reliable parties. This makes it easier for borrowers, especially SMEs, to switch financial service providers seamlessly, increasing availability of funding sources and opportunities.

One recent example of the use of alternative data in SME financing is the launch of the Commercial Data Interchange (CDI), a core pillar of HKMA’s “Fintech 2025” strategy. The HKMA officially launched the CDI in October 2022, with a proof-of-concept study dating back to November 2020. During the pilot launch phase, the CDI facilitated over HKD 1.6 billion of SME loans or 800+ loan cases. This initiative aimed to enable more efficient financial intermediation in the banking system and to facilitate the innovative use of commercial data to enhance financial services.

The CDI connects five types of stakeholder, namely, data owners (i.e. SMEs), data consumers (i.e. financial institutions), analytics service providers, solutions providers and data providers (i.e. commercial entities that collect the digital footprint of data owners). With the CDI, each bank and data provider has connections to the platform, making it simple for them to link their systems to the infrastructure for data access. This allows SMEs to share their digital footprints with their banks. The data help banks in a number of ways, including KYC, credit underwriting, product development, customer acquisition and credit monitoring. As we have highlighted, the use of alternative data is especially important for credit decisioning.

Figure 5: HKMA CDI initiative

Source: HKMA CDI, Accuracy

Hong Kong is not alone. The Data Governance Act, as approved by the European Parliament in April 2022 and applicable from September 2023, aims to increase data sharing in the EU so that businesses and start-ups can access more data. The regulations will allow greater use of data gathered in various public sector domains. They also enable the construction of shared European data platforms in various fields, including finance.

Additionally, policymakers in India, Japan, Singapore and South Korea are proposing a number of initiatives to encourage and accelerate the adoption of data sharing frameworks in the banking industry. For instance, the Monetary Authority of Singapore and the Association of Banks in Singapore have released an API playbook to promote data interchange and communication between banks and Fintechs.

Advanced analytics solutions

Banks are searching for better credit scoring techniques to improve the predictive power of their models. For example, they are developing or considering AI and machine learning for their credit scoring. These methodologies are more sensitive to real-time indicators of an SME’s creditworthiness than traditional credit scoring methods.

Decision tree and random forest are among the most commonly considered machine learning techniques that can be applied in SME credit scoring. Researchers are also exploring new solutions such as hybrid BWM and TOPSIS, when facing issues of insufficient data.[1] A detailed discussion on various advanced analytics solutions can be found in the next part of this paper.

Leveraging the enormous amounts of data gathered, Shopify, a leading all-in-one e-commerce platform that powers millions of businesses globally, has become a leader in using machine-learning techniques. Not surprisingly, it has launched Shopify Capital, a data-powered product that enables merchants to secure funding and accelerate their business growth. According to the company, Shopify has constructed Shopify Capital using a version of a recurrent neural network (RNN) that analyses more than 70 million data points across the Shopify platform to understand trends in merchants’ growth potential and provide cash advances that match their business needs. Since its inception, Shopify Capital has provided over USD 3.8 billion in funding.

Process streamlining & automation

In this digital era, another rising trend is the demand for seamless and automated credit approval processes. This trend is prominent in all phases of credit application, from client interactions to data collection (e.g. use of API, open banking, the CDI initiative), credit decision-making (automated models) and result communication (workflow streamlining and integration). Automation’s ultimate goal is to speed up the banking services for clients while reducing decision-making time, saving money, and improving productivity and efficiency for banks. In some cases, SME loan processing is part of the broader banking CRM suite, making it easier for banks to manage the whole customer lifecycle digitally.

Let’s take NeoGrowth, for example. NeoGrowth is a pioneer in SME lending in India, with a unique underwriting model based on digital transactions. The company has used technology to provide consumers with a smooth and seamless digital experience, where the entire process flow – from lead generation to loan origination, approval, disbursement and collections – is handled digitally.

[1] BWM – best worst method; TOPSIS – technique for order of preference by similarity to ideal solution

Figure 6: Digitally integrated operations in SME loan lifecycle

Source: NeoGrowth annual report, Accuracy

SME CREDIT SCORING APPROACHES

SME credit scoring refers to risk models to help financial institutions gauge SMEs’ creditworthiness and risk level. Traditionally, most banks use credit scorecards developed based on logistic regression for its simplicity to use and interpret. However, as we mentioned above, with the rapid development of data and analytics fields, some banks have started to adopt more advanced and dynamic models. In particular, decision models based on machine-learning techniques (e.g. decision tree, random forests) have gained popularity in recent years.

Figure 7: SME credit scoring flow

Source: Accuracy

Logistic regression-based credit scorecards

Credit scorecards developed by a combination of weight of evidence (WoE) transformation and logistic regression are among the most commonly used credit decision tools in banks. These credit scorecards were widely used in the past few decades; they have been well tested and have proved their effectiveness. Today, they are still the most popular scorecards used and maintained by banks, thanks to their simplicity to use and explain, while remaining effective.

There are seven important steps in developing a logistic-regression-based SME credit scorecard, including data processing, variable transformation and selection, logistic regression, performance inference, segmentation analysis, scorecard scaling and scorecard validation. In contrast to retail credit scorecards, segmentation analysis is usually performed for SME scorecards as companies in different sectors may exhibit very different risk characteristics. A detailed discussion on retail credit scorecards can be found in our previous whitepaper – FINANCIAL SERVICES & BANKING: RETAIL BANKING TRANSFORMATION – CREDIT SCORING.

Figure 8: major steps in SME credit scorecard development

Source: Accuracy

Decision tree

Machine learning now makes a substantial contribution to making credit decisions thanks to the rapid growth in data availability and computer capacity. One of the most popular supervised learning techniques is tree-based machine learning.

A decision tree is made up of two parts: branches and nodes that use various features from a dataset at each node to recursively partition a training sample. The algorithm iterates through all conceivable binary splits in search of the feature and related cut-off value that best distinguishes one side as having predominantly higher credit quality and the other as having relatively low credit quality. As an example, we can build a decision tree as below:

Figure 9: Illustrative decision tree

Source: Accuracy

• The most crucial factor is the interest-expense-to-sales ratio. The sample model decides that when data in the root node is divided into instances with the ratio < 2.5% and those with the ratio ≥ 2.5%, the figure of merit is optimised.

• Then, until it reaches the leaf node where the stopping requirement is met, this process is repeated for each new daughter node, i.e. loan size, working-capital-to-debt ratio, firm age, and cash to sale in this case.

• Finally, it provides the probability of default for each leaf node, of which the threshold is 0.2 in this case. Any borrowers that fall within the <0.2 criterion shall be granted the loan, while the others are rejected. This threshold is at the discretion of the lender.

Random forest

A random forest combines many different decision trees to get a prediction that is more precise and reliable. When compared with a single decision tree, a random forest avoids overfitting concerns, especially when there are enough trees in the forest.

To improve performance, numerous decision trees should be created in a random forest. The distinctness of each decision tree in the random forest is ensured by the random selection of data subsets and features. Overfitting is prevented since the model outcome is based on the combined predictions from each individual decision tree model. However, it is crucial to note that the individual decision tree models should not correlate highly with one another in this situation. The random forest approach does not make any assumptions about the data or its distribution, unlike many other algorithms (such as linear regression, SVM, etc.). Consequently, it typically only needs minor data transformations. As the random forest technique uses random feature subsets, it can work well with high-dimensional datasets (a dataset with a large number of features).

The random forest is especially effective compared with other models under the following circumstances:

• When there are outliers in the dataset, the random forest technique is unaffected by them.

• Many algorithms may take noise in the dataset as patterns (or extra manual power is required to remove outliers); however, the bagging method employed in random forest ensures that the noise in the dataset is not mistaken as signals or patterns.

• The random forest includes efficient methods to estimate missing values and preserve accuracy when there are missing values in the dataset, even when a sizable fraction of the data is missing.

Figure 10: Illustrative random forest structure

Source: Accuracy

Hybrid BWM and TOPSIS method

In addition to logistic regression and machine-learning techniques, an alternative method to develop an SME credit-scoring model is the hybrid BWM and TOPSIS.

• BWM is a decision-making logic tool that requires fewer data and less effort in development. It aims to find the optimal weights by minimising the gaps between actual weights and business judgement. Specifically, this process can be described as a linear model.

• TOPSIS is a tactic to determine an SME applicant’s relative position in contrast to a pool of borrowers. It finds the relative rank by calculating the weighted normalised matrix, obtaining the positive and negative ideal solutions, computing the Euclidean distance of the applicant between the positive and negative solutions and computing the relative closeness to the ideal solution.

This hybrid BWM and TOPSIS method requires judgement that is more subjective, but it offers an unparalleled performance in terms of cost, ease of development and implementation, and flexibility.

Figure 11: Illustrative steps in BWM and TOPSIS method

Source: Accuracy

Comparison of different modelling approaches

Overall, different modelling techniques, including but not limited to the those stated above, can be used to facilitate credit decisioning. These methodologies have their relative strengths and weaknesses, which we summarise in the below table.

Figure 12: Comparison of different modelling approaches

Source: Accuracy

FINAL REMARKS

SMEs make up a sizeable portion of the economies of both developed and emerging markets. Providing access to financing for small businesses has been a challenging task due to a variety of factors, including the expense and difficulty involved in determining the creditworthiness of small businesses with a lack of sufficient quality data.

However, banks are now able to lower the costs of originating and underwriting loans to SMEs while also increasing the performance of their SME loan portfolios, leveraging alternative data and new modelling techniques. These developments have led to an overall increase in the financing accessible to SMEs, and in time, will drive employment and economic growth.

At Accuracy, we have created our own SME rating model, Accur’Rating®. We originally developed the rating model using logistic regression but have recently migrated it to be a random forest model. We use this model to help clients evaluate investments in private debt, understand the credit quality of various corporates, etc.

We are in an era in which SMEs will become even more important for global economic development and driving innovation. With the right incentives, technology and knowledge, now is the time for banks to tap into and expand SME banking.

1. Digital transformation in retail banking industry

Over the past 10 years, retail banking has experienced a new wave of digital transformation. We have seen the rise of FinTechs and TechFins, which have brought integrated and faster financial services to their customers, whether banked or unbanked. With more advanced and agile technology solutions, and potentially less restrictive regulations, these challengers were able to boom in both developed and developing markets. Nowadays, retail customers are increasingly asking for one-stop shops and seamless customer experiences after having experienced the super-apps. With the global pandemic over the past two years, this trend has only accelerated.

Against this backdrop, traditional retail banks are not left with much choice but to undertake necessary digital transformations to meet their customers’ expectations. Many have done so in the past few years, and the rest are certainly at least planning to do so in the near future.

Today, the industry is evolving rapidly. Open banking and APIs are driving the development of new ecosystems for financial services, in which multiple players, traditional and non-traditional alike, are competing against each other.
In this article, we will discuss the key trends in the retail banking industry, followed by an analysis of the future of traditional retail banks and digital banks.

2. Trends in retail banking transformation

Many retail banking transformations are happening in the market. We can broadly categorise them into three types: (1) data enablement, advanced analytics and data-driven business decision-making, (2) customer experience 2.0 and (3) automation of end-to-end services (i.e. adoption of technology for on-boarding, e-KYC, risk management, internal controls, etc.).

Figure 1: Key trends in retail banking transformation

Source: Accuracy analysis

Trend 1: Data enablement, advanced analytics and data-driven business decision-making

Decision-making processes in retail banking are making increasing use of big data as well as AI & machine learning. This trend has disrupted almost all aspects of banking, from how banks on-board customers to how they empower them. Meanwhile, this trend also serves as the cornerstone of other transformation and innovation trends that are reshaping the retail banking landscape.

It is clear to see that technological advancement has been the driving force behind retail banking evolution. The advent of modern computers significantly accelerated banking processes and enabled computations that had been impossible beforehand. The use of the internet, which facilitates information communication and flattens the financial world, represents another huge technological leap in the retail banking industry. And most recently, over the last decade, the penetration rate of smartphones has been rising significantly, resulting in the proliferation of mobile banking. In the future, we believe that big data and AI & machine learning will drive new waves of transformation.

Big data use case – customer segmentation. One such example is micro-segmentation. With the increased use of data and advanced AI capability, retail banks can generate dynamic and granular client segments. The development of big data analytics and the increasing awareness and accessibility of alternative data have gradually enabled banks to make use of more valuable data in a cost-effective way. Retail banking has long been a data-driven business, where data is generated at every stage of the customer journey. However, in the past, most banks did not have an efficient process or the necessary IT infrastructure to realise the data’s potential. But traditional data use was just the tip of the iceberg. Huge amounts of alternative data, whether structured or unstructured, are generated every second from various data sources internally and externally. The value of data can be further ‘mined’ if it is combined with AI and machine learning techniques. Granular segmentation of customers can help the development and marketing of hyper-personalised products and services, as well as the optimisation of product pricing.

Alternative data use case – tapping into underserved segments. The use of alternative data not only improves customer segmentation but also enables banks to assess the creditworthiness of untapped customer segments. This helps to extend financial services to the two billion unbanked adults globally. Another opportunity lies in the SME market. Currently, many SMEs are underserved by traditional banks or are often subject to unsatisfactory credit terms. As a result, many FinTech and TechFin firms embrace the opportunity to provide alternative financing to SMEs. Kabbage, which was acquired by American Express in 2020, uses an automated lending platform to provide finance to small businesses and consumers. The FinTech company uses alternative data, such as business volume, transaction volume, time in business, statistics from e-commerce platforms or even social media activity to approve the funding. Additional benefits provided by digital SME cash flow solutions also include simplicity, speed, and transparent and real-time monitoring for small businesses.

Machine learning use case – credit decision-making. Another trend is leveraging machine-learning models to optimise business decision-making. The best way to imagine machine learning is to think of it as a replacement for traditional rule-based models. One real-world example of a rule-based model is a credit scorecard. Traditionally, a scorecard is developed by picking risk factors with high predictive power from a full list of human-determined risk factor candidates. The scorecard is a static, rule-based model for banks to make credit decisions. However, these mechanisms are not designed to capture complicated relationships and can become outdated if not enhanced frequently.

Nevertheless, a machine-learning model requires much more data for training. The calculation algorithm might also explore more complicated and non-trivial relationships between data. These are the underlying reasons why ML models can outperform traditional ones. By way of illustration, several FinTech start-ups in retail lending, such as Affirm and Upstart in the US, have developed ML models that, according to their analysis, can outperform their FICO counterparts by a wide margin (approving more customers with lower losses or incurring fewer losses with the same amount of customers).

Figure 2: Affirm ML modelling example

Note: For a given level of risk, our proprietary model is capable of accepting significantly more applications when compared to FICOS’s scoring methods through a superior ability to price risk. Alternatively, for any given consumer sub-segment, our model produced lower risk outcomes than FICO scoring.
Source: Affirm Investor Presentation (March 2022)

Other use cases. A number of other areas exist where banks can make use of AI and machine learning. In retail banking, advertising optimisation, customer segmentation, fraud detection, advanced risk analytics and data-driven decision-making, to name but a few, naturally come to mind as the transformations are already happening. Considering the wider picture, machine learning can also help asset pricing, market making, recruitment, procurement and more. In total, as long as banks have quality data to work with, use cases for ML models can be very broad.

Banks are increasingly devoting more resources to developing analytics toolkits and improving their AI and machine learning capabilities. These help boost revenue, lower costs and improve overall efficiency. In addition to big data and AI & machine learning, other sub-trends in powering new technologies, such as shifting to the cloud and the expanding use of APIs to unleash digital potential, are unfolding.

Trend 2: Customer experience 2.0

In an increasingly challenging, dynamic and uncertain business environment for retail banks, where regulations are continually changing, interest rates are consistently low, competition is fierce and consumer behaviours and expectations are shifting, bespoke customer services to deepen customer relationships have become essential.

Moving from a product-centric model to a customer-centric model. A shift to a more customer-centric strategy is critical for retail banks to thrive. One key reason for adopting a customer-centric approach is the growing number of millennial and Generation Z customers, who, compared with older generations, have different expectations. For example, they want to have total control over banking information, a seamless and digital banking experience, and more diverse and innovative financial and banking product offers. Because of growing and shifting expectations, retail banks have been forced to pivot strategically, such as through the creation of new banking channels, product innovation and even the implementation of new organisation charts (e.g. creating new positions such as head of digital banking and head of customer lifecycle management).

Another key aspect here is to organise data around customers instead of products. In the past, banks usually stored data according to product types (i.e. credit cards, loans, etc.); however, with the help of advanced data analytics, banks now focus more on building a holistic view of their customers by leveraging both traditional and alternative data.

In delivering a superior customer-centric approach, banks should develop and monitor their client-centric key performance indicators (“KPIs”), which would help streamline customer experience metrics and accurately identify areas for improvement. For instance, client-centric KPIs about retail banking products can cover monthly active users per product, app use duration per product, time taken to obtain funding, etc. These metrics focus more on the customer’s use and engagement experience, instead of the traditional product-oriented focus (e.g. number of loans per year).

Another important aspect of a customer-centric approach is advisory service. As financial services become digital, it is vital for banks to maintain the human element and a constant connection with their customers. Banks need to ensure quick or even real-time interactions, which could be enabled by a combination of robotic advisers and human advisers. Furthermore, a new trend is coming to light in which clients demand more individualised budgeting, spending, and even investing advice, which can be seen as a critical field for banks to differentiate themselves. For example, Singapore’s OCBC bank has launched its robotic investment service, RoboInvest, to offer its customers a simple and digital investment advisory experience.

Enhanced super-app experience. With rapid transformations already under way, banks need to move beyond the surface of digitalisation. A growing trend is to enrich the service offerings provided in mobile apps. The ultimate goal is to develop a one-stop shop, like a “super app” platform, to cater to customers’ holistic financial needs and other needs in their daily lives. One example is the WeChat app from China. It has grown from a simple messaging app to a super app with a plethora of services ranging from digital wallet services, money transfers, payments, finance and investing, taxi and transportation services, hotel and tour booking services, ecommerce, social life and even gaming.

Whilst it is not necessary for all banks to create a super app, it is clear that the concept of moving money from one’s bank account to other places seamlessly is gaining traction. As a result, core features such as banking, financing and investment and payments should be integrated into the retail banking experience. For example, customers may wish to invest in funds, stocks or even crypto/ NFTs within their banking apps. They also need easy access to loans within the apps. Payment services, which traditional retail banks paid less attention to, are also essential for a seamless digital experience. Several disruptions in the payment field can be observed. For example, with the need for contactless payments amid the pandemic, the industry has seen increasing adoption of digital payment methods (i.e. Apple Pay and Google Pay). The ecommerce trend also serves as a strong tailwind for digital payment. In addition, in terms of credit cards, there is a growing trend of buy now pay later (BNPL) solutions. Overall, with banking, financing and investment and payment experiences going digital, the industry has seen a convergence in these services in one super app. One well-known example of a super finance app is the Cash App ecosystem developed by Block, Inc. (formerly known as Square). Starting from supporting seller activities, the FinTech giant has expanded to a consumer ecosystem and launched the cash app. With an impeccable digital experience provided within one single app, Cash App has become one of the leaders in consumer financial apps in the industry.

Figure 3: Square’s seller and cash app ecosystem

Source: Square’s Investor Presentation (September 2020)

There are multiple benefits to offering an enhanced digital experience and even a super app. First, a good digital experience can increase customer loyalty and reduce churn rate. Second, it acts as a marketing tool to attract new customers and create a network effect among friends and colleagues. Third, the more a customer’s financial journey occurs within the app, the more data is collected to generate valuable business insights. Fourth, banks can cross-sell new products or expand to new markets, further monetising the customer base. Finally, the partnership with companies in the broader ecosystem (e.g. loyalty reward schemes with partners) can also unlock enormous business potential.

Trend 3: Automation of end-to-end services and the use of blockchain technology

The third major trend in the retail banking industry is automation. The ultimate goal for automation is to reduce decision time, save costs, improve productivity and efficiency for banks and provide a faster banking experience for customers. In the following figure, we can see the most common areas with potential for automation and their respective cost/volume considerations at the banks’ risk and finance & operations functions.

Figure 4: Key processes for automation in the retail banking industry

Source: Accuracy

Banks should ask themselves which procedures, whether customer-facing or internal, can be automated across the customer lifecycle. Many banks are currently aiming to automate as many manual procedures as possible, such as loan application approval, funding, fraud detection and document verification. The advantages of automation can be numerous. It allows for significant cost saving, increased volumes, faster decision-making, improved customer services and reduced risk of human error. However, one factor worth bearing in mind is that, despite the automation, the decision engines of retail banks should retain some degree of flexibility in order to navigate the dynamic environment successfully and deal with unexpected bespoke demand.

Blockchain or distributed ledger technology (DLT) is another trend that is revolutionising the banking industry by streamlining end-to-end business workflows. This technology can provide automation, trust and security for banks and their customers. The most common use cases include trade finance, cross-border transactions, insurance, payment and settlement, and asset tokenisation, among others. For example, Contour is a decentralised platform developed by a consortium of eight banks (Bangkok Bank, BNP Paribas, Citi, CTBC Holding, HSBC, ING, SEB and Standard Chartered) and three delivery partners (Bain Consulting, CryptoBLK and R3). It is a global digital trade finance network that uses blockchain technology to provide end-to-end trade-related services connecting businesses, lenders and partners seamlessly and securely in real time. Although blockchain technology is currently used mostly in corporate banking, it is expected that the use case will gradually spread to retail banking.

3. Retail banks in the future

The majority of banking activities now take place online; more and more people are familiar with internet banking and mobile banking. However, in the post-pandemic era, the reality is that having a mobile app that simply moves the traditional banking experience to a digital format or having some degree of automation is not enough. In the future, banks must rethink their entire business model to take their customers’ experience to the next level. With all of the transformations taking place, retail banking will never be the same. Traditional retail banks and virtual banks must identify and capitalise on their respective strengths while attempting to mitigate their weaknesses.

Future of traditional retail banks

We believe that banks should view innovation and digital transformation as a growth engine instead of simply reacting to the disruption brought by FinTechs and TechFins. Traditional players have advantages in terms of bigger customer bases and the amount of resources they can invest in technology infrastructure. They also have a broader range of products and solutions for their customers, and stronger brand names to gain customers’ trust. When compared with younger and less experienced digital banks, these make a significant difference.

Figure 5: Key benefits of traditional banks

Source: Accuracy

Traditional banks have their weaknesses too. The main concern is the customer experience. In today’s fast-paced environment, customers often find traditional banks to be slow. There always appears to be a long queue at the branch. Applications are always time-consuming and the paperwork seems endless. Despite some degree of digitisation, when it comes to speed, traditional banks lag behind their digital counterparts. When compared with services offered by technological players, the gaps are even wider.

Another concern is resistance to change. Just as traditional carmakers are hesitant to commit fully to electric vehicles for fear of cannibalising the pipeline of their current vehicles, traditional banks are reluctant to move their distribution channels online, which may compromise their current success.

High running costs are another disadvantage. Traditional banks incur large costs associated with physical premises (such as branches and ATMs), which represent a significant burden when clients no longer visit these facilities. The army of staff becomes costly as well. In addition, the lack of agility of legacy systems also hampers business transformation. Often, there is limited room for manoeuvre to reduce staff or implement massive cost reduction programmes. Regulatory and political surveillance also limits the options for such actions.

In the future, traditional players need to deploy strategies that maximise their strengths and minimise their weaknesses; speed to adopt transformations will become key.

Greater concentration and restructuring is also likely in the future of retail banking. Concentration aims to sell all businesses that are far from the banks’ main markets in order to be as large as possible domestically (e.g. sale of Bank of the West by BNPP, sale of French retail banking by HSBC). Restructuring, particularly in Spain but also in France (e.g. the merger between SG and CDN), aims to lower a bank’s breakeven point. Further, a trend whereby traditional retail banks acquire or partner with emerging FinTech players has come to light, in order to build specialised businesses rapidly. For example, J.P. Morgan acquired OpenInvest, a leading FinTech start-up that assists professionals in providing tailored value-based investment solutions, benefitting the bank’s Private Bank and Wealth Management advisory service offerings. Another is the leading AI lending FinTech Upstart, which partners with several US regional banks to help them grow their customer lending portfolios with seamless digital experiences.

Meanwhile, traditional players will have to rethink their distribution models in light of digitisation and convergence of services. Such convergence leads to embedded finance, which integrates financial products directly into the customer’s purchase journey.

Future of digital banks

Digital banks, or virtual banks, are FinTech companies that provide mobile and online banking solutions via apps, software and other technologies. They are digitally based, with no physical facilities. They typically specialise in certain financial products and services, catering to specific customer segments. For a digital bank, there are several key steps to consider for continual business success.

Figure 6: Key steps in a digital bank’s business development

Source: Accuracy

Digital banks have several advantages over traditional players. The first stems from their technological foundation. They are digital natives who build the required IT infrastructure from the ground up and are able to provide the right level of customer experience with more flexibility (e.g. 24/7 services). In addition, smaller digital banks typically specialise in certain segments and can provide a more tailor-made customer experience, which is difficult for large generalist banks. They can also add some features such as budgeting and personal financial planning to enhance the customer experience. In terms of operating costs, their smaller size allows them to be more agile than their traditional counterparts, both in terms of labour costs and fixed costs, as they have no physical branches. The nature of being virtual also makes this new type of bank more eco-friendly.

Figure 7: Key advantages of digital banks

Source: Accuracy

Their disadvantages frequently stand in direct opposition to their strengths. The limited service offering allows digital banks to focus on and better meet specific needs; however, in a world where technology allows for the emergence of multi-service platforms and customers expect increasingly simple answers and one-stop shops, addressing only part of the financial services needs is not necessarily the right direction in the long term. One of the major consequences of this limited supply is the inability to capture the most profitable customers of retail banking: the premier banking accounts. This explains their low income, which peaks at EUR 20 per customer against EUR 300 to 400 for traditional banks in Europe.

But this does not necessarily rule out a bright future for digital banks. As they shift their focus from growth to profitability, they need to act quickly in terms of rolling out new products and establishing partnerships.

One thing of note is that the digital banking landscapes in developed and developing countries differ significantly. The road is often wide open for them in underbanked countries. For example, Nubank, a Brazilian digital bank, has around 54 million customers in Brazil, Colombia and Mexico and has a market capitalisation of USD 37 billion.

The situation is different in ultra-banked countries like France. Digital banks there are frequently forced to make difficult choices due to low profitability and a trend towards convergence in financial needs. They can quickly broaden their range of products to meet more customers’ needs or partner with merchants to offer unique products. They can also explore selling their business to traditional players or even technology giants who are eager to tap into the field.

Regulatory support

Increasing regulatory support for both traditional and digital banks is a crucial driver of banking transformation. For example, the United Kingdom has launched the Open Banking standard in order to improve customers’ banking experiences and to encourage competition and innovation in the industry. The standard covers five core components: API specifications, security profiles, customer experience guidelines, operational guidelines and a reference library. Since its inception in 2017, over 230 third-party service providers and more than 90 payment service providers have joined the Open Banking ecosystem.

The concept of a FinTech sandbox is also critical in the industry to facilitate FinTech innovations. It allows industry players to experiment with creative ideas, collect real-life data and perform pilot testing in a safe environment. This also helps speed up product launches (without full regulatory compliance), reduce development costs and refine the consequence of failures. The Hong Kong Monetary Authority (HKMA) introduced the Fintech Supervisory Sandbox (FSS) in 2016, which was upgraded to FSS 3.0 in November 2021. The Monetary Authority of Singapore (MAS) has also established the FinTech Regulatory Sandbox framework since 2016.

Final remarks

The battle will only become more fierce in the next 10 years. Apart from traditional players and digital players, there are also formidable contenders joining the game: technology companies (e.g. Apple launched Apply Pay and Apple Card), retailers (e.g. Walmart acquired several FinTech start-ups to develop an all-in-one app for consumers to manage their money) and even blockchain technology (e.g. Bitcoin surpassed PayPal in transaction volume). Looking ahead, we believe that in a world filled with both opportunities and challenges, both traditional retail banks and digital banks will need to accelerate their transformation. They will have to constantly reshape themselves and rethink the business models to suit the needs of their customers, staff and society as a whole.

WHAT IS CLIMATE RISK AND WHY DOES IT MATTER FOR FINANCIAL INSTITUTIONS?

Climate risk has been a hot topic – if not the hottest – all over the globe in the past few years. It refers to the possible negative impacts that climate change and the transition to a low-carbon economy may have on the economy and society at large. On one hand, physical risk such as more varied temperatures, more frequent and intense floods, wildfires, droughts, storms and other extreme weather events, rising sea levels, loss of biodiversity and more are bringing potential adverse effects on lives, health, infrastructure and financial and economic assets. On the other hand, even if society wants to move to a low-carbon economy, some industries may see significant changes in asset values or operating costs. The speed with which the shift takes place is also a concern, since the transition could be costly for some companies.

Following the rise in global awareness of climate issues, various international conferences have taken place and several international commitments have been made to address climate change. The financial services industry is certainly at the forefront of this transition due to its systemic importance. Globally, central banks and regulators are demonstrating their awareness and commitment to tackling climate change by issuing various guidelines, protocols and frameworks, and financial institutions are taking actions to adhere to the new rules.

Managing climate risk is a relatively new field and could prove to be complex and challenging. In this article, we will discuss how financial institutions can tackle climate risk effectively.

TYPES OF CLIMATE RISK

Climate risk is a very broad concept, and the associated risks were not well defined until the Bank of England established three categories of climate change risk in 2015. Though the definition and understanding of climate risks may vary across jurisdictions, the three types of climate risk below are the most commonly used.

Table 1 – Three types of climate risk

CLIMATE REGULATION FOR FINANCIAL INSTITUTIONS WORLDWIDE

Central banks and regulators worldwide are establishing climate-related bank regulations or guidelines. The new rules often include stress tests, mandatory risk disclosures, supervision of the risk management of financial institutions and potentially the introduction of additional capital requirements for banks. The regulations are expected to change over time and banks will be engaged continuously to understand and mitigate the risks posed by climate change to the sector.

In 2019, the Bank of England became the first central bank to issue climate risk supervisory expectations. Since then, regulators in most developed regions have followed suit. Some of the major climate regulation milestones and stress testing are indicated in the timeline below.

Figure 1: Major climate-related regulatory and stress testing milestones

Source: Accuracy analysis

CLIMATE RISK MANAGEMENT PILLARS

Climate risk management refers to the approach of making climate-sensitive decisions. The approach seeks to promote sustainable development by reducing vulnerabilities to climate risk. For financial institutions, the guidance comprises four pillars in general.

Figure 2: Four pillars of climate risk management

Source: Accuracy analysis

AN ILLUSTRATIVE FULL-SCALE IMPLEMENTATION FRAMEWORK

With all this in mind, we recommend that an implementation programme for climate risk management should cover the elements as we illustrate in Figure 3 below. This can be broken down into three phases of work: (1) planning and portfolio review, (2) solution implementation and (3) policies, procedures and risk culture.

Figure 3: Full-scale implementation framework 

Source: Accuracy

In the following sections, we will introduce certain critical pillars in the implementation of climate risk management and demonstrate the relevant tools that will make implementation smooth and cost-effective.

MANAGEMENT QUALITY ASSESSMENT

Today, companies often have “green” policies in place to help tackle climate change. Sometimes, part of the management team’s remuneration package is determined by the success of the green initiatives. However, corporate executives often have doubts over whether their companies are doing enough. They are also often unsure of the next steps. Therefore, one of the key elements in this phase is to assess management awareness and quality.

The Transition Pathway Initiative (TPI) established a widely accepted framework called the Management Quality Framework. The framework tracks the progress of companies in tackling climate change through the following five levels:

• Level 0 – Unaware of climate change as a business issue
• Level 1 – Acknowledge climate change as a business issue
• Level 2 – Building basic capacity (management system, processes and reporting)
• Level 3 – Integration into operational decision-making
• Level 4 – Strategic assessment

Banks should assess how their corporate clients are addressing climate risks from governance and strategy perspectives. As shown in Figure 4, a well-designed questionnaire can be used to capture such information from clients.

Figure 4: Template for TPI management quality framework – questionnaire and indicators

Source: Accuracy

GHG PROTOCOL AND PCAF STANDARD

For Phase 2, we start to perform quantitative and qualitative examination. Similar to IFRS or GAAP in the financial accounting world, there are generally accepted standards for measuring greenhouse gas emissions; the Greenhouse Gas (GHG) Protocol is leading provider. Developed under the partnership between the World Resources Institute (WRI) and the World Business Council for Sustainable Development (WBCSD), the GHG Protocol establishes a standardised framework to measure and monitor GHG emissions, aiming to enhance measurement and monitor reliability, accuracy and comparability across companies, industries and countries.

The protocol accounts for all six greenhouse gases identified in the Kyoto Protocol, including CO₂, CH₄, N₂O, HFCs, PHCs and SF₆. These emissions can be classified into three scopes.

Scope 1. All direct emissions from owned or controlled sources (combusted on-site). Common types of Scope 1 activities include stationary combustion (fuel consumption at a facility), mobile combustion (e.g. vehicles) and refrigerants (e.g. from air conditioning).

Scope 2. Indirect emissions from purchased energy from utilities (combusted off-site). Specifically, Scope 2 activities include both purchased electricity (calculation approach can be either market-based or location-based) and purchased heat and steam.

Scope 3. Indirect emissions occurring in the supply chain. These activities can be grouped into eight upstream activities (purchased goods and services, capital goods, fuel and energy-related activities, transportation and distribution, waste generated in operations, business travel, employee commuting and leased assets) and seven downstream activities (transportation and distribution, processing of sold products, use of sold products, end-of-life treatment of sold products, leased assets, franchises and investment).

In terms of calculation, the general approach is

The activity refers to the level of emission activity (e.g. tonnes of fuel consumed) and the emission factor is a factor to convert activity data into emission data (e.g. kg of CO₂e / tonnes of fuel burnt). It is worth noting that although there are six types of GHG, all emissions are converted into CO₂e for better comparability. To facilitate the calculation, the GHG Protocol has developed corresponding Excel tools that can be customised for implementation.

Like other corporates, financial institutions generate Scope 1, 2 and 3 emissions in their daily operations. However, they should place further attention on their Scope 3 emissions, notably in relation to investment activities (i.e. GHG emissions financed by their loans and investments).

The Partnership for Carbon Accounting Financials (PCAF), an open collaboration of banks, has established a global GHG accounting standard. The standard aims to reduce inconsistencies in carbon accounting methods, allocate the emissions of companies to financial institutions fairly based on their share of the financing and help the financial sector facilitate a transition to decarbonisation. It provides a framework for measuring and disclosing emissions from six major asset classes, including listed equity and corporate bonds, business loans and unlisted equity, project finance, commercial real estate, mortgages and motor vehicle loans. The asset classes defined by PCAF are based on financing types and sources (i.e. corporate finance, project finance and consumer finance), use of proceeds (i.e. known or unknown as defined by the GHG Protocol) and activity sector (e.g. all sectors, real estate, motor vehicle).

In terms of calculation, the general approach is set out below.

Banks should develop templates based on the PCAF standard in order to calculate their Scope 3 emissions.

Figure 5: Accuracy template for Scope 3 emissions (investment activities)

Source: Accuracy

NET ZERO AND TARGET SETTING

With an objective to achieve net-zero emissions by 2050 and limit global warming to 1.5°C, the Science Based Targets initiative (SBTi), a partnership between CDP, the United Nations Global Compact, World Resources Institute (WRI) and the World Wide Fund for Nature (WWF), has established tools for organisations to set science-based targets for emission reduction. There are three key technical pieces in the target-setting processes – carbon budget, emissions and allocation approach.

Figure 6: Three key pieces in the target setting processes

Source: Accuracy

Two of the common target-setting approaches are the absolute contraction approach and the sectoral decarbonisation approach.

Absolute contraction approach: this approach applies to all sectors excluding power generation and oil & gas. It assumes a linear annual reduction rate based on IPCC carbon budget scenarios (i.e. 4.2% for the 1.5°C goal and 2.5% for the well-below 2°C goal). Using these guidelines, companies should aim for a decarbonisation rate that takes into account the number of years since the base year emissions. For example, if a company sets its base year as 2020 and uses the 1.5°C scenario, the GHG emission target should be a 42% emission reduction from the base year level by 2030 (4.2% multiplied by 10 years).

Sectoral Decarbonization approach (SDA): this approach is sector-specific; however, not all sectors have relevant SDA tools available. It is based on the idea of “intensity convergence”, which assumes that the carbon intensity of an individual company converges with the homogeneous sector’s carbon intensity by 2050. The target percentage of reduction differs based on sectoral IEA carbon budgets.

As financial institutions play a key role in climate risk management, there are specific target-setting approaches for them. It should be noted that financial institutions’ science-based targets should cover Scope 1, 2, and 3 emissions.

For Scope 3 category 15 (investment activities), based on the temperature scoring method developed by the CDP and WWF, SBTi has established two approaches: SBT portfolio coverage and SBT temperature scoring. These approaches enable financial institutions to align their investment and lending portfolios with the emission targets. The applicable target-setting methods depend on the type of asset class.

Figure 7: Applicable target-setting methods for selected asset classes

Source: Accuracy analysis

When using the portfolio coverage method, financial institutions commit to engaging with their investees to set their own approved science-based targets (SBT). On a portfolio basis, the financial institution should aim to achieve 100% SBT portfolio coverage by 2040 linearly. The 2040 timeline is set to allow companies enough time to implement their targets to achieve net zero by 2050.

When using the temperature scoring method, financial institutions determine the temperature score of their investment portfolio based on the available GHG emission reduction targets of their investees. As companies may have multiple climate targets, transformation is performed to convert targets into temperature scores for both Scopes 1+2 and Scope 3 over three time frames – short (targets shorter than 5 years), medium (5–15 years) and long (over 15 years) terms. Specifically, each target is mapped to a regression model based on target type, the company’s sector (ISIC), the intensity metric and the scope. The portfolio level scores are generated based on various weighting options, such as weighted average temperature score (WATS), total emissions weighted temperature score (TETS) and total assets emissions weighted temperature score (AOTS). Then, financial institutions analyse ways to improve a portfolio’s temperature score, such as analysing the hotspots or performing what-if analyses. Ultimately, financial institutions should determine relevant and practical actions to achieve the long-term targets.

Figure 8: Temperature scoring method – key steps

Source: Accuracy analysis

To facilitate target setting, SBTi has developed Excel tools and an open-source Python library for target setting under various methods. For example, Figure 9 shows a target-setting tool specific to commercial real estate and residential mortgages based on the SDA. Figure 10 presents part of the open-source coding templates of the temperature scoring approach.

Figure 9: SDA tool for commercial real estate and residential mortgages

Source: SBTi, Accuracy analysis

Figure 10: Excerpt from open-source coding templates of temperature scoring approach 

Source: SBTi, Accuracy analysis

CLIMATE RISK STRESS TESTING

Another essential quantitative assessment for financial institutions regarding climate risk is stress testing. A stress test corresponds to a “what if” analysis, where scenarios that would cause shocks to banks are used as inputs. A general operation of the climate risk stress test framework is illustrated in the below figure.

Figure 11: Climate risk stress test framework

Source: Accuracy

Adopting the tools and data provided by several worldwide organisations related to climate risk, such as the Network for Greening the Financial System (NGFS), we have developed several dedicated stress test models that are fit for regulatory purposes. Overall, stress testing can be either a top–down analysis or a bottom–up analysis. We will discuss more on the bottom–up analysis in the next section.

The key elements of a top–down approach are set out below:

1. Identify scenarios for stress testing based on the NGFS scenarios

2. Forecast key climate and macroeconomic indicators under each scenario

3. Develop the relationship between projected indicators and company profitability and leverage and estimate the impact of chronic physical risk

4. Translate the relationship to changes in the probability of default (PD).

Identify scenarios for stress testing based on the NGFS scenarios: Each NGFS scenario looks at a distinct set of assumptions about how climate policy, emissions and temperatures will change over time. For an orderly transition, the two common scenarios are (1) net zero 2050 (limits global warming to 1.5°C and reaches global net zero CO₂ emissions by 2050 through stringent climate policies and innovation) and (2) below 2°C (limits global warming to below 2°C through gradual increases in stringency of climate policies). For a disorderly transition, the two common scenarios are (1) divergent net zero (reaches net zero around 2050 but with higher costs due to divergent policies introduced across sectors) and (2) delayed transition (assumes that annual emissions do not decrease until 2030 followed by strong climate policies to limit warming to below 2°C). For a hothouse world, the two common scenarios are (1) nationally determined contributions (NDC, including all pledged policies even if not yet implemented) and (2) current policies (assumes that only currently implemented policies are preserved, leading to high physical risks).

Figure 12: Major NGFS scenarios

Source: NGFS, Accuracy analysis

Forecast key climate and macroeconomic indicators under each scenario: Integrated Assessment Models (IAMs) assist in the generation of key climate and macroeconomic variables based on several NGFS scenarios. The outputs are transition trajectories through time and across different regions/countries, based on various scenarios. Projected carbon prices, GHG emission levels, secondary energy prices, temperature rises and other factors are all taken into account. Aside from transition risk, the outputs of IAMs are also used as inputs into other macro-econometric models (such as the PIK model) to predict the degree of physical chronic risk.

Develop the relationship between projected indicators and company profitability and leverage and estimate the impact of chronic physical risk: With the projected climate and macroeconomic indicators, we can explore how these macro indicators affect company profitability and leverage ratios.

Let us take the imposition of carbon pricing policies as an example. In most cases, this policy will result in greater operational expenses on a company’s income statement. The linkage is due to (1) extra costs of carbon prices paid on direct emissions and (2) higher indirect expenses due to higher energy (i.e. utility) invoices. As a result, the profit margin is squeezed and the overall profitability of the company suffers. This will increase the likelihood of default.

Another linkage relates to capital expenditure. Companies may need to invest in new machinery and production technologies in order to meet the carbon emission targets. They may therefore need to issue additional debt to fund this climate-related capital expenditure. As a result, the company’s debt-to-equity ratio will rise, raising the risk of default.

The likelihood of a corporate default is also influenced by chronic physical risks. Temperature rises, for example, would reduce economic productivity. As a result, expected yearly GDP growth rates could become slower or possibly reverse. This deterioration in the macroeconomic environment assumption also increases the risk of default.

In the following example, we illustrate the analytics framework that was adopted by the Hong Kong Monetary Authority (HKMA) in a research paper¹ published in March 2022. This framework is consistent with what we have described above.

Figure 13: Illustrative relationship between climate-change policies and probability of default

Source: NGFS, Accuracy analysis

Translate the relationship to changes in the probability of default (PD): Multiple approaches can be employed to estimate the extent of changes in the probability of default. In the HKMA example, the regulator has adopted a set of regression formulae to estimate the impacts on PD.

In the following equations, the subscript or superscript 𝑖 denotes the firm, 𝑡 denotes the time (year) and 𝑠 denotes the scenario.

• Profitability – defined as earnings over total assets:
• Earnings – revenues minus operating expenses:
• Revenue growth rate – captures how differences in the growth rate of total assets from climate policies may affect firms:
• Operating expenses growth rate – captures what proportion of the increase in revenues is translated to operating expenses:
• Total assets growth rate – captures how differences in GDP growth rates from climate policies affect the growth rates of total assets for firms:
• Leverage – defined as total debt of firms over total assets:
• Output gap – defined as the difference between the actual output of an economy and its potential output divided by its potential output:
• Probability of default:

This framework is logically sound and pragmatic. In practice, we recommend that banks consider this as a starting point, but customisation using the bank data will be necessary. For example, the framework is entirely top–down in nature and does not incorporate a bottom–up analysis; we therefore need to perform single counterparty analysis to estimate PDs for major exposures before generalising using regression.

SINGLE COUNTERPARTY ANALYSIS

For single counterparty analysis on PDs, it is typical to adopt the Merton model. The model is structural in nature and is designed to assess the probability of default for single-name counterparties whose balance sheet items are available and can be projected.

The Merton model takes into account balance sheet components (e.g. equity, short-term debt and long-term debt), asset return volatility and the risk-free rate for calculations. This analysis is calculated bottom–up.

The steps for conducting single counterparty analysis using the Merton model are as follows:

1. Identify top counterparties by exposure (e.g. the top 30 counterparties that cover 70% of the portfolio’s total exposure).

2. Analyse the counterparties’ balance sheet items (e.g. sales volume, unit price of products, capital expenditure, impairments) based on different climate change scenarios. Such analyses should take into account both projected macroeconomic and sectoral indicators from the top–down analysis and analysis of idiosyncratic risk (e.g. the adaptability of the counterparty to climate change based on TPI management quality review).

3. Estimate the inputs for the Merton model (i.e. equity amount, debt level, asset volatility and the risk-free rate). In particular, asset volatility and the risk-free rate can be estimated or obtained from external data providers.

4. Perform single counterparty calculation on PD:

Where N^-1 is the standard normal inverse function

Figure 14: Merton Model

Source: Accuracy analysis

TCFD REPORTING

Another important aspect in climate risk management is governance, especially the need for climate-related disclosure. The Task Force on Climate-related Financial Disclosures (TCFD) published a set of recommendations in 2017 and further enhanced them in 2021 to help businesses disclose risks and opportunities arising from climate change.

TCFD has established 11 recommendations surrounding four thematic areas (i.e. governance, strategy, risk management and metrics and targets). The key requirements are as follows:

With an ever greater number of large financial institutions starting to publish their TCFD reports over the past few years, disclosures probably represent the least challenging part of climate risk management, as many good references and templates are already available.

¹ The paper can be found here: https://www.hkma.gov.hk/media/eng/publication-and-research/research/research-memorandums/2022/RM01-2022.pdf

1. Digital transformation in retail banking

Over the past few years, the banking industry has witnessed a new wave of digital transformation. Virtual banking, for example, has become more popular in many regions. Other digitalisation trends such as open banking, RegTech, AI and data-driven decision-making, to name a few, are in the headlines.

In addition, the Covid-19 pandemic is changing the way that banks and customers interact. Today, retail banking products are very much commoditised, with interest rates and other features of bank offers proving similar between them. FinTechs and TechFins have therefore emerged to bring new and better customer experiences to their incumbent counterparts. Customer expectations have also been changing, and customers are seeking digital, seamless, fast and integrated services more and more. Thus, on the other side of the table, banks are not left with much choice but to undertake necessary digital transformations to meet expectations.

Many retail banking transformations are taking place in the market. In the broad sense, we can categorise them into three types: (1) moving from product-centric to customer-centric (i.e. to have more and faster customer interactions, to offer more personalised services and advice, etc.), (2) automating end-to-end services (i.e. adoption of technology for on-boarding, e-KYC, risk management, internal controls, etc.), and (3) enabling big data analytics and data-driven business decision-making.

In this article, we focus on the second and the third categories. In particular, we dedicate this article to the discussion of credit scorecards, as one of the major tools for data-driven risk management and business decision-making. We will review traditional scorecard development methodologies and then discuss the latest trends.

2. Key makes it possible to unlock value

Retail banks typically use credit scorecards, which are mathematical models, to predict the behaviours of their customers. The most important behaviour to predict is whether the customers will default on or repay their borrowings. When it comes to such predictions, two types of scorecard are widely used: the application scorecard and the behavioural scorecard.

Table 1 – Comparison between application and behavioural scorecards

3. Traditional scorecard development framework

There are a number of tools that can be used for the development of retail credit scorecards. Historically, SAS was arguably the dominant programming language for retail credit risk management, including the development of credit scorecards. Over the past decade, open source programming languages, such as Python and R, have become more and more popular. While most banks are still using SAS now, many have started using open source languages in parallel.

Traditionally, a six-phase framework is adopted for credit scorecard development. As demonstrated in figure 1 below, the six phases are (1) data processing, (2) variable transformation and selection, (3) logistic regression, (4) performance inference, (5) scorecard scaling and (6) scorecard validation. Refer to appendix 1 for a more detailed discussion regarding the development procedures.

Figure 1 – Six-phase credit scorecard development processes

Source: Accuracy

4. Challenges in traditional credit scoring

Traditional credit scorecards have been used by market practitioners for a few decades. However, they are not perfect when considered through the lens of big data. Below we highlight the major challenges faced by traditional credit scorecards today.

Figure 2 – Key challenges faced by traditional credit scoring

5. Key enablers to unlock value

We have already briefly mentioned the trends to tackle the challenges encountered by traditional credit scoring. These are certainly at the heart of retail credit decisioning in the era of big data analytics.

Figure 3 – Key trends in retail credit scoring

Source: Accuracy

Trend one: Big data analytics and the use of alternative data

Looking at retail banking globally, we are seeing a strong focus on improving data and deeply understanding customer needs to create personalised experiences. Big data analytics and the use of alternative data have become one of the most prevalent trends in the industry’s transformation. With rising computing power and increasing access to advanced analytics tools, market practitioners are starting to realise the hidden value of data as well as to search for new data sources.

Retail banking has long been a data-driven business, where data is generated at every stage of the customer journey. However, historically, most banks did not have an efficient way to realise the potential of the data nor the IT infrastructure necessary to do so. Furthermore, traditional data as used in the past is just the tip of the iceberg; huge amounts of alternative data, in either structured or unstructured forms, are generated every second from various data sources, both internally and externally, in this digital era.

Over the past decade, thanks to advances in big data analytics, retail banks now have increasing capabilities to process traditional and alternative data efficiently; thus, they are able to build up the customer’s 360-degree profile digitally. With that in mind, banks are starting to provide a more tailor-made customer experience via their banking apps and digital platforms. In addition, upselling and cross-selling campaigns can now target specific customer segments based on insights from big data analytics. Developments in AI and machine learning also help banks and data providers to gain insights from unstructured data (e.g. using nature language processing (NLP) to gauge a customer’s sentiments).

Figure 4 – Traditional data and alternative data comparison

Source: Accuracy

The use of alternative data not only improves the robustness of the scorecard model but also enables banks to assess the creditworthiness of untapped customer segments. This helps to extend financial services to the two billion unbanked adults globally.

With alternative data, retail banks are also able to develop more scorecards, such as income scores, propensity scores and marketing scores. These further help banks decide to whom to lend their money, how much to lend, in what time frame and through what channels.

Some FinTech firms and digital financial service providers have taken the initiative to make use of alternative data sources for credit scoring. Credit bureaus, such as Experian, can now add rent payment history to their credit scoring algorithms thanks to a tool developed by the UK PropTech firm CreditLadder. Lenddo,a software business in Singapore, has incorporated social media and mobile phone data to assess clients’ credit levels. By aggregating data from SMS footprints, electronic devices, emails and credit bureau reports, among others, Algo360, an alternative credit score solution provider, helps new-to-credit customers get loans. Small FinTech companies have used smartphone activity, including calls, GPS data and contact information, to execute credit scoring in microfinance. As alternative data accumulates, the output from predictive models is likely to become more reliable and accurate over time.

Alternative data is not only beneficial when credit scoring individuals, but also in the case of SMEs. Banks commonly consider SMEs to be high-risk clients since information about them is limited, causing difficulty in evaluating their creditworthiness. Because of the intrinsic qualities mentioned previously, alternative data, in conjunction with traditional data sources, will help to build a more comprehensive profile of SMEs, allowing lenders to make better decisions. Digital SME lenders (e.g. Kabbage, an Atlanta-based FinTech company) are making wide use of alternative data such as bank account money flows and balances, business accounting, social media, real-time sales, payments, trading, logistics, and credit reporting service provider data, as well as various other private and public sources of data, to improve risk assessment and to tap into a large market of underserved SMEs.

Moreover, the value of data can be further ‘mined’ if combined with AI and machine learning techniques, which brings us to the second major trend in the digital transformation of retail banking: AI and machine learning in modelling.

Trend two: AI and machine learning in modelling

Retail banking is one of the industries where the use of artificial intelligence (AI) and machine learning (ML) has become widespread. We have talked about the tremendous potential of data, and we believe that these new techniques are well placed to assist in unleashing this potential, particularly when it comes to credit decisioning.

Machine learning can be applied to strengthen traditional logistic regression credit scoring or a solely ML-based model can be developed for credit scoring. Below we highlight some ML techniques that can be applied by banks when developing their credit scoring systems.

Figure 5 – Common machine learning analytics applied for credit scoring system development

Source: Accuracy

An ML-based model would have several advantages over a logistic regression model. First, it can capture the non-linear nature of risk factors, and thus if trained appropriately, can possess higher predictive power. Second, it is agile and dynamic enough to perform the timely assessment of customer credit quality based on greater amounts of relevant and recent data. Third, the model can be highly automated and self-improving, thereby lowering ongoing operational costs.

Higher predictive power

ML-based models are trained with much more data than their traditional counterparts. These include both traditional data and alternative data as discussed above. While traditional models are not designed to discover complicated relationships between large amounts of data, ML-based models are much stronger in this area. As such, it would not be surprising to see that ML-based models are more predictive than traditional models.

More agile and dynamic

ML-based models are continuously trained with the most up-to-date data, so that they are able to perform real-time assessments of customer creditworthiness. This allows the models to provide rapid feedback to model users for credit approval and other decision-making processes. Due to their agility, ML-based models are also more customer-centric and offer smoother assessments of customer creditworthiness. As a result, greater financial inclusion is possible.

Figure 6 – Risk assessment over time – ML model vs traditional model

Source: Accuracy

Highly automated

ML-based models are designed to be self-improving over time and thus highly automated. Traditional models require users to recalibrate them (e.g. on a yearly basis) and redevelop them (e.g. every few years). ML-based models are able to update themselves based on updated data feeds. As such, operational costs for ML-based models are lower, especially in the long term.

With these benefits, it is no wonder that credit bureaus are aggressively using ML to evaluate large amounts of data and generate improved insights. Equifax, for example, provides its clients with tailor-made services by applying neural networks to an artificial intelligence credit scoring approach. Equifax is not alone in experimenting with ML; Experian boosts its analytics products with ML capabilities to provide richer, more insightful information. Even for ‘credit invisible’ clients with infrequently updated credit files, VantageScore incorporates ML to analyse risks and provide ratings. ML has also proved to be effective in detecting high-risk behaviours and providing more accurate credit scorecards by TransUnion and FICO. A blend of Tree Ensemble Modelling (a machine learning technique employed by FICO) and scorecards significantly improves predictive performance in credit assessment, compared with traditional scorecards.

Figure 7 – Machine learning outperforms scorecards

Source: FICO Blog (March 2022)

In addition to traditional credit bureaus, FinTech companies are also actively exploring possibilities in ML to run their businesses. For example, LendingClub, the world’s largest online platform connecting investors and borrowers, has created its credit-scoring algorithm based on ten years of LendingClub data, AI and ML technologies; Kabbage is developing next-generation ML and analytics stacks for credit risk modelling and portfolio analysis; and LendUp, an American online direct lender, employs ML algorithms to identify the top 15% of borrowers who are most likely to pay back their debts.

Limitations

Notwithstanding the advantages of ML-based models, they possess some limitations to be resolved. First, ML-based models are not as trivial as traditional models, and the modelled results can be challenging to interpret. It can also be more challenging to explain the models to regulators and auditors. Second, the performance of ML-based models is highly dependent on the quality of the data used. When feeding huge amounts of data into the models, ensuring the quality of the data can be challenging.

Trend three: Process automation

The third trend is the increasing automation in almost every part of the business. In order to provide fast interactions and personalised customer experiences, automation in know your customer, credit approval, risk management and reporting has become highly important. For example, OppFi, a leading financial technology platform, effectively automates the credit scoring process by using AI models, real-time data analysis and proprietary scoring algorithms. Zest AI, an AI-empowered credit life cycle management organisation, provides banks with its automated services in data processing and documentation as well as compliance validation, deployment and integration. With the help of process automation, banks and FinTech companies are largely improving customer experience and greatly reducing operating costs by cutting loan application processes to a few minutes. Credit scoring is at the heart of credit approval and risk management, and its automation largely relates to the automation of data processing, modelling and validation.

Data processing

With big data analytics, banks use both internal and external data to a great extent. Data collection and data cleansing are the major tasks to be automated. Data collection involves the collection of data from different sources, whether traditional or alternative, as well as its digitisation and standardisation. Data cleansing involves data validity checking, data backfilling, treatments for outliers and doubtful data, etc.

Modelling

A large part of model development can be automated with proper governance and approval processes. For ML-based models, this is more trivial as the models are designed to improve themselves on an ongoing basis using the latest data. For traditional models, automation can be useful for recalibration and the generation of challenger models.

Validation

The validation of models can be entirely automated, whether for traditional or ML-based models. Model validation consists of calculating predefined performance metrics and comparing them with predefined thresholds. It is relatively straightforward to automate such processes and generate validation reports.

Figure 8 – Process automation with the help of ML and AI

Source: Accuracy

What Accuracy does

For clients who need to navigate the digital transformation in the retail banking industry, especially in credit scoring, Accuracy is well placed to work with you on the following tasks:

• Perform an independent review and validation of your existing credit scorecards

• Develop credit scorecards using programming languages including SAS, Python, R, VBA, etc. The development process is semi-automated for easier repetition and maintenance

• Advise you on the adoption of alternative data for credit scorecard development, whether for traditional or machine learning models

• Develop machine-learning-based credit scorecards using open-source languages such as Python

• Perform automation on data processing, modelling and validation

• Perform the overall strategic shaping of retail banking digital transformation and adoption of big data analytics

At Accuracy, our financial services industry experts work with banks and non-bank financial institutions on mergers and acquisitions, strategic transformations, quantitative modelling and adoption of technology solutions. We have been working closely with global financial institutions as well as small and medium-sized institutions over the past two decades.

Appendix 1 – detailed procedures for retail credit scorecards development

For lawyers involved in the world of litigation and arbitration, claims for damages of all kinds are a common (if not daily) occurrence. However, as the quantification of such damages falls to experts in the fields of accounting, valuation and economics, many legal practitioners can find it difficult to sensecheck points put forward by experts on both sides of the debate.

This article was drafted with exactly this intent in mind: to provide legal practitioners with a straightforward introduction to the main concepts and methods adopted in the assessment of damages.

1. DAMAGES FRAMEWORKS

In common law jurisdictions the theory and principles underlying damages claims are well established. Although the specific application of whether certain types of claims are allowed may differ in civil law jurisdictions such as China, the underlying principles of assessment set out in this article should still apply.

One of the key documents for this discussion is Fuller and Perdue’s classic article, The Reliance Interest in Contract Damages (1937)¹, which elucidates 3 key principles of contract damages, namely:

• The Expectation principle;

• The Reliance principle; and

• The Restitution principle.

The Expectation principle holds that damages following breach of contract should put the claimant in the economic position in which they would have been, if the respondent had fulfilled its promise.

The Reliance principle holds that damages for the breach should make the claimant as well off as it would have been had the promisor never made its promise at all.

The Restitution principle holds that damages for breach require the respondent to return any benefit conferred on them by the claimant as a result of the promise.

The first of the 3 principles (Expectation) is doctrinally dominant in discourse about damages and is regularly cited in expert witness reports regarding damages, hence it will form the focus of this article.
One important elaboration of this principle was clarified in the Chorzow Factory case (1928), wherein the Permanent Court of International Justice (PCIJ) established the reparation standard for intentionally wrongful acts under customary international law as follows:

“The essential principle contained in the actual notion of an illegal act… is that the reparation must, as far as possible, wipe out all the consequences of the illegal act and re-establish the situation which would, in all probability, have existed if that act had not been committed.” ²

Practically for the expert, this typically means assessing the economic position of the claimant under two situations: the actual situation, in which the “illegal act” occurred, and a counterfactual (the “But For”) situation in which it did not. Reparation, or damages, would then equal the difference between the two, thereby re-establishing the economic position in the counterfactual situation.

2. TYPES OF DAMAGE SUFFERED

There are several types of damage which can be suffered by the claimant, depending on the exact illegal act involved:

Additional costs

This type of damage is commonly encountered in construction disputes where a contractor has incurred additional costs beyond its tender price due to action (or inaction) by the owner, or conversely, rectification / remedial costs if the subcontractor’s work has been delayed or substandard.

Lost profits

This is one of the most common types of damage claims encountered in commercial disputes. An example would be where a supplier has failed to supply a key, hard-to-source component of a product leading to a loss of sales and therefore profits.

Loss of use of an asset/investment

One potential example would be where the claimant (a manufacturer) loses the ability to produce and sell its products due to damaging acts by the respondent, such as expropriation of or intentional damage to a manufacturing plant.

Loss of opportunity

This type of damage can often be encountered in claims against professional advisors, where for example incorrect advice or inaction by an advisor leads to the claimant losing out on an opportunity to invest in a redevelopment project.

Reputational harm

An example would be in cases where an action or allegation by the respondent has damaged the image or reputation of the claimant’s business, leading to concrete economic loss.

3. COMMON BASES FOR CLAIMING DAMAGES

Two common types of legal dispute where expert assistance is often required to assess the quantum of damages are (i) commercial contract disputes and (ii) investment-treaty disputes under Bilateral Investment Treaties (BIT).

Commercial contract disputes can arise wherever a contracting party has not fulfilled its part of a contract, whether that be in terms of providing certain products or services, or completing agreed work to a certain standard within an agreed duration, etc. Investment treaty disputes, by definition, arise between a state (or State-Owned Entity) and an investor, which can be an individual or a company, often when assets or enterprises operated by said investor are expropriated by the state. The Chorzow factory case referred to in Section 1 could be considered a typical case as it involved a nitrate factory where the factory in question was expropriated by the Polish government from its German owner(s).

Although from a legal standpoint there are many differences between the two types of claim (not least procedural), from an economic valuation perspective the applicable principles are very similar. Hence examples of both types of case will be covered in the sections below.

4. OVERVIEW OF VALUATION APPROACHES

Except in extremely straightforward cases – possibly where a claimant has been deprived of an asset whose value is standardised – most damages claims will involve some kind of valuation process. These claims can be classified as either direct or indirect losses. Direct losses refer to instances where the claimant has suffered the loss of access to or use of an income-generating enterprise or asset, and therefore would include shareholder disputes, divorce cases and certain types of expropriation claims. Indirect losses, as usually defined in contract law, arise from a special circumstance of the case and are only recoverable if the party knew or should have known of the circumstance of the loss when entering into the contract.

Approaches to valuing losses can broadly be divided into three main categories, namely (i) Income, (ii) Market Multiples, and (iii) Cost-based approaches. In very simplistic terms, the income approach values an asset based on the income it will generate; the market multiples approach values an asset by comparing it to other comparable assets or businesses in the market; and the cost-based approach values an asset based on the current replacement or reproduction cost of an asset, whilst taking into account any deductions required for deterioration or obsolescence of the asset.

Income Approach

The income approach is strongly preferred in valuing losses due to its flexibility and wide applicability. In theory, some form of the income approach can be used for valuing any income-generating asset³. This approach converts the expected future economic benefits from the asset – generally, cash flows – into a single, present value. Because this approach bases value on the ability to generate revenue and profits, it would be well-suited to valuing established, profitable businesses, as well as, say, new mining assets where the income streams are well defined. In comparison, it would be more challenging – but not impossible – to reliably apply this method to an early-phase high-tech start-up company as the range of valuations produced would be extremely wide due to uncertainty as to the size and timing of cash flows.

Types of claim in which income approaches could be adopted include:

I. Breach of contract disputes where a management or distribution contract has been terminated. Common situations where these are seen involve long-term hotel management and pharmaceutical distribution contracts;

II. Unfair competition disputes where, for example, a competitor may have diverted business or orders away from the claimant company;

III. Advance Loss of Profit claims where an incident has led to either delayed start-up or interruption of production, such as at a power plant or cement factory.

But For v. Actual

In our experience of disputes, the historical actual situation is generally a matter of factual evidence and agreed upon between the parties (although they may not agree on the ‘forecast’ actual situation). One of the key tasks of the expert is rather to determine what the counterfactual situation would have been (the But For scenario). The loss is effectively the difference between the Actual and But For situations, as shown in the diagram above.

In all the examples listed above, an expert would need to examine the historical books and records of the company, as well as its internal budgets and business plans, and also take into account any relevant industry research as to future trends, in order to form an opinion as to what a reasonable But For scenario would have been. We discuss establishing a reliable But For scenario further below.

The income approach can also take into account common litigation issues such as offsetting mitigation of losses by the claimant, and/or discounts for minority holdings, etc. Once an income approach is decided upon there are two main methods of arriving at a present value, namely Discounted Cash Flows (“DCF”) and capitalised earnings, with DCF being much more common.

One well-known published example of a case where the income approach was adopted is the matter of Suez v. Argentina⁴, being an investor-state ICSID dispute arising from Argentina’s termination of the concession granted to a consortium of claimants led by Suez S.A. (“Suez”) to provide water distribution and waste water treatment services to the city of Buenos Aires. The Suez consortium had been granted a 30-year concession as part of the privatisation of said services in 1993 and had run the concession relatively smoothly for the first 7 years of the concession. Tensions arose between Suez and the Argentine government during the Argentine financial crisis of 2001 – 2003, leading to the eventual termination of the concession by Argentina in 2006. Suez claimed US$1.09 billion in lost management fees, unpaid dividends and losses on equity investments, all of which were assessed using the income approach.

Although the final award by the ICSID tribunal was substantially less than that originally claimed (US$404.5 million), the tribunal agreed with the use of the income approach by the claimants’ expert.

Market Multiples Approach

The underlying logic of the market multiples approach is that the value of an asset or business should be similar to the value of another comparable asset or business, for example a business of a similar size in the same industry. An easily understood analogy would be that the price of a 10-year-old Ford sedan should be similar to the price of another Ford sedan of the same model and age.

The market multiples approach is often used in cases involving loss of use, loss of opportunity and reputational harm, as it relies on valuing an asset as a whole, rather than the additional costs or loss of profits arising from a specific action.

This approach is typically used (both in damages contexts and in the investment world) in the valuation of the equity of non-listed companies, for which a share price is not directly attainable but prices for similar, listed companies are. For the market approach to be appropriate, it is crucial that there be a sizeable pool of companies which are similar in terms of product and scale to the subject company and for which there are sufficient observable data points regarding their value. However, the market approach can be used for any assets for which there are sufficient comparators with observable prices.

Applied to the valuation of non-listed companies, the expert (i) identifies recent, arm’s length transactions involving comparable public or private businesses, and then (ii) develops pricing multiples which can be applied to the subject company’s normalised earnings or other relevant metrics of value. These pricing multiples can be based either on the market price of comparable listed companies on a stock exchange, or alternatively on real-world transactions involving entire comparable companies or operating units which have been sold.

The advantages to the market multiples approach are that it is simple to understand, widely used in the investment industry, is based on objective, observable third-party data and does not rely on internal business plans which can often be over-optimistic or biased.

The most common limitation to the use of this method is the lack of sufficient comparable companies, particularly in the case of high-tech start-up companies where there may be few, if any, other listed companies offering the same product or service.

An example where this approach was applied was in the ICSID arbitration case of Crystallex International Corp. (“Crystallex”) v. the Republic of Venezuela, in which Crystallex – a Canadian mining company – launched a claim arising from Venezuela’s expropriation of a gold mine being developed by Crystallex in that country. In that case, the tribunal rejected the respondent’s cost-based valuation approach, and strongly preferred the approach of the claimant’s expert, which was a market multiples approach using the gold reserves of the mine as a metric of value, leading to a damages award of US$1.2 billion in Crystallex’s favour.

Cost Approach

The cost approach is based on the assumption that most or all of the value of a company is in its assets. In this method, the expert determines the overall enterprise value by calculating the value (whether that be Book value or Fair Market value) of the company’s assets net of its liabilities.

The advantages of this method are that it is simple to understand, is based on the current situation of the company and is arguably less subjective in that it does not involve projecting the future performance of the company. It can be an appropriate method when valuing holding companies, companies in liquidation or asset-intensive businesses where cash-generating operations tend to contribute less of the overall value. An assessment of sunk/additional costs, which are typically based on the claimant’s historical financial records, would also fall under the cost approach umbrella.

However, for the majority of companies where cash-generating operations do contribute most of the value, it would not be appropriate. Also it does not directly value intangible assets such as brands or Intellectual Property (IP), and so the expert would have to assess that value separately.

An example of an investment dispute where the cost approach was applied is Asian Agricultural Products (“AAP”) v. Sri Lanka. AAP was a Hong Kongbased company which had a minority shareholding in Serendib Sea Foods “Serendib”), which engaged in prawn farming in the eastern region of Sri Lanka. Serendib’s prawn farm sustained severe damage during a major domestic insurrection between a separatist guerrilla group and government forces. Subsequently, AAP alleged that it suffered a total loss of its investment and claimed compensation of US$9 million. In the final award, the tribunal awarded US$460,000, being compensation purely for the tangible assets of the business, as Serendib was loss-making, and had only made two shipments of its single product (prawns) to its target export market (Japan) at the time of the incident.

5. ESTABLISHING A RELIABLE BUT FOR

One of the most important aspects in arriving at a robust calculation of loss under the income method is establishing a reliable But For scenario.

First, the expert must show that he or she has correctly identified the impact of the breach. Many contemporaneous factors can affect a company’s performance in a given year, and failing to correctly identify these other factors can lead to the expert overestimating the loss. For example, imagine a hypothetical dispute where an auto component manufacturer had failed to supply the required components in time to a well-known car manufacturer. However, the breach coincided with depressed demand for cars in multiple countries due to the impact of the Covid-19 virus. In such a case it would clearly be wrong to forecast loss purely based on pre-Covid-19 historical performance as the overall market conditions at the time of the breach were materially different.

Second, the expert needs to demonstrate that rather than unquestioningly accepting the company’s projections at face value, he or she has critically examined historical performance and any forecasts of the company to arrive at an opinion.

This process involves not just looking at the company’s financial statements, management accounts and internal forecasts, but also external evidence such as broker forecasts and market research reports. These external information sources can be very helpful in highlighting areas where the company has been overly optimistic, and pointing out market-wide trends which may have impacted on the company’s performance during the loss period.

It is at this juncture that benchmarking has a large role to play: a company’s forecast sales prices for a commodity can be benchmarked against broker projections, or the entire valuation using the DCF approach can be benchmarked against alternative methodologies such as market multiples or a costbased approach.

6. CONCLUSION

The above is a general layman’s introduction to the main valuation approaches used in assessing damages in litigation cases. The particular valuation method(s) adopted in any given case must be rooted in the type of loss claimed and the facts of the case.

Finally, it should be noted that the methods listed above are not mutually exclusive, and it is common for experts to use a secondary method (e.g. market multiples) as a sense-check for their primary valuation method (often an income approach).

¹ Fuller & Perdue, The Reliance Interest in Contract Damages (Pts. 1 & 2), Yale Law Journal Vol 46 No.3 (Jan 1937)

² Factory at Chorzow (Germany v. Poland), Merits, 1928 Permanent Court of International Justice

³ As opposed to non-income generating assets (often collectibles) such as fine wine, art, jewellery and gold

⁴ ICSID award in the matter of Suez et al. v. The Argentine Republic dated 9 April 2015, https://www.italaw.com

Open Banking is changing the face of the Canadian financial services industry

Consumers increasingly expect online banking services to rival the services they can access in branch. Open Banking or Consumer-Directed Finance – which officially recognizes consumers’ ownership over their financial data – has the potential to drive innovations in financial technology that will vastly improve the customer experience. Open Banking policy is driven by, and beneficial to, consumers, but it has the potential to seriously disrupt the financial services industry. As fintechs and tech giants begin entering the market and developing products and services based on Open Banking APIs, the Canadian banking oligopoly will begin to erode.

Absent immediate and decisive action by incumbents, there is a significant risk that traditional financial institutions may eventually be reduced to commoditized providers of financial services, competing with other incumbents solely on price in a race to the bottom. For example, Open Banking enabled applications could drastically reduce the need to shop around for mortgages and loans; consumers could simply share their credit information with a third-party app that provides them with a list of quotes from major financial institutions from which they could simply select the lowest rate. Despite posing a threat to incumbents, fintechs can also serve as allies, helping them better serve their customers while defending against the more serious competitive threat posed by the tech giants.

Tech giants are capitalizing on the shift to consumer-directed finance to penetrate the financial services market

Worryingly, most Canadian financial institutions lack the infrastructure and / or the expertise needed to compete effectively with tech giants like Amazon and Facebook, who have both the technological and financial resources to displace incumbents. Google has recently entered the consumer banking space by allowing consumers to open checking accounts and transfer money digitally through the Google Pay app and Apple partnered with Goldman Sachs in 2019 to launch the Apple credit card. In China, an early adopter of Open Banking, tech giants like Tencent and Alibaba have already begun to dominate the financial services market with integrated platforms such as WeChat Pay and AliPay.

Chinese Mobile Payments Market Share by Transaction ValueSource: Bloomberg, iResearch data as of June 30, 2020

In addition to looming competition from Western tech giants, fintechs also threaten to disrupt the industry by chipping away at the consumer-facing link in the financial services value chain by providing innovative services built on Open Banking APIs (including account aggregation, robo-advisory, automated accounting etc.). This shift is particularly problematic in an era of near-zero interest rates putting pressure on lenders’ bottom lines. While fintechs may appear to threaten incumbents, they can also serve as perfect partners for financial institutions looking to remain competitive in the face of potential competition from tech giants. By providing fintechs with access to an API ecosystem and creating mutually beneficial partnerships, financial institutions can leverage their expertise and agility to bring innovative products and services to market faster. Speed to market is key because Open Banking adoption, as well as the competitive pressures that come with it, is intensifying and incumbents should act quickly.

Growth of Financial Services APIs and Fintech Deal Volume

Source: Crunchbase, Programmable Web, Accuracy Analyses

The Canadian Open Banking ecosystem is still in its infancy, incumbents should learn from experiences abroad to help prioritize the most promising use cases

The secular digitization trend in the financial services industry was accelerated by the COVID-19 pandemic, as well as the ensuing lockdowns, which forced consumers to increasingly rely on digital banking as bank branches around the world were shuttered. According to a recent report by TrueLayer, a UK-based Open Banking data aggregator, use of their Payments API grew 832% between March and July 2020. Further, the average transaction value of those payments more than doubled since last year, while usage rates have yet to fall off. The same report analyzed millions of API calls and found that, as of 2020, PFM (personal finance management) was by far the most popular Open Banking use case, representing nearly a quarter of all API calls. PFM includes applications such as account aggregation, smart budgeting and auto-saving. PFM applications are likely the most popular use case because they’re the easiest to implement, but a there are number of emerging use cases in proptech, insurtech and regtech that promise to further revolutionize consumer banking.

Open Banking API Calls by Use Case – Europe (% of API Calls)

Source: TrueLayer

Open Banking not only makes retail banking more convenient for consumers, it also provides SMEs with a previously inaccessible suite of time saving digital tools that help them stay competitive. The TrueLayer report shows that nearly 10% of all API calls for related to automated accounting applications. By automating accounting and other back-office tasks, entrepreneurs can spend more time on their businesses and less money on professional services.

Canadian incumbents have begun preparing for the shift towards consumer-directed finance by forging partnerships with leading fintechs

A small group of large financial institutions, commonly referred to as the Big 6, has long dominated the Canadian financial services value chain. Other industry players including insurance companies, wealth management firms, credit unions and payment processors have successfully competed with the Big 6 in certain verticals, but none have materially disrupted the industry across the value chain. While the oligopolistic nature of the industry has helped Canadian banks outperform their European and American peers, consumers have gotten the short-end of the stick. Open Banking promises to change this by opening up bank data and processes to third parties who can leverage it to create innovative products and services. Most incumbents lack the expertise, technological infrastructure and operational agility to build competitive products and services internally. As such, most incumbents are forced to chose between acquisitions and partnerships when implementing Open Banking use cases, with the latter being the preferred choice for most. Canadian industry leaders have already established partnerships with a number promising fintechs across the value chain in anticipation of the looming paradigm shift.

Canadian Fintech Partnerships

Source: Luge Capital, Company Websites

In 2019, Desjardins announced a partnership with Hardbacon, a personal financial management and account aggregation application, in order to drive traffic to Desjardins’ online brokerage service. In 2017, RBC collaborated with Wave to provide their SME clients with a suite of accounting, invoicing and financial management tools. These mutually beneficial partnerships help incumbents by allowing them to offer improve their service offering and stay competitive without substantial investment. While the existing incumbent-fintech partnerships are effective, they are far from sufficient. Customers’ needs and expectations are constantly evolving and incumbents must continuously innovate to ensure they can keep up with the rapid pace of change. If they don’t, the tech giants will.

Selection of Financial Services Industry Clients

In collaboration with:
Charles-Antoine Condomine (Manager), Marius Henault (Analyst),
Justine Schmit (Manager) and Vincent Thebault (Manager).

IN SUMMARY

It is widely recognised that a safe haven investment is one whose value does not fall during an economic or financial crisis. A safe haven investment is therefore a counter-cyclical investment in the sense that it is highly resistant to economic cycles and exhibits lower correlation to more risky asset classes.

Gold is often presented as the safe haven investment of choice. It has served as a store of value since ancient times. Its market price is not directly linked to changes in financial markets or the economic context, but this does not necessarily mean that its value will not fall, as was the case from 2012 to 2016 (figure 7).

Even if real estate is also often presented as a safe haven investment, it is worth investigating the reality of such a proposal. Indeed, real estate is often presented and discussed as a whole in the mainstream media. However, the term covers various asset classes that each follow their own logic and rationale. The current crisis is revealing the risk associated with various assets, and now is therefore an ideal time to discuss this notion.

In this article, we will examine the real estate market from two different perspectives:

A. First, we will analyse direct ownership of a real estate asset. We will note that the French prefer investing in real estate directly, as it is a reassuring asset class with an unrivalled (and even improving) balance between risk and reward over the long term.

B. We will then focus on mechanisms for indirect investment in real estate, such as shares in different types of real estate companies, developers and REITs¹, with a special focus on this last type. These companies (whether listed or not) tend to outperform the market in the long term, but they are more risky than direct ownership of the underlying asset. This usually results from the high levels of debt that these companies often have, as well as the highly specific nature of their assets (housing, shopping centres, offices, warehouses, etc.).

As a conclusion, we will touch upon the impact of the current health crisis on the French real estate market. The market has slowed down considerably, with multiple risks weighing down on both real estate assets and stocks.

1. INVESTING IN BRICKS AND MORTAR, AN UNBEATABLE RISK–REWARD RATIO OVER THE PAST 30 YEARS

A. Real estate, an asset class appreciated by investors (and individuals)…

The French are fond of investing in real estate: for example, 65% of them owned their main residence in 2018, compared with 52% in Germany². This proportion grew continuously between 1980 and 2010 and has since remained flat at this level. Further, real estate represents approximately 61% of the wealth of an average French household³.

Although the price of real estate can vary depending on different parameters (location, interest rate, economic context, demographics, etc.), several reasons can explain the preference of individuals and investors for this class of asset:

• Real estate assets are tangible, physical, material.

• They benefit from (i) a primary use value in the sense that they fulfil certain fundamental needs (for individuals as much as for businesses) and (ii) a high exchange value. These assets can easily be rented or sold, which makes them liquid in a market and therefore easily transferable. This represents a level of security for the owner, who is able to get rid of the asset quickly and relatively easily.

• Real estate lasts over time, with a longer operating cycle and investment horizon than the majority of other assets (long construction and occupation period (minimum 18 months, French law for commercial leases 3, 6, 9 or 12 years), etc.).

• Real estate assets are able to generate stable revenues over time.

For these reasons, the price of real estate does not correlate particularly highly with other asset classes such as shares or bonds. These differences make investing directly in real estate an attractive option when pursuing a strategy to diversify an investment portfolio. But beyond pure diversification purposes, investing directly in real estate in France can help investors to optimise their risk–reward ratio in their portfolios.

B. …and an unbeatable risk-reward ratio over the past 30 years

The return on a real estate asset can be received in two ways:

• Rent received (or saved for an individual in his or her main residence) from the letting of the asset

• The variation of the value of the real estate over time.

Based on data published by the Institut de l’Epargne Immobilière et Foncière (IEIF), over a period of 30 years, the return on a real estate asset has equalled that of the stock market but with a lower level of volatility (and therefore lower risk)⁴.

Figure 1: Average risk–reward ratio over 30 years (1988–2018) in France for different classes of asset

Source: IEIF (Institut de l’Epargne Immobilière et Foncière)

–

Figure 1 notes:

The return calculation is based on an entry price, an exit price and intermediate flows for unlisted assets and on annual performance with revenues reinvested for listed assets.

*Livret A corresponds to a type of instant savings account.

If we look at the development of the risk–reward ratio in the shorter term (five years in the figure below), we can see that real estate globally outperforms other classes of asset.

Figure 2: Average risk–reward ratio over five years (2013–2018) in France for different classes of asset

Source: IEIF

Over the period 2013–2018, a period of more limited volatility in the stock market, the return on real estate assets remains high for a limited risk. The stability in this performance can be explained by several factors, including:

• the scarcity effect of real estate in certain areas (land availability, lack of new housing construction, etc.);

• the appetite of the French for this asset class;

• strict financing conditions required by lending banks in France, based on solvability criteria versus real estate value. Such a mechanism limits downward trends in case of economic downturns;

• recent easier access to credit and a downward trend in interest rates.

The risk–reward ratio of real estate assets is therefore more attractive than that of other classes of asset, no matter their level of risk. It should be noted that unlisted real estate funds (SCPI, OPCI) perform in line with direct ownership. Only gold (which does not generate rent or dividends!) comes close to the return profile of a real estate asset, but it suffers from a more disadvantageous volatility profile due to a significant price sensitivity in terms of different tensions in the economy, movements further amplified by leveraged financial products.

C. Transaction volumes historically affected by crises but proving resilient

The characteristics of the real estate market have pushed French investors to invest heavily in this asset class.

Figure 3: Number of transactions and year-on-year change in prices of second-hand housing in France from Q1 2000 to Q3 2019

Source: CGEDD according to DGFiP (MEDOC) and notary databases

The number of conveyancing transactions for homes in France remained globally constant until the subprime crisis; it even grew between 2001 and 2003 despite the stock market crash following the bursting of the dot-com bubble and the 9/11 attacks in 2001.

The volume of transactions fell by approximately 30% between 2007 and 2008, as access to credit was restricted in this period. This fall in the number of transactions led to a decrease in the average price of real estate in France of 9% between Q1 2008 and Q1 2009.

The real estate market started to recover from 2009, with the massive quantitative easing programmes instigated by central banks and updated conditions for access to credit. Between spring 2009 and summer 2011, real estate prices in France grew by 12%⁵, and the cumulative volume of transactions over 12 months returned to its level from before the crisis, all this despite the ongoing eurozone crisis.

This level of transaction volumes continued growing significantly over the period, after a low point in 2012. This low point mostly derived from a slowdown in investment decisions made by individuals because of the economic uncertainty partially generated by the sovereign debt crisis in the eurozone.

Thus, we can see that over a long period the volume of transactions for residences can sometimes be affected by economic crises, but it tends to recover quickly. In parallel, the value of the underlying assets is resilient in times of crisis, notably because of the reluctance of real estate owners to reduce selling prices.

Direct ownership of real estate can therefore be considered a safe haven investment due to its lower volatility compared with the stock market. Direct ownership’s past performance can even be considered as a paradox and a golden era as its return on investment has been equal to (over a 30-year period) or even higher than (over a 5-year period) stocks. Such surprising performance can be explained by several factors, such as falling interest rates or the scarcity effect. In the next part, we will review indirect real estate ownership investment through ownership of REIT shares.

2. LISTED REAL ESTATE PLAYERS: FROM DEFENSIVE TO VOLATILE VALUES

A. Two large categories of shares for two different roles: developers and REITs

As a reminder, the real estate industry can be split into two main types of players:

• Developers, who build and then sell properties

• REITs, who invest in and then manage (or outsource management of) properties.

These two categories for the most part comprise listed international players operating in several countries – real estate provides investment opportunities all around the world. The largest market is in the USA, with over 220 listed real estate funds and numerous significant developers. By comparison, Europe has around 30 listed funds.

The IEIF Real Estate France index provides a view of the market performance of real estate players in France, composed of 13 REITs and five real estate developers listed in France (cf. appendices for breakdown). Over the period from 1991 to 2020, companies making up the index developed significantly, with for example several major mergers and acquisitions (Unibail and Rodamco in 2007, then Westfield in 2017, Klépierre and Corio in 2015). Furthermore, several French REITs are amongst the leaders in Europe.

B. Real estate stocks: from defensive to volatile values

As shown in the figure below, the IEIF index has outperformed the CAC 40 since the dot-com bubble burst in 2001–2003, with a rapid recovery following the subprime crisis of 2008–2009.

Figure 4: IEIF Real Estate France index vs CAC 40 until 31 December 2019 (base 100 as at 31/12/1990)

Source: IEIF, CapitalIQ

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Figure 4 notes:

As REITs distribute a significant level of dividends, the indices have been given with the reinvestment of gross coupons.

In September 2000, the CAC 40 reached a significant peak of 6,944 points (excluding reinvested dividends), the culmination of a period of strong growth since the middle of the 1990s. The bursting of the dot-com bubble followed, then the 9/11 attacks in 2001, leading to an almost 65% decrease in the value of the CAC 40 in two and half years. During this crisis – and even though the CAC 40 fall was accentuated by the weight of banks in it – REIT share values, in contrast to the CAC 40, continued to climb (even faster than gold). Over this period, investments in listed real-estate companies acted like safe haven investments, with prices uncorrelated to market developments.

Conversely, after the dot-com crisis and the 9/11 attacks, namely in around 2003, REIT market values correlated much more closely with the development of the rest of the market. This change in the behaviour of REIT share values results from a certain number of structural factors detailed below.

Risk–reward ratio of real estate stocks vs direct ownership of real estate assets

The figures below consider the REITs included in the IEIF Real Estate France index, in the analysis of risk–reward presented previously⁶.

Figure 5: Risk–reward ratio over 15 years in France – REITs vs other classes of asset, 2003–2018

Source: IEIF (Institut de l’Epargne Immobilière et Foncière), Accuracy analysis

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Figure 5 notes:

The periods 1991–2003 and 2003–2018 are presented for the stock exchange index and REITs.

As previously observed, between 1991 and 2003, REIT shares constituted low-risk assets, exhibiting for that matter a reward profile below that of the market. Conversely, the period 2003–2018 shows a change in behaviour for these assets, becoming more volatile with a greater reward profile.

Further, we can observe the difference in volatility between the real estate assets (direct ownership of real estate assets) and the shares of the real estate companies. This can mainly be explained by the level of financial leverage of these companies (figure 10).

Several elements can be put forward to explain the shift in the profile of real estate stocks towards greater volatility:

• Growth of financial leverage: REITs saw their debt ratio increase from 0.6 in 2005 to close to 1 from 2018⁷.

• Underlying real estate prices grew significantly in the 2000s, thereby increasing the value of REIT portfolios and leading to significant growth of their corresponding share prices.

• The dividend distribution rate grew from 0.6% in 2005 to 6.4% in 2018 (see appendix, page 12)⁸.

• A tax incentive scheme (SIIC regime in 2003) was introduced in France creating a structural surplus value in this sector, even if, for several years now, the market net asset values of REITs (mainly based on the gross market values of the underlying assets) have been significantly below their share market value, notably for retail REITs (e-commerce impact).

• New high-performing niches have developed (e.g. logistics with the growth of e-commerce).

• There was a consolidation trend in the market, with several significant mergers as previously mentioned.

The strong performance of the real estate market also pushed numerous institutional players to increase the percentage of their investment in the sector, or even to create their own real estate funds, such as Axa, Amundi and BNP Paribas. However, when it comes to retail, performance has recently been affected by the expansion of e-commerce, further accelerated by the COVID-19 crisis (see below).

Due to several factors (including higher leverage and dividend yields), the profile of REIT stocks has changed: formerly defensive and resilient to economic cycles, they have now become volatile, yield assets.

3. THE COVID-19 CRISIS: A LEAP INTO THE UNKNOWN

We have not seen the like of the current crisis since 1929, either in terms of its nature or in terms of its current and future impact on the economy and the markets. Stock markets globally lost almost a third of their value in less than a month and numerous economies shut down as a result of the lockdown of several billion people across the world.

Listed real estate companies have not been spared: the IEIF Real Estate France index has been affected even more than the CAC 40 since the start of the year, as shown in the figure below.

This is notably the consequence of more volatile stocks (as explained previously), in particular the impact of the previously unforeseen closure of shopping centres. It is, however, too soon to conclude on any kind of development in the valuations of REITs and real estate developers.

Figure 6: IEIF Real Estate France index vs CAC 40, base 100 as at 31/12/2019

Source: IEIF, CapitalIQ

Two major risks are weighing down on the real estate market (both direct and indirect ownership):

• A restricted availability of credit: as was the case during the subprime crisis, banks may restrict access to credit for a certain period, reducing investors’ borrowing capacities or removing them from the credit market altogether.

• Business failures: business failures would lead to an imbalance between supply and demand in the professional real estate market.

As observed in the past and if the conditions for obtaining mortgages are favourable, the safe haven status of direct ownership investment real estate should make it possible for real estate transactions to pick up again and for asset values to remain stable or suffer only a very limited decrease.

However, market players – both REITs and developers – are exposed to far greater risks.

The lingering risk for REITs in the short and medium term relates mostly to rental risk. As their portfolios are largely composed of offices and retail properties, the health of the economy as a whole will have a direct impact on their level of risk.

Almost all retailers had to close for a certain period of time during the lockdown. The recoverability risk and the level of rent during and after the lockdown period is therefore very significant. This is all the more true as rent (and service charges) represent a very high fixed cost for retailers.

We are thus seeing an increase in the number of requests from retailers for the outright cancellation of rent during shutdown periods and the full variability of rent on the basis of revenue for a period to be defined (generally the time it takes to regain the pre-crisis level of activity).

In the longer term, this trend may end up calling into question the standard long-term commercial lease in France, with the aim (for retailers) of making lessors bear more of any potential operating risk.

As for real estate developers, the risks are more limited at this stage and mostly concern delays in construction, the slowdown in the commercialisation of certain assets, and changes to buyers’ needs.

Indeed, the massive use of remote working during the lockdown and the proof of its success may well lead the main users of office spaces to rethink their use of – and even their need for – their headquarters and other buildings.

CONCLUSION

Direct ownership of real estate clearly presents the characteristics of a safe haven investment (limited risk, certain return, uncorrelated with economic cycles, etc.); indeed, it has demonstrated this fact historically. Paradoxically, it has also demonstrated historically that it is at least as profitable as stocks for a more advantageous risk profile. We note, however, that these characteristics depend heavily on the development of interest rates and access to credit policies put in place by financial institutions.

Conversely and since approximately 2003, listed developers and REITs have become offensive stocks that outperform and are more volatile than the market. The new risk–reward ratio associated with them can be justified through various parameters: the sometimes high level of leverage of these players, an accommodating dividend distribution policy, the economic performance of the underlying asset and the implementation of tax incentives, notably.

The economic crisis resulting from the COVID-19 health crisis has had a direct impact on the real estate market. In the short term, the hardening of conditions to obtain credit should, in theory, automatically lead to a reduction in transaction volumes, leading to deflationary pressure on prices. In the long term, we are witnessing the calling into question of the use value of certain assets (commercial, office, urban residential real estate, in particular). If this paradigm shift were to become structural, it would be a departure from the long development cycle analysed above and would necessarily result in a decline in financial value.

Appendices

Figure 7: Price of gold vs CAC 40 from 01/01/1980 to 21/07/2020

Source: CapitalIQ

Figure 8: IEIF Real Estate France index – Weighting as at 21/07/2020

Source: IEIF

Figure 9: REITs and real estate companies vs CAC 40, base 100 as at 31/12/2019

Source: CapitalIQ

Figure 10: Ratio of net debt / equity, 2003–2018

Source: CapitalIQ

Figure 11: Dividend Yield, 2003–2018

Source: CapitalIQ

¹ REIT: Real Estate Investment Trust

² Proportion of real estate owners within the French and German populations 2009–2018, Statista

³ Revenues and wealth of households – 2018 edition, INSEE

⁴ The volatility formula used by the Institut de l’Epargne Immobilière et Foncière is not shared but it is generally accepted that it is calculated as an annualised standard deviation calculation.

⁵ Notaries / Insee index of second-hand homes

⁶ Real estate developers have been excluded from this analysis because few players have been listed over a long period.

⁷ Ratio between net book value of debt and net book value of equity

⁸ Ratio of the dividend from year N-1 / share price in year N

IN SUMMARY

The social and environmental issues that we currently face constitute major challenges that raise questions for all of us. When it comes to these issues, we no longer have a choice: we need to find innovative solutions to tackle them.

Today, philanthropists are no longer the only ones addressing social and environmental issues; financial investors are now fully fledged players in this challenge.

Why? Because it has now been proved possible, and not at all contradictory, to combine social and economic performance in an investment. Indeed, key players in the public and private sectors have seized the opportunity offered by impact investments. They see impact investing as a major tool to catalyse the change of scale needed for innovative responses to fundamental issues.

This paper is aimed at providing a first insight as to how stakeholders (regulators, investors and social entrepreneurs) are working together to create a professional ecosystem looking to improve society while generating financial returns.

Over the next episodes of this series will deep dive on the main sectors, the challenges of social impact measurement and the financial innovations related to this market.

A. Impact investing refers to investments with an intentional positive impact on people and/or the environment, taking place via sustainable and profitable initiatives and subject to evolving impact measurement.

B. In 2018, this market was estimated at $502 billion, a market eight times larger in just five years.

C. This growth has been mainly driven by (i) growing social consciousness in our society regarding environmental issues, (ii) new and traditional investors searching for ways to contribute to society in order to complement limited public spending to address these problems alone and (iii) growing interest for responsible and economically sustainable business models.

D. However, this sector is facing lots of new challenges such as how to appropriately measure social impact, the necessity for a legislative framework and the constant need for innovative solutions.

1. THE CURRENT IMPACT INVESTING MARKET

A. Impact investing: much more than just a trend

Impact investing is undoubtedly a concept with various and evolving definitions. The Rockefeller Foundation used the term for the first time in 2007 during a conference organised to evaluate the possibility of developing a type of investment with a social and environmental
impact.

The Global Impact Investing Network (GIIN) defines impact investing as an investment that explicitly combines social return and financial return. More concretely, it refers to investments with an intentional positive impact on people and/or the environment, taking place in sustainable and profitable businesses and subject to proactive impact measurement before, during and after the investment (greenhouse gas emissions avoided, number of jobs created, etc.). This type of investment aims to break the usual practices of financial players who used to either give money away without any expectation of financial return (“doing good”) or invest money as well as possible to maximise financial returns (“doing well”). Impact investing is therefore doing good and doing well at the same time.

It is essential to disassociate impact investing from socially responsible investments. The purpose of the latter is to exclude from investment portfolios certain companies that may be less virtuous than others or to choose, in each sector, the “best in class” according to extra financial criteria. Unlike socially responsible investments, impact investing concerns companies where investors prioritise not so much “return” but “meaning”. It is necessary to understand the differences between those companies that claim to be “green” because they apply environmental, social and governance filters when they screen financial deals and those companies whose true ambition is to create positive social and environmental impacts.

Spectrum of business based on social impact and financial return

Source: “Financial performance of impact investment” market study 2017-2018 and Accuracy analysis

B. A growing $502 billion financial market

The impact investment market has grown significantly in the last decade. In terms of assets under management, GIIN estimated a market of $60 billion in this sector in 2014¹. This figure increased to $228 billion in 2017² and $502 billion³ in 2018, a market eight times larger in just five years.

The graph below highlights the diversification of the investments in this sector. Impact investors have different asset allocation strategies, investing across geographies, sectors and types of instruments.

Assets under management (“AUM”) by geography, sector and instrument

Source: GIIN 2019 Annual Impact Investor Survey and Accuracy analysis

Regarding return on investment, contrary to the general belief that impact funds generally underperform traditional investment funds and do not necessarily seek market returns, GIIN published a study “Global Impact Investing Network – 2019” whereby it shows that 66% of impact funds seek market-rate returns. Moreover, 77% of the funds performed in line with their expected financial return and 14% outperformed expectations⁴.

In Europe, the UK has pioneered in impact investing benefitting from an innovative and growing market since the 2000s thanks to numerous political and regulatory initiatives, followed by Northern European countries, like Sweden, and Western European countries like France, the Netherlands and Switzerland.

Expectations vs. performance

Source: GIIN 2019 Annual Impact Investor Survey and Accuracy analysis

C. Consciousness + opportunity + search for meaning: three key growth drivers

The question that now comes to mind is the following: why is this market growing today? Our analysis demonstrates that two worlds are converging. First, the world of finance, which has experienced many setbacks since 2007, is seeking to include ethical considerations in its investments; it is discovering the impact investment as a business model capable of generating social wealth with more secure longterm profitability. Second, the world of social entrepreneurs, who are now able to generate profitability and a strong social impact, wishes to free itself of public finance (which is more and more limited). This convergence has been driven by several factors.

• First, society is now more conscious that economic growth aspirations are outpacing global resource supplies. Currently, we consume 1.7 planets’ worth of resources⁵ and, consequently, we are facing challenges such as climate change, resource depletion, biodiversity loss, consumption patterns, pollution and waste management, supply chain fairness and widening income inequality. The United Nations explained that significant resources are required to address these challenges. The Sustainable Development Goals (SDG) gap provides opportunities for private capital to supplement public funds.

• Second, as proved by the United Nations, social public spending is no longer growing as fast as is necessary. Charitable donations and public aid are no longer sufficient to tackle global social issues.

• Third, world leading investment firms are increasingly attracted to responsible business models and socially responsible returns. Not only does this add another layer of motivation to their staff but it also provides new business opportunities. These players are able to bring an outstanding talent pool to assist in the development of new opportunities with a long-term view on sustainability and social impact. The development of a more socially responsible form of finance is also a response to the excesses of the financial sector highlighted by the 2008 crisis.

Estimated capital requirement – Potential private sector contribution (USD trillion)

Source: World Investment Report United Nations Conference on trade and development 2014 and Accuracy analysis

2. THE FUTURE OF THIS GROWING MARKET

A. Innovate, finance and support – the new players in the game

The growth of this sector has created a new innovative and large ecosystem that includes more and more players and solutions. These new players are helping to put in place the tools that promote a more efficient functioning of this market. This new ecosystem is mainly composed of entrepreneurs, investors and support structures.

Previously considered as a “niche” for a few specific social investors, impact investing is now a new market for “traditional” investment funds. For example, a leading global investment firm announced in February 2020 the closing of its Global Impact Fund at $1.3 billion. This fund is dedicated to investments in companies with business models aimed at providing solutions to environmental or social challenges. In the same month, another leading investment fund announced its intentions to direct its investments towards companies committed to impact investing.

In addition, the EU has reached an agreement to establish European rules defining sustainable investments. This new framework (due to be in place by 2021) would give a “green” label for investments that cover renewable activities; it would grant lower labels for investments that are not fully renewable but help to reduce CO₂ emissions.

Impact Investing ecosystem

Source: Accuracy analysis

B. The main challenges for a better future

What challenges does impact investing face? The first is the impact measure itself. Due to its specific characteristics, financial players must adopt a new logic: the objective is to generate returns on financial investment while obtaining the greatest possible social impact. One of the key challenges for this is to measure social impacts in the same way financial impacts are measured, that is with some form of tangible metric. Several options are currently being explored in order to measure the “Social Return On Investment” (SROI).

The second challenge relates to the legislative framework and public incentives. The legislative framework should be adapted to allow and enhance the development of impact investing worldwide. One good example of this was introduced in the UK in April 2014 with the Social Investment Tax Relief (SITR), a tax reduction of up to 30% of the investment for individuals who invest in small social enterprises.

The third challenge relates to completing the funding chain of social companies. Like traditional investment funds, the majority of impact funds choose to finance companies either in the growth stage or to finance mature companies. Indeed, mature companies represent approximately 55% of assets under management and companies in the growth stage represent 34%, but companies in the venture and seed stages represent only 9% and 3% respectively 7. Therefore, the ecosystem needs to complete the social entrepreneurship funding chain so that appropriate funds are available at all the key development stages of a social enterprise.

The fourth challenge concerns the financial world’s need to continue innovating in order to find available liquidities for impact investment players. An example of these are Social Impact Bonds (or “Pay for Success Bonds”) which pursue the objective of raising private capital to finance public social actions. Social Impact Bonds intend to fund activities, which, if not carried out, will result in future costs for public authorities. The first Social Impact Bonds were launched in the UK. The project goal was to provide re-entry services to prisoners leaving prison. The bond is remunerated on the basis of a fixed and clear objective: to reduce offense recurrence by 10%. If the objectives are achieved, public authorities reimburse investors with capital, at a specific rate computed based on the savings made by the public authorities thanks to the bonds. However, if the objectives are not achieved, private investors lose the investment.

CONCLUSION

All stakeholders in this innovative ecosystem have a role to play in promoting this new dynamic investment market. Financial intermediaries like banks, financial players, financial and strategic advisors and accounting firms, must also better understand how impact investing can become a vector of progress.

Companies focused on impact investing are clearly here to stay. They aim to have an impact on our society and at the same time earn a return for their investors. However, the road ahead is long and there are many challenges to establish a common framework to enhance this blooming ecosystem. The current pandemic will increase social issues. It will also force countries to rethink and redesign how we define the economy and how we assess value. Now is the time to capitalise on this sector in order to build markets that ensure sustainable growth for all. Now is the time to do “well” by doing “good”!

¹ The Impact Investor Survey – Global Impact Investing Network and J.P. Morgan – May-2015
² Annual Impact Investor survey – Global Impact Investing Network – 2018
³ Sizing the impact investing market – Global Impact Investing Network – April 2019
⁴ Annual impact investor survey 2019 – Global Impact Investing Network – 2019
⁵ Ecological Footprint — The Global Footprint Network
⁶ Financed by public and private investment.

SUPPORTING START-UPS

Episode 1:
How do business support structures enable value creation in France?

Episode 2:
Business support structures looking for a new role and new models

IN SUMMARY

At a time when innovation is increasingly becoming the driving force behind all economies, start-ups find themselves on the front line thanks to their simple and agile structures that enable them to venture into the most promising sectors.

However, innovation cannot develop successfully without a complete and adapted ecosystem, bringing together all the players that must interact and join forces to bring innovative projects to life (organisations, companies, start-ups, universities, investors).

This article details the dynamics that are  forging the innovation ecosystem in Morocco, whether related to national strategies or private initiatives. Our mapping of support structures makes it possible to assess strategic issues in particular, for any country looking to take full advantage of its potential for talent  and entrepreneurship.

This is key as innovation constitutes a critical lever for the economic growth and development of a country.

A.  Support structures for innovative start-ups have multiplied in Morocco thanks to political and private initiatives.

B. Nonetheless, their presence in Morocco remains uneven and concentrated in the Casablanca region.

C. Large Moroccan companies are progressively contributing to the innovation ecosystem and are starting to use open innovation as a value creation lever.

D. Moroccan innovation,  measured in terms of patent applications and start-up fundraising, is still not achieving its full potential.

1. INNOVATION SUPPORT STRUCTURES IN MOROCCO
WHAT DOES THE CURRENT INNOVATION LANDSCAPE IN MOROCCO LOOK LIKE?

A. A growing number of structures

Support structures are composed of both physical and non-physical structures. They include (i) incubators and accelerators, (ii) co-  working spaces, (iii) support programmes and (iv) financing programmes. Based on our research, there are 74 active and planned support  structures in the country.

Among them, Technopark was the pioneer and now constitutes a textbook case. Created in 2001 as the fruit of a  public–private partnership, Technopark is managed by the MITC (Moroccan Information Technopark Company), whose founding  shareholders are the Moroccan state (35%), the Caisse de Dépôt et de Gestion (17.5%) and Moroccan banks (47.5%). The MITC offers  work spaces and supports startups by allowing them to benefit from its privileged ecosystem. The model was duplicated in Rabat in 2012  and Tangier in 2015 and will soon be duplicated in Agadir (opening planned in 2021). Technopark has supported over 1,100 companies  since its creation, particularly in the information and communication technology (ICT), green technology and cultural industry sectors. It  is well known that start-ups require financial support and specialised assistance. But the need to integrate them into a community to  interact and exchange ideas is just as vital. Indeed, a start-up community represents a rich and diverse source of collective intelligence,  which enables start-ups to discuss ideas in co-working spaces, exchange best practices and build a network to develop.

New structures  have developed in Morocco with this very thinking in mind, offering support, training and mentoring services. These support structures  organise different events like hackathons, where various teams (composed of developers and project leaders) must find the solution to a  strategic issue by producing a proof of concept (in general, software or an application) in a very short space of time. In December 2019,  Emerging Business Factory organised the first ‘water hackathon’ in Marrakech with the aim of making water use in the area sustainable  and eco-responsible.

Other support structures have implemented co-working spaces for all those who wish to launch their entrepreneurial projects and are looking for a community of partners. One such example is New Work Lab, created in Casablanca in 2013. It is a space dedicated to the development of Moroccan start-ups through the organisation of meetings, training and the provision of a co-working space.

Mapping of start-up support structures

B. An uneven geographic split

Although support structures are concentrated primarily in and around Casablanca, regional dynamics resulting from a strong political will  take shape through:

• the duplication of Technopark in other cities in the country;

• regional development projects, such as the innovation city of the Souss-Massa region, which plans to make R&D laboratories available  to start-ups, or Mazagan’s urban hub, developed by the OCP and the government;

• support mechanisms on a national scale, with for example the Réseau Entreprendre Maroc and Injaz Al-Maghrib, which support start-  ups, or even the financing programme Fonds Innov Invest.

However, the support offered in some large Moroccan cities, like Fes and  Meknes for example, is far below the needs of their large student populations.

At the start of the school year in 2017, the Euromed University of Fes (UEMF) had over 1,300 students and researchers², suggesting a  potential talent and entrepreneur pool that should not be neglected.

C. Mostly generalist structures supported by a wide range of sponsors

Though the vast majority (75.7%) of support structures are generalist, three specialisations stand out:

• ICT, in particular thanks to the rise of fintechs working with corporates (e.g. StartOn, Fintech Challenge);

• green technology, with Morocco having set itself the target of reducing its energy dependence and investing in renewable energies (e.g.  Social Green Tech Bidaya);

• the social and solidarity economy, relying on, for example, sport to create a link between youth employment and the entrepreneurial  spirit (e.g. TIBU Maroc).

It is interesting to note that the sponsors of support structures are diverse: 57% of support structures are backed  by at least two organisations (assistance, financial support, etc.). Further, 32% of these structures come from public-private partnerships.  Entrepreneurial support initiatives thus form part of a collective intelligence approach, a pooling of resources between complementary players – in short, open innovation.

2. THE GROWING INVOLVEMENT OF LARGE COMPANIES
HOW ARE LARGE MOROCCAN COMPANIES TAKING HOLD OF INNOVATION?

A. OCP: a heavyweight in the national economy and a global innovation model

Moroccan companies are gradually incorporating open innovation and digitalisation into their organisations in addition to increasing their  employees’ awareness of innovation culture. A good example of this can be found in the OCP group.

OCP is the world leader in  phosphates and the leading industrial company in Morocco. It has put in place an ambitious investment programme (2008-2027), where  it aims to double its mining capacity and triple its transformation capacity.

However, where it is of particular interest is in its efforts to  boost innovation. Indeed, it has initiated several projects to boost innovation in Morocco and within the group. In addition to physical  support structures, numerous programmes have been implemented, such as the Seed- Stars Startup Competition or the Impulse  acceleration programme in partnership with Mass Challenge, as detailed below.

The university environment gives us access to innumerable research centres across the world and open innovation […]
When we are at the university, we are able to have a different type of dialogue, one that is much more productive

Mohamed Soual, chef economist at OCP.

B. The growing involvement of Moroccan banks

Moroccan banks are not to be outdone in this matter. Attijariwafa Bank and BMCE Bank of Africa were pioneers in 2001 by financing  Technopark Casablanca. They have been highly active in the promotion of innovation over the past five years.

Moroccan banks’ initiatives in favour of entrepreneurs have naturally led them to turn their innovation approach inwards to improve their  own processes and offers in the context of increasing digitalisation. But the delegation of support management to a pure player is often  essential in order to facilitate cooperation and maximise value creation between stakeholders. This is particularly the case when the  stakeholders have very different cultures, notably when it comes to public-private partnerships.

Though Morocco’s start-up ecosystem has been strengthened by the launch of various support and financing mechanisms (as detailed  below), the number of innovative technology companies in the country measured in terms of patents and fundraising is not meeting its  potential, as detailed in the following pages.

Management of support structures created by Moroccan companies on the Moroccan All Shares Index (MASI)

3. INNOVATION AND ITS FINANCING IN MOROCCO
HOW HAS INNOVATION DEVELOPPED IN MOROCCO OVER THE PAST FIFTEEN YEARS?

A. Successive industrialisation strategies have contributed to raising the general level of innovation

Comprehensively evaluating the innovative nature of a country requires consideration of its institutional environment, infrastructure,  training, R&D, and market structure and creation. The Global Innovation Index 2019 ranks Morocco 74th out of 126 countries based on  80 variables ranging from ease of obtaining credit to the protection of minority interests in a company. This index also distinguishes  between input variables that define a country’s potential for innovation and output variables that measure effective innovation.

Our  analyses here focus on the two output criteria that seemed the most tangible: research dynamism, measured through the number of patent  filing requests (industry driver), and fundraising for technology and digital start-ups, which testifies to the potential for economic  development. Reviewing the development of these variables against the backdrop of successive industrialisation plans implemented by  the Ministry of Industry, Trade, and the Green and Digital Economy since the mid-2000s highlights a correlation. As shown in the figure   elow, thanks to industrial policies, as well as the country’s stability and closeness to the European Union, Morocco has become a top  destination for foreign investors.

Since 2005, three major industrial strategies have succeeded each other, with a substantial effect on the  development of the number of patents filed. Nevertheless, these effects seem to differ based on the nature of the players considered.  Indeed, patents filed by non-residents tripled between 2014 and 2018, whilst those filed by Moroccan residents almost halved over the  same period.

The dynamism of national research seems to be losing momentum and remains mostly the domain of universities (58% in  2018), with Moroccan companies only requesting 9% of patent applications.

At the same time, the significant growth in filings from  abroad testifies to the increased appeal of the country. This can be explained by two factors. First, the presence of foreign actors in  Morocco has intensified across various sectors, like the automotive or aeronautics sectors, following the Industrial Acceleration Plan.  Second, the implementation of a new way of filing patents by the European Patent Office, thanks a partnership with the Ministry of  Industry, Trade, and the Green and Digital Economy in 2015, enables those filing patents in the European Union to also request patent  protection in Morocco. Thus, the USA (20%) and European countries, with France and Germany in the lead (8% each), are the most  represented among the countries of origin of those filing patents.

Patent filing requests in Morocco (2005–2018) and industrialisation strategies

Note:
We have analysed the development of the number of patent applications because of the ease of access to this data. Though this makes it possible to have a vision of the effects of the successive industrial policies, it does not make it possible to assess the entirety of their effect

B. But the funds raised by start-ups remain modest compared with other countries in the region

Fundraising constitutes another indicator of the dynamism of the innovation sector. Though it is difficult to establish a causal link  between fundraising and the practice of filing patents, these two phenomena constitute complementary indicators of the dynamism of  innovation in the countries concerned.

The spread of fundraising practices reflects, in particular, the growing participation of national and  international private players in innovation financing. However, the small amounts concerned tend to show the predominance of public capital in innovation financing or indeed the internalisation of innovation by existing companies.

In terms of fundraising, Morocco places 12th in Africa in 2019 with USD 7 million raised by technology and digital start-ups (vs USD 3 million in 2018 corresponding to 15th  place)³. We have gathered data to compare the situations in Algeria, Tunisia, Nigeria, Kenya and Egypt with that of Morocco. The  differences can be explained by various factors such as access to financing, financing raised in other countries or the use of alternative  eans of financing. In particular, we have put the amounts raised in perspective by setting them against the respective GDPs of each  country. Finally, for all these countries except Algeria (for which we do not have enough data), we have studied the development of the  amounts raised between 2018 and 2019.

Generally speaking, we note an increase in fundraising over these two years: Morocco, Kenya, Nigeria, Egypt and Tunisia all experience a significant increase in the amounts collected. As for the amounts themselves, Kenya and  Nigeria stand out clearly from the other countries. The case of Nigeria may be explained simply by the size of the country’s economy (USD 368 billion in 2018); the case of Kenya, however, is different (USD 87 billion in 2018). Indeed, Kenya proves to be fertile ground  for the development of startups. Widely distributed low-cost internet and the digitalisation of payments in 2007 with the launch of  M-Pesa, a mobile phone money-transfer system, have greatly facilitated transactions and have been a boon to entrepreneurs in the country.

By way of comparison, the amounts raised in Morocco seem low in relation to the country’s GDP (USD 120 billion in 2018).  Beyond the difference in the size of the economies considered, there may be various reasons for this result: fewer private initiatives due to  an economy structured around rent-based activities or in low-risk sectors, insufficient tax incentives for both entrepreneurs and  investors or even the less prevalent practice of fundraising.

The lower access to fundraising in Morocco compared with other African  countries (Egypt, Nigeria, Kenya, Rwanda, etc.) can also be explained by the language. In contrast to the francophone Morocco, the other countries mentioned are anglophone, meaning they are more easily able to capture foreign capital (particularly coming from the US and  the UK).

These figures can be used on a macroeconomic scale to measure trends, such as the opening of certain economies to foreign capital, but they also reveal the level of appropriation of certain best practices by local players. In this context, public policies can play a facilitating role in a ‘top–down’ approach. Nevertheless, local realities should not be ignored. Indeed, beyond public policies, it is the players’ actions and the quality of their interactions that enable them to create together innovative programmes and determine the  dynamism of a sector. OCP, as mentioned in part 2, is a prime example of this and highlights the importance of involving all players in the  implementation of an innovation ecosystem.

Thus, using best practices inspired by foreign countries could strengthen local ecosystems. These measures would be of such a nature as to enable the realisation of the innovation potential of a country like Morocco by promoting the rise of start-ups.

Development of fundraising between 2018 and 2019

Source: Partech (fundraising) and World Bank (GDP)

Si ces chiffres peuvent être utilisés à l’échelle macroéconomique pour mesurer des tendances telle que l’ouverture de certaines économies aux capitaux extérieurs, ils révèlent aussi l’état d’appropriation de certaines bonnes pratiques par les acteurs locaux. Dans ce contexte, les politiques publiques peuvent jouer le rôle de facilitateur dans une approche « top-down ». Néanmoins les réalités locales ne oivent pas être ignorées. En effet, au-delà des politiques publiques, ce sont les jeux d’acteurs et la qualité des interactions qui permettent la co-construction de programmes innovants et déterminent le dynamisme d’un secteur. L’exemple de l’OCP abordé dans la partie 2 est à e titre évocateur et souligne l’importance d’impliquer l’ensemble des acteurs dans la mise en place d’un écosystème d’innovation.

Ainsi, la diffusion des meilleures pratiques inspirées des pays étrangers pourrait renforcer les écosystèmes locaux. De telles mesures seraient de nature à permettre la réalisation du potentiel d’innovation de pays comme le Maroc, en favorisant l’essor des start-ups.

¹ Relationship between the number of existing support structures in the region and all existing support structures in Morocco

² L’économiste.com, Edition n°5032, 2017, ‘Fès-UEMF : Une université à la fine pointe de la technologie’

³ Partech, 2019 Africa Tech Venture Capital report, page 13 (released in January 2020)
Note on methodology: Partech, a venture capital fund, publishes the ranking of fundraising rounds in Africa annually
The start-ups taken into consideration must fulfil the following criteria: (i) the start-ups are tech and / or digital, (ii) their market is Africa (in both
operations and revenues) and (iii) the funds raised exceed USD 200,000

The 2020 results of banks in France largely support the long-term trends in retail banking in the country.

With this in mind, it should prove interesting to analyse the figures over the past five years to evaluate the impact of the breakdowns at work. This will help to understand the marked decrease in the relative weight of retail banking in the results of the six largest French banks, whether it makes up a significant proportion of their income (Mutualistes, Banque Postale) or a much smaller one (BNP Paribas, Société Générale).

The fall in revenues of around 1 % per annum for all banks combined is the main driver of these developments. Within net banking income (NBI), it is of course the interest margin that is declining, partly because of low interest rates, and partly because of commercial
practices.

This decrease in margin materialises in a much higher decrease in gross operating profit in retail banking, with a fall of 5 % per annum since 2014. As the cost of risk is decreasing significantly, however, the fall in net profit is limited.

In addition to this macroeconomic context, French retail banks are suffering from the specificities and practices of the French market.

Mortgages, for example, as the “harpoon” product of the customer relationship, have always generated particularly low margins for banks in France. This proved to be highly detrimental during the waves of early repayments from 2015 to 2018 and ended up costing the system several billion euros in NBI for individual gains in market share that were practically non-existent.

To compensate, the banks have all sought to expand their outstanding amounts: those linked to mortgages have increased by 28 % since 2014, passing from €833 billion to €1.071 trillion. Questioning the profitability of this choice is all the more pertinent given that French banks often use brokers for mortgages (40 % of volumes), despite some of the densest agency networks in Europe.

The other major supply of credit, consumer credit, generates structurally higher margins. The outstanding amounts are approximately five times smaller than for mortgages (€188 billion in September 2019), and the market is dominated by the specialised entities of BNP Paribas, Crédit Agricole and Crédit Mutuel (80 % market share between them).

But consumer credit has seen growth rates of 3 % per annum since 2014, and it forms a major part of the strategic plans of all banks. It regularly sees innovative new products, such as the recent split payments innovation, and competition is expected to grow in this business area in the years to come.

In terms of savings, the two regulated products, Livret A and PEL, which are specific to the French market (but correspond approximately to instant savings and home ownership savings accounts), represent over €540 billion in savings at the end of September 2019. The rates that they offer, however, are more of a hindrance than a help to French banks in the current economic context.

Indeed, home ownership savings represent a specific difficulty for retail banks, with its even higher interest rate. It has passed from 2.5 % to 1 % since 2014, but this has not stopped the outstanding amounts from climbing €60 billion over the same period, putting a further strain on the NBI of the banks that collect them, with an average rate of 2.65 %. Here again, the banks do not all follow the same policy.

Therefore, whilst the six main French banks suffer in varying degrees when it comes to retail banking, they do not all have the same strategies. Those banks with the strongest networks may be able to choose between products and volumes, but those with less extensive networks must further diversify and more closely monitor the profitability of each activity.

The symptoms may vary, but for banks to get back their profitability, the remedies that they must employ are probably the same: continue to better segment and personalise offers, and invest so as not to be left behind by neobanks in terms of customer experience. They might also choose a different way of functioning for mortgages. This already seems to be the case since the beginning of the year.

Article from Les Echos – 25 February 2020

SUPPORTING START-UPS
Read Episode 1: How do business support structures facilitate value creation?

IN SUMMARY

If innovation is a strategic issue both on a ‘macro’ scale in terms of national economies and on a ‘micro’ scale in terms of the businesses involved, then so is its financing. This financing relies heavily on business support structures.

In France, the first support structures aimed to provide an outlet for public research, but the rise of private structures has come hand in hand with a growing awareness of the need for profitability. As a result, though the number of business support structures continues growing, it is no longer uncommon to see some placed under compulsory liquidation (Ekito, 33 Entrepreneurs) or required to change course (Numa, Usine JO).

A viable business model is difficult to achieve when providing services only aimed at start-ups. Fundraising has therefore become an entirely separate source of revenue for business support structures, in a context of heightened competition between them. Moreover, a structure’s ability to support fundraising – measured by the number rounds undertaken as well as by the amounts raised – has become an indicator of performance, sometimes somewhat reductively.

At the same time, the way in which traditional fundraising works is being called into question by the rise of new mechanisms, such as crowdfunding or the use of blockchain and, more generally, by significant societal developments. It is important to assess both the potential and the limits of these new tools and means of financing, as well as the perspectives that they open up to financing players, first among which are the business support structures.

A. Business support structures play an important part in the financing of innovation in France.

B. Different support structure models offer different approaches when supporting fundraising.

C. Although continuously growing, traditional fundraising measures come with a number of significant limitations for start-ups.

D. On a global scale, the Initial Coin Offering (ICO) phenomenon represents an attempt to reinvent fundraising, calling into question in particular the role of traditional players such as investment funds and business support structures.

E. However, taking advantage of the limitations of ICOs, new practices are already coming to the fore; they present new opportunities for innovation players in France.

1. INNOVATION FINANCING IN FRANCE: THE ROLE OF BUSINESS SUPPORT STRUCTURES

A. The substantial growth of venture capital activities in recent years has considerably strengthened the role of business support structures

Venture capital activities – the financing of risky companies with strong potential – have grown significantly in France since 2015 (almost 30% per annum on average for fundraising over the period 2015–2019). In 2019, over €5 billion was invested in these types of operation.

This phenomenon should be viewed against the backdrop of the growing number of start-ups over the past decade, which has brought with it the development of the business support structure market. These business support structures have also seen their number skyrocket, and over 700 communes in France have at least one.

Business support structures are generally the first to assist companies in their fundraising. Indeed, 74% of total venture capital investments in France are used to finance incubated start-ups¹. Further, the incubation of a start-up by a business support structure maximises its chances of succeeding in its fundraising. This occurs because (i) the fact of being incubated sends a sign to potential investors and (ii) the business support structure makes it easier to connect young businesses with investors, in addition to providing them with the knowledge necessary to undertake such operations.

Start-up fundraising in France by year [€bn]

Sources: EY 2019 barometer of venture capital, Accuracy analyses

B. Essential support in advance of and throughout the fundraising process

Beyond purely belonging to an ecosystem – a source of value in itself for all stakeholders – there are three major benefits when an innovative project works with a business support structure: (i) administrative and financial support, (ii) human support necessary for the operational and strategic structuring of the company and (iii) the means to measure market response. This last benefit can help when reflecting on the business model, defining customer segments, undertaking targeted surveys or even developing a user-centred approach along the lines of design thinking. All these actions make it possible to gain traction commercially and to make progress towards a proof of concept. This is essential for the initial fundraising round to succeed and to open access to further private financing
at a later stage. The start-ups that succeed in their fundraising are, in a way, privileged, and the role of business support structures is fundamental well in advance of this stage. The diagram below, which displays the resources available for innovative businesses depending on their level of maturity, clearly shows the stakes surrounding support provided in advance. It shows in particular ‘Death Valley’, a tricky step where many startups fall and unfortunately do not get back up.

Whilst financing mechanisms for innovation are available in the technological and economic maturation phases, difficulties arise during the proof of concept phase and at the commercial launch. This is the moment when the start-up generally needs additional financial resources to boost its commercial traction but it also comes precisely when the start-up has used up all its equity funding. Not yet sufficiently desirable for private investors looking for commercial growth (and therefore waiting for proof that customers have validated the offer!), the start-up finds itself in danger of failing.

This state of affairs is all the more significant in non-metropolitan areas. Indeed, in such areas, private financial resources are less accessible, whilst the need is greater due to a lack of available technical skills (concentration of key profiles such as developers in large cities) and a smaller ecosystem (difficulty obtaining access to large customer accounts, industrial partners, financing specialists, etc.).

Detail of key steps in the creation and financing of a start-up

Therefore, the role of business support structures before fundraising is vital, particularly in non- metropolitan areas, to reduce the length of time spent in ‘Death Valley’ as much as possible. More precisely, this means delaying entry into this phase, all whilst anticipating coming out on the other side.
The support provided will therefore follow two complementary and interdependent axes (illustrated in the diagram above):

• The acceleration of technological and commercial maturation: the aim here is to guide the start-ups and give them the technical means necessary to realise their idea and bring it to market. The technical improvement and economic development of the project feed into each other in an iterative process centred on the customer. Ultimately, we get to the minimum viable product and an initial
commercial proof of concept.

• The administrative and financial engineering aiming to maximise leverage effects: to obtain the proof of concept, financial resources need to be anticipated, mobilised and optimised to meet technical, human and commercial needs. However, sometimes start-ups do not sufficiently understand the innovation financing chain, particularly the mechanisms offered by structures like Bpifrance or regional authorities, which are increasingly at ease with their economic jurisdiction. Beyond pure knowledge of these mechanisms, business support structures help start-ups to use them at the right time and to benefit from substantial experience in administrative engineering.

These two axes help to secure the start-ups’ development path and make them desirable to investors, whilst giving them more time and therefore greater negotiating power.

“Our incubator’s knowledge of innovation financing is a key factor in our current growth. It has enabled us to obtain proof of the technical and commercial relevance of our solution and, therefore, prepare our recent fundraising round of €1.5 million with greater peace of mind.”

Pierre Naccache
co-founder of Asystom

C. Differentiated approaches based on the business support structures

Business support structures offer a wide variety of sizes and models, with varying profiles and therefore approaches.

Private structures, public structures, university structures

There are three main families of support structures that can be identified: those financed primarily by public subsidies, split into public subsidies and university funding, and those where private capital dominates. The structures financed publicly can concentrate on a wider range of topics and support a larger number of start-ups; for structures supported by universities or private capital, their economic equation requires them to take a more narrow and selective position in order to generate profits.

The public or private nature of the support structures also has an influence on how investors perceive the support.

We have analysed the top 20 support structures in terms of the average amount raised and the number of fundraising rounds supported. They represent 39% (i.e. a cumulative amount of approximately €1,750m) of the total amount of funds raised by incubated start-ups in France.

It is interesting to note that public support structures (excluding universities) represent 35% of this fundraising in number of rounds. Further, two of them hold the top two spots (Agoranov and Bpifrance Le Hub – see chart below). This success can be explained by the fact that their presence in a particular operation sends a very positive signal to investors: their engagement is a sign of stability, in that they do not necessarily favour short-term profitability, but take into account economic development, the strengthening of local ecosystems or support for strategic sectors.

Structures that have supported the most start-ups towards fundraising

Source: Study on the fundraising activities referenced by Capital Finance over the period 31 March 2017–8 April 2019

The top three support structures in France in terms of fundraising

Note: Four grandes écoles incubators place in the top 20 (Drahi -X Novation Center, ParisTech Entrepreneurs, Incubateur HEC and ESSEC Ventures). This can be explained by the fact that these structures aim to develop and apply scientific innovations, but also by their extensive networks of alumni, particularly among the main financing players (investment funds, banks, business angels, administration).

Venture capital structures, large group structures

For private structures, two main types of business plan stand out: one based on the integration of a venture capital activity, the other organised around strong links with a large group.

In the first case, the structures support a small group of high-potential startups, in which they also acquire shares.

Undertaking subsequent fundraising rounds is therefore a necessary condition of profitability for these players.

In the second case, the support structures have more of a technology-monitoring role for the group to which they are attached. Through them, the group takes a stake (often a minority interest), aiming to create new product and service lines that fit the core business of the group or to counter the risk of potential disruptions.

Generalist structures, specialised structures

Generalist structures generate 44% of fundraising; the remainder is generated by support structures with specific sectoral positioning. The information and communication technology, health and energy sectors are particularly well represented, covering 21%, 8% and 7% of the number of specialised structures respectively. As for the average amounts raised by sector, food, energy, telecoms and chemicals cover
the most significant amounts. Note that the average of the foodtech sector is inflated by the record level of funds raised by Wynd (€72 million), a start-up supported by ShakeUpFactory, an accelerator specialised in foodtech².

Number of fundraising rounds by incubation sector

Source: Study on the fundraising activities referenced by Capital Finance over the period 31 March 2017–8 April 2019

More or less selective structures

A support structure’s ability to generate funds is not directly linked to the number of start-ups it supports – though the contrary could well be expected.

If we consider the 17 largest support structures in terms of number of fundraising rounds undertaken, they support on average 64 start-ups (49 excluding Wilco, which has supported over 300).

However, the three most ‘successful’ structures (Agoranov, Bpifrance le Hub and The Family) all support a smaller number (41, 55 and 17 respectively).

This reflects the varying degree of selectivity between support structures. In particular, an investment-fund-type structure like The Family will choose projects primarily based on their future ability to raise funds. Public structures like Agoranov and Bpi have selection criteria that include short-term fundraising prospects but also wider objectives, such as offering a commercial outlet for technologies developed in public research laboratories.

Number of start-ups supported and fundraising rounds undertaken by structure

Source: Study on the fundraising activities referenced by Capital Finance over the period 31 March 2017–8 April 2019

D. The limits of business support structures

The growth of the amounts invested in venture capital and the multiplication of business support structures hide an uneven situation in France, as well as the numerous inefficiencies that startups face.

First of all, though business support structures have certainly spread out across the country in recent years (particularly via the FrenchTech label), fundraising remains concentrated in Île-de-France. Start-ups in this area in 2019 represented 75%³ of the amounts collected in France and 97 in 10 of the most significant amounts raised. This can be explained by the fact that the majority of investment funds and business angels are based in Paris.

Further, as well-intentioned as the support provided may be, fundraising can be seen as a risk by entrepreneurs looking to retain control of the management of their company.

Finally, undertaking fundraising rounds remains a difficult task for start-ups: they have to invest significant human and financial resources in processes where the outcomes are unknown; they
often have to repeat the processes for each potential investor with no possible economies of scale; and they are often limited to a national target so as to reduce the number of physical meetings.

In this context, alternative modes of financing have started to emerge little by little, namely crowdfunding mechanisms (crowdfunding, crowdlending, crowdequity) and blockchain mechanisms
(Initial Coin Offering, Security Token Offering).

Number and value of fundraising rounds by region in France in 2019

Source: Accuracy and Eldorado analyses

2. THE INITIAL COIN OFFERING (ICO) PHENOMENON – THE WRONG ANSWER TO REAL PROBLEMS

A. The beginning of ICOs

The years 2017–2018 saw the rise of a new way of fundraising for start-ups: Initial Coin Offerings or ICOs, a play on the term IPO for Initial Public Offering. An ICO corresponds to the issue on the primary market of an asset (a token), of which the ownership and transactions are recorded on a blockchain⁴ ICOs quickly became an innovative means of raising funds for tech entrepreneurs.

In this process, tokens represent the future right of use of a service, with the token issue guaranteeing the financing. They almost equate to a voucher that can be resold on the secondary market. ICOs can therefore resemble crowdfunding, as the issue aims to finance a service that usually only exists at the fundraising stage of the project.

As soon as the tokens are issued, they can be exchanged directly on the secondary market. Their value is determined by the demand for the service that requires or results from their use. Investors therefore bet on the growing adoption of the service to maximise their return on investment. In addition, it is not rare for project owners to reserve a portion of the tokens issued for themselves in order to benefit from the success of their service.

How an ICO works

B. After seemingly replacing traditional financing means, ICOs are in great decline

After the very first ICO in July 2013 by the Omni project, these operations multiplied in parallel with a significant increase in the market capitalisation of cryptocurrencies, the primary investment vehicle for these operations.

Between September 2017 and November 2019, more than $29 billion was raised in the world by this type of operation, primarily at the end of 2017 and in the first half of 2018 (between September 2017 and December 2017, the capitalisation of this market quadrupled). It should be noted that in France, the number of these operations over the same period was more limited, with only 48 ICOs and $153.6 million raised⁵. However, this is not representative of the situation in the country, insofar as numerous entrepreneurs chose to organise their ICOs elsewhere⁶.

Whilst the success of this method of financing can be explained initially by a certain number of specific circumstances, such as the strong increase in the capitalisation of cryptocurrencies, other structural factors need to be considered in order to take the full measure of this phenomenon.

First, the relative efficiency of this fundraising, that is, the ratio between resources (both human and financial) mobilised by a start-up and the amounts collected is significant. For a small company, issuing tokens on the primary market is in theory less expensive than classic fundraising and makes it possible to access a greater number of investors. As for larger companies, an ICO is also less expensive than issuing regulated financial securities.

Breakdown of funds raised in France and across the world
in 2018 and 2019 by fundraising process

This mechanism also has the advantage of security, with the blockchain by its very nature unable to be hacked. Finally, it offers better liquidity because the securities can be easily exchanged, in contrast to a direct investment in a start-up, something that is far from liquid.

The strong growth of the amounts collected during ICOs could give the impression that they would become the indispensable fundraising tool for innovation. However, since the second half of 2018, the amounts collected, as well as the number of ICOs, are in net decline: $2bn per month on average between September 2017 and August 2018, against $0.36bn (i.e. five times less) between September 2018 and November 2019.

Such a slowdown can be explained in particular by the fall in the market capitalisation of cryptocurrencies. Indeed, the loss of value of cryptocurrencies has reduced investors’ available funds and has weakened the cash positions of companies that retained amounts raised in the form of cryptocurrencies. The inordinate exposure of some projects to price fluctuations has led to numerous bankruptcies, which has also highlighted the complete lack of protection for token-holders. The fall in the number of ICOs can also be explained by the numerous fraud schemes and scams that plague this type of operation, of which some have featured heavily in the media.

Despite this mixed balance sheet linked to a passing trend for cryptocurrencies, ICOs remain important for three reasons: (i) they represent an expression of mistrust towards historical players in fundraising (advisory services, traditional investors, and business support structures), (ii) they serve as a means of obtaining financing for those that could not obtain it elsewhere and (iii) they are attractive to those with a pronounced desire for innovation. It is based on these elements that a new generation of financing models is now being built.

Amounts collected by ICO, number of ICOs and market capitalisation of cryptocurrencies

Source: CoinSchedule, ICO Bench, Accuracy analyses

3. TOWARDS NEW HYBRID MODELS?

A. Initial Exchange Offering: a return to trusted third parties?

An Initial Exchange Offering (IEO) is an ICO undertaken directly on a cryptocurrency exchange platform (an exchange). In this process, the exchange plays the role of the trusted third party. By sorting through the projects and undertaking sufficient due diligence on them, the exchange guarantees investors that the project is serious and, in particular, that it has potential. Indeed, the exchanges⁷ have the skills necessary to value the projects and put their reputation on the line by accepting to list them – thus providing the projects with the liquidity that investors seek.

The IEO practice has been growing since the end of 2018: whilst the number of ICOs has been consistently falling since March 2018, the number of IEOs has been consistently rising since January of the same year.

This recentralisation is somewhat paradoxical in light of the decentralisation aims of blockchain, but it makes it possible to assuage the concerns of investors. By repurposing the principle of the ICOs, IEOs show that this innovative process can still be relevant for certain projects, whilst conserving the far from negligible advantages it has over traditional fundraising, provided that asymmetry of information between start-ups and investors is remedied. It highlights that only a thorough analysis of the business model, the addressable market and the benefit of blockchain technology can guarantee a project’s reasonable chances of success.

In this sense, the ICO experiment makes the case for the increasing involvement of trusted third parties such as advisory firms, support structures and, in the case of IEOs, exchanges.

Amounts collected by ICO / IEO and market capitalisation of cryptocurrencies

ICO vs IEO: development of amounts collected

Source: CoinSchedule, ICO Bench, Accuracy analyses

B. Security Token Offering: a hybrid model

A Security Token Offering (STO) corresponds to an issue on the financial securities primary market, represented by a token on a blockchain. Far from a simple voucher like in the context of ICOs or IEOs, a token here gives rise to the right to future revenues, either fixed or variable depending on their configuration. As the financial securities are subject to strict regulations, investors have potential recourse against ill-intentioned start-up entrepreneurs.

Blockchain here represents only the technological infrastructure on which the transactions are registered. However, it opens up wider perspectives in terms of the digitalisation of financial securities and beyond. The acquisition of a €5 million stake in Tokeny Solutions⁸, a Luxembourg start-up specialised in the “tokenisation”⁹ of financial assets, by stock market operator Euronext, follows this dynamic.

Indeed, blockchain characteristics make it possible to consider the lowcost securitisation of any type of asset, whether works of art, financial securities or real estate. The liquidity of these securities on the secondary market is not yet guaranteed due to the lack of exchange platforms with the necessary licences and sufficient volumes, but dozens of projects across the world are under development in this area.

Examples of STO projects in progress around the world

Blockchain also represents an opportunity to develop innovative financial securities, which for example allocate to investors a percentage of certain elements determined in advance, such as operating profit or profit before tax. Whilst this type of instrument was already possible before blockchain, the automation made possible by smart contracts¹⁰ changes the cost/benefit equation. This would mean generating wider interest among investors, in exchange for less involvement in governance.

Though the ICO phenomenon is losing momentum, business support structures can learn a great deal from it, despite first perceiving it as a threat. It has revealed the complexities and the excesses of ‘traditional’ fundraising and is an opportunity for the innovation financing ecosystem to reinvent itself with new hybrid mechanisms.

And it is not the only driving force. Indeed, the digitalisation of operations is growing with more and more services being performed remotely: support, events, conferences, fundraising, etc. This revolution in our way of working is leading to a greater number of structures with no physical locations and off-site support programmes.

In addition to adapting to this innovative way of working, business support structures will have to adapt to new market requirements: improve their ability to operate in non-metropolitan areas by mobilising relevant investors outside major cities or develop their mastery of cutting-edge technologies such as blockchain, which makes it possible to digitalise complex operations without jeopardising data security.
Between the search for profitability, new start-up needs (mobility, flexibility, transparency), developments in utility (crowdfunding) and technology (blockchain), and changes in their ecosystem (digitalisation, globalisation, decentralisation), business support structures need to be as innovative as their innovative clients!

Notes

¹ Accuracy study led between 31 March 2017 and 08 April 2019 on over 650 fundraising rounds undertaken by 380 incubated start-ups

² Wynd operates mostly but not exclusively in foodtech

³ Accuracy analyses

⁴ A blockchain is a distributed database that cannot be falsified. It can only be modified by increments. For further details, see the book: Blockchain – The key to unlocking the value chain, M. Della Chiesa, F. Hiault, C. Téqui (Eyrolles, 2019)

⁵ According to Accuracy analysis of CoinSchedule data
⁶ Les Echos, « ICO : les start-up tricolores boudent la France », 06/06/2018

⁷ The exchanges are the largest players in this new ecosystem; by their size alone, they give investors confidence

⁸ Les Echos Investir, “Euronext prend une participation de 23,5% dans la fintech Tokeny Solutions” (Euronext takes 23.5% stake in Tokeny
Solutions fintech), 01/07/2019
⁹ Tokenisation describes the act of creating a token on a blockchain thereby materialising the ownership of an element external to the blockchain – for example shares in financial assets

¹⁰ A smart contract is a transaction that is conditioned and programmed on a blockchain

SUPPORTING START-UPS
Read Episode 2: Business support structures looking for a new role and new models

IN SUMMARY

The unbridled rhythm of innovation, the risk of disruption, the volatility of clients and the dearth of talents. These are all factors pushing large groups to innovate not only quickly, but also efficiently. This innovation imperative requires, in particular, large companies and start-ups to come together.

However, large companies wanting to support start-ups is not enough to make the collaboration work. If innovation clearly constitutes a bridge linking the worlds of large groups and young businesses, its foundations can be weakened due to strategic objectives and ways of working that are structurally different.

Our mapping of business support structures in France makes it possible to understand the primary trends in the French innovation ecosystem, as well as the performance levers able to be used to support start-ups:

A. Large French groups are actively engaged in supporting start-ups. This is the case for 90% of CAC 40 companies, which have created their own structure, participated in multi-company schemes or joined existing structures.

B. Initially centred on the Parisian region, French innovation is developing rapidly outside the capital, with larger cities welcoming more and more support structures.

C. The trend is for structures specialised in the supporting entity’s sector(s) of activity: this is both a means of differentiation and a performance factor. It also enables the large group to integrate created value more easily into its own activity.

D. Five axes of reflection make it possible to define the most appropriate format of support for the large group’s strategic objectives. Four relate to the solutions provided: hosting, human, technical and financial resources; the fifth relates to the level of maturity of the start-up.

E. A third-party expert is essential to implementing the support strategy, but also its governance. It is a question of facilitating cooperation and maximising value creation between the parties involved, which may have extremely different cultures!

INTRODUCTION

Whether it is a matter of the heart or the head, the union between large groups and young start-ups today is vital to securing growth levers in a world under constant transformation.

But how can large groups get their bearings among the myriad possible support formats? From incubators to accelerators, via co-working spaces, company nurseries, fablabs and corporate venture capital, what criteria should a group choose to find the right support structure to meet its strategic objectives? Should it specialise in its own area of activity or stay generalist, ready to capture value wherever it can be found?

Accuracy has undertaken a mapping of French support structures to provide the necessary keys to fully understanding the ecosystems in place. This vision will make it possible to judge which third-party experts can provide assistance in finding a truly productive and profitable approach.

1. INNOVATION IS UNDERGOING A REVOLUTION!
HOW TO TAKE ADVANTAGE OF IT TO CREATE VALUE?

A. From absorption to support

In an ever more uncertain environment, innovation is no longer restricted to internal R&D investments, patent portfolio management and the integration of outsourced technologies. It is now closely linked to risk-taking, through investments in audacious projects: to stay in the race, companies have to bet on (more or less young) disruptive entrepreneurs.

The majority of large companies initially adopted a strategy of absorption. This was sometimes aggressive and destabilising for the entrepreneurs, and often inefficient in terms of innovation. However, previous failures and the appearance of new open innovation tools have boosted new practices. Today, 90% of large groups favour start-up support structures, either by creating their own or by sharing or delegating the management of it.

Management of support structures in which CAC 40 companies invest

For example, the Vinci group created its own structure, “Léonard”, which among other things, stimulates intrapreneurship. It is all at once a start-up incubator, a co-working space and a meeting place for actors in municipal/regional transformation. As for Airbus, it signed a partnership with the incubator Centrale Audencia ENSA Nantes. In addition to the services provided by the incubator itself, those working there have access to a dedicated space (technical showroom and co-working space) able to host their intrapreneurial projects.

Other companies prefer to ask a third-party expert to set up their support system. For instance, AstraZeneca asked a pure player, Interfaces, to create and then manage its “Realize” programme, which aims to innovate in terms of a patient’s journey, data management and scientific innovation in the field of oncology.

B. A more and more balanced regional network

Paris and the French desert? Not so fast… It is not surprising that the capital is the nerve centre of French innovation: it boasts 26% of existing structures, including the top performing ones and those receiving the most media attention. However, the other regions of France are not to be outdone: major regional cities are also giving themselves the means to play a role in the race for innovation.

Indeed, the French ecosystem has a network of support structures that is becoming more and more complete. More than 700 municipalities have at least one support structure, and all regions are seeing their number of structures increase.

Breakdown of support structures in France

Our quantified analysis makes it possible to take an inventory of the situation and to predict the future dynamics of each region. Ile-de-France shows an innovation support ecosystem that is already relatively mature, whilst the other regions, even those already well developed such as around Bordeaux and Toulouse, continue to show strong growth prospects.

In short, France’s innovation ecosystem is rather logically based on the economic dynamism of the different regions and seems to form a Sun Belt à la française, which starts in Rennes and descends all the way down to the Nice region, passing by Bordeaux, Toulouse and Montpellier.

C. Innovation ecosystems more and more specialised by sector

In this regionalisation of innovation, certain areas have chosen to rely on their economic history to create specialised channels by sector. But is it better to go generalist or specialist? The majority of large groups have had to make this decision, with each adopting the strategy that seems most relevant to its strategic and economic imperatives.

However, the fact is that specialisation is gaining ground. Themed platforms now make up a significant proportion of the support structures in France. This may be because, on the one hand, the added value of the support may be significantly larger, and on the other, companies generally seek benefits in their areas of activity. Moreover, specialisation is a differentiation factor in the face of increasing competition following the rise in the number of support structures in recent years.

The development of the banking sector illustrates this transformation perfectly. Since 2014, Crédit Agricole’s “Village by CA” has spread throughout France, in line with the presence of its regional head offices, and regardless of the sector of application. Its aim is to assist entrepreneurs by providing coaching, a potential network of business partners and mentoring by bank employees, in the hope that they then become suppliers or clients of the bank. All other large banks have followed suit, creating their support structures, but sometimes limiting them to their core business lines. For example, “Plateforme 58” by La Banque Postale, is active in banking and insurance, as well as in financial technologies, health, education and services. As for BNP Paribas with its acceleration programme “Bivwak!” (in addition to “WAI”), HSBC with “Lab innovation” and Société Générale with “Swave”, they concentrate on innovations that are applicable to the bank’s business lines, supporting fintechs and insurtechs.

In this specialisation trend, certain sectors seem more attractive than others. The graph below clearly shows the areas that are over-represented in the innovation ecosystem when looking at their market size. In all probability, the greentech, fintech, biotech, and agritech sectors, but also media and communication, will drive innovation for the next few years. Hence why it is important for companies to position themselves now to secure the creation of value tomorrow!

Investment by specialisation

2. HOW TO CHOOSE THE RIGHT FORMAT FOR START-UP SUPPORT AND MAXIMISE RETURN ON INVESTMENT?

A. What type of structure for what strategic objectives?

Even if 90% of CAC 40 companies have chosen to invest in at least one start-up support structure, the format used is not always appropriate to achieve their strategic objectives.

There are multiple types of support structures. The services offered vary, ranging from simple hosting services to the provision of machine tools for prototypes, access to mentoring or bespoke acceleration programmes, the organisation of networking events and also assistance with financing. So how should a large group choose the most appropriate format for its strategic objectives?

Of course, it should start by clarifying these objectives, which underpin its investment logic. Is its ambition to obtain a quick return on investment? To participate in the development of a region to make it more dynamic? To monitor technology closely in order to integrate any developments by the start-up as quickly as possible? To face human resources challenges through intrapreneurship, the recruitment of new talents, the employer brand or the sharing of new ways of working?

Defining these objectives make it possible in turn to define the type of start-up to target (in particular, in terms of maturity) as well as its associated needs (hosting, technical means, human resources, and financial means). The relative weighting of these five elements therefore determines the most relevant support structure, in light of both the strategic priorities of the large group and the actual needs of the start-up.

Indeed, the mapping below presents the different support ecosystems that exist in France, based on the relative weight of each of these five criteria.

Mapping of main support structures

By way of example, incubators are essentially aimed at communication objectives, HR issues and technology capture, their offer mostly includes hosting, coaching or mentoring, and is aimed more at start-ups. As for fablabs, the objective is less geared towards communication and more towards the development of talents and regions. For that reason, they tend to deal with more mature projects (often in their prototyping stage), for which a large group may supply significant technical means.

B. The thorny question of governance: a trusted third party to make alliances last

Once the structure has been identified and fully considered, the difference – as usual – resides in execution.

First, to attract the most promising start-ups, groups must ensure that they bring a differentiating factor to the table. It is for this reason that Univail-Rodamco-Westfield offers the opportunity to test innovations and business models in its shopping centres, whilst the highly active communication surrounding EDF Pulse provides a strong level of exposure.

Second, supporting start-ups is an investment project just like any other, and in this respect, it requires the rigorous monitoring of KPIs defined in advance. This performance steering, whether it be through strategic partnerships, equity investments or support programmes, raises the tricky question of how much independence is necessary to innovate. How can a large group implement a governance structure making it possible to provide support to the start-up but without suffocating it? Adapting internal processes so as not to stifle the start-up’s development with too much rigidity, involving top management to strengthen the legitimacy of the programme internally, communicating regularly but not intrusively… There are many different success factors, the implementation of which may require the presence of a trusted third party.

This third party can contribute to building a bespoke support programme and supervising it once in place, particularly in the case of multi-company structures such as “Plant 4.0”, which groups together Total, Vinci Energies, Solvay, Eiffage, Orano and Air Liquide.

The trusted third party must understand the advantages and disadvantages of each type of structure to create a bespoke programme that responds effectively to the large group’s strategic innovation challenges. But above all, it must be a bridge between the large group and the start-up: indeed, these actors each have differing strategic objectives, which only converge when it comes to innovation. Accuracy can be the trusted third party, acting to orchestrate, coordinate and optimise cooperation in this “shared space”.

¹ Relationship between the number of existing support structures in the region and the entirety o