On Business Model Risk in Fintech Credit Platforms

Recent financial innovations and the development of digital technologies have considerably changed the landscape and the business models of the finance industry. Hence, the development of Fintech, defined by the Financial Stability Board (2017) as “technologically enabled financial innovation that could result in new business models, applications, processes or products with an associated material effect on financial markets and institutions and the provision of financial services”, might have major impacts on the sector of credit institutions, a process analyzed by Stulz (2019). The emergency of FinTech credit institutions thus poses specific types of risks that can only be understood by analyzing the different kinds of business models involved.  

To analyze Business Model Risk in more detail, it might be useful to analyze the drivers of fintech credit. The degree of competition in credit markets seems to play a major role (Claessens et al. (2018)). A less competitive banking system with higher credit margins can lead to alternative sources of credit. Moreover, if platforms have advantages in assessing borrower risks, those platforms might develop in markets where the asymmetry of information between creditors and borrowers is higher.  

The regulatory environment also is of foremost importance as it might create confidence in the new Fintech structures but also lead to regulatory arbitrage. It might be worthwhile to highlight that a typical platform does not bear credit risk or liquidity risk, as it is the case for standard credit institutions. Too much regulation might also stifle innovation. In some countries, for instance, the regulators have introduced new regulations and license regimes, implying that platforms partner with banks to originate loans. The recent low yield environment has led to a search for higher yields, eventually leading to a shift in the investment base with more institutional investors involved. This implies that the business models need to adapt to asset managers’ asset allocation strategies, eventually by structuring tailor-made products.  

Before analyzing the platforms in more detail, it is worthwhile to highlight that there are different ways to match borrowers and lenders (Omarini (2018)). In the diffused model approach, the platforms take an active role in selecting loans and matching borrowers with lenders. Plat forms allocate funds by diversifying loans among several borrowers. The platform thus has a higher probability that every borrower gets his request of funds. In a sense, there is some “asset allocation” activity of the platform involved. In the direct model approach, investors directly select loans according to provided information.  

The way the interest rate is determined might also differ. Milne and Parboreeah (2016) distinguish two alternatives: reverse auction and automatic matching. In a reverse auction, lenders disclose their minimum interest rates and borrowers their maximum rates. The matching takes place when there is correspondence. Note that the matching engine and procedure might be of foremost importance and might imply a risk of operational misallocation of risks. With automatic matching, the platforms set interest rates and select loans according to the lenders’ risk profiles.  

We can now focus on the different business models with eventually different matching engines and procedures.  

The traditional model is also called client segregated account model. The platform matches borrowers and lenders, typically with an automated or manual reverse auction procedure. The amount of collected money is kept as collateral in segregated accounts. For platforms that use the notary model, the platform only acts as a broker between lenders and borrowers. A partnering bank originates the loans and sells them to investors. In the guaranteed re-turn model, the platforms collect funds with a guaranteed interest that depends on the credit risk features of the loans.  

Two approaches exist. First, a pre-screening procedure is used and the loans are disclosed on a website to be selected by lenders. Second, there is an algorithm that automatically invests with compensation being a function of risk-return characteristics and loan duration. Finally, the balance sheet model, implies that platforms keep loans on their balance sheets. Those loans are sold to institutional and/or retail investors. Investors typically bear the credit risk. It is interesting to note that such an approach would enable the platforms to create some structured products.  

The different business models might imply different agency problems that can be analyzed through the lens of principal-agent models in finance and the recent subprime crisis’ business model experience. It can be expected that the higher the level of sophistication of the business model, the higher the risk of principal-agent problems. The traditional model can thus be taken as a benchmark to analyze the agency problems implicit in more advanced business models.  

Let us consider the notary model where loans are originated by a fronting bank, but the bank could sell the loans to institutional investors with eventual securitization involved. From an analytical viewpoint, this model bears resemblance to the originate to distribute approach and can be analyzed through this theoretical framework. It is, however, important to distinguish whether the bank is involved because of regulatory constraints or not. Moreover, from an asymmetry of information and agency point of view, it is important to highlight that the bank and the platform are involved in credit analysis and investment information.  

It is thus important to understand why and how the banks’ and the platforms’ information sets differ. It is also important to highlight that in some countries the structure that is emitting the loans, the loan originator, is not a bank but a micro-credit institution. This micro-credit institution is not regulated like a bank, which might also have corporate finance governance implications. Moreover, it is important to check the ownership structures as it might imply conflicts of interest.  

The guaranteed return model seems less complex at first sight. The platform acts as an intermediary between borrowers and lenders and provides credit risk assessment as well as investment performance assessments. Borrowers are supposed to pay a guaranteed fee and lenders are promised a guaranteed return. The CGFS FSB (2017) classification does not indicate how funds are used and which method, diffused versus direct, is used in the matching process. It is important to check and understand the matching process and engine as this might imply operational risks. Moreover, if a diffused allocation model is used, it is important to analyze how the funds are used.  

For instance, Trustbuddy, a Swedish platform went bankrupt because of misallocation of funds. Finally, how is the return formally guaranteed? With derivative products or insurance that hedge risks and limit the probability of bankruptcy or by just announcing a guaranteed return.  

The balance sheet model is obviously the most complex in terms of potential business model risks. The lending platform, given its credit analysis, provides loans but those loans are kept on a balance sheet. This balance sheet platform can then sell those loans to retail and institutional investors. The investment relationship can be implemented through a securitization platform. Those types of business models look suspiciously as an originate-to-distribute model with all the agency problems that come with it. 

As suggested by Vallee and Zeng (2019), the analysis and understanding of the different platforms’ information and risk bucketing approaches are of foremost importance to protect non-sophisticated investors from exploitation by sophisticated investors and platforms.  

Dr. Michel VERLAINE
ICN Business School  
Michel.verlaine@icn-artem.com 

Janis EISMONTS 
CEO of Sneakypeer 
janis@sneakypeer.com
www.sneakypeer.com 

References 
Claessens, St., Frost, J., Turner, G. and Zhu, F. (2018) “Fintech credit markets around the world: size, drivers and policy issues”, BIS Quarterly Review.  Committee on the Global Financial System and Financial Stability Board (2017) “FinTech credit: market structure, business models and financial stability implications”, CGFS papers May. Milne, A. and Parboreeah, P. (2016) The business models and economics of Peer-to-Peer lending, Euro Credit Res Inst. Omarini, E. (2018) “Peer-to-Peer Lending: Business Model Analysis and the Platform Dilemma”, International Journal of Finance, Economics and Trade.  Stulz, R. (2019) “FinTech, BigTech and the future of Banks”, NBER Working Paper Series. Vallee, B. and Zeng, Y. (2019) “Marketplace Lending: A New Banking paradigm?”. The Review of Financial Studies, 32(5).