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  • Writer's pictureRachid Mouchaouche

Are Fintechs paving the way to credit access for all ?

Updated: Sep 30, 2022

A recent deep dive into the Fintech ecosystem has led us to measure its scope and analyze its main drivers. If digital transformation affects all industries, those that rely on massive information processing are naturally the most impacted.

In a highly regulated market, dominated by large historical players with a strong inertia, the upheaval of the industry has taken its time. The evolution - the revolution? - is nevertheless underway: Fintechs evolving in the "Digital Lending" segment are taking over the space left vacant, before gradually challenging the big players on their historical playing ground.

In this regard, the year 2021 was the high point of the amounts invested in these Fintechs. With a growth of +200% compared to 2020 levels, these amounts have reached $21 billion worldwide.

Considered through the prism of the credit market on which we focused our analysis, the vacant space is already substantial, for example:

  • Where access to credit was based on simple and too rigid criteria, the finesse of artificial intelligence algorithms makes it possible to propose offers to previously excluded populations

  • Where granting credit used to be time-consuming, with multiple files and commissions, digitalization makes it possible to broaden the offer to provide almost instantaneous proposals - particularly through low- or no-regulation offers such as 'Buy Now Pay Later' (BNPL) - while controlling the risk level

The Fintechs and their risk assessment solutions

Fintech companies that enter the credit market in its broadest sense (every financing facility granted to individuals) invest market areas where traditional players are, if not absent, at least not historically present.

Indeed, these traditional players, such as banks and other credit institutions, have a culture and tools for assessing borrower risk that are adapted to "perfect" or "structured" information (marital status, salary records, tax returns, geostatistical data, etc.).

The barriers to entry in these markets are high and the players very powerful.

Fintechs enter the part of the credit market characterized by the so-called "imperfect" or "unstructured" borrower information (partial or costly to obtain for decision-making). Unless forced to do so, this market segment will naturally be less occupied by traditional players given the costs associated with the acquisition and the transformation of this information compared to the so-called "perfect" information they already have.

This imperfection of information on the borrower associated risk is rooted in several microeconomic observations:

  • The reluctance of certain demographic categories to disclose information that could qualify their credit risk (e.g., the case of the 'Millennials' in the United States)

  • The lack or absence of structured information regarding individuals who are not working, not employed or, on the contrary, who have a multitude of activities, diluting the relevance of the information sought

  • Insufficient credit history associated with past behavior that did not provide information on the borrower risk (e.g., the case of "well-off" young people who have not taken out any credit for capital goods purchases)

  • The partial incorporation of risk assessment information on individuals with low credit ratings but with additional income or financing capacities not identified by traditional channels

In addition to these findings associated with behavior, socio-economic status, and partial scope coverage, the information may also be imperfect due to the time constraints required to collect it (e.g., the case of BNPL).

Finally, borrowers may find themselves in a "gray zone" where the "structured" information held by traditional players makes them ineligible for credit or confronts them with rates that are overly high. These are the same borrowers for whom "unstructured" information ultimately allows them access to credit through a more holistic analysis of their repayment capacity.

In addition to enlarging the segments of consumers that can be reached, technology is also a factor in accelerating the processing of this information, making it possible to broaden the use cases for credit. The almost instantaneous assessment of a consumer's ability to repay a loan allows to make offers on the fly, whether online or in a physical store.

As a result, Fintech companies entering these markets need robust and scalable tools that allow them to operate the potential of this imperfect information to achieve an acceptable level of borrower risk; tools that can calculate and interpret risk from unstructured information.

Where one might expect a gradual replacement of historical tools and methods, the ability to extend the scope of application both in terms of consumer categories and possible use cases is now enabling these new players to develop rapidly. They don't have to wait for the endless and laborious change management implemented by these giants, paving the way for them to sell and integrate their more agile and inclusive tools.

What better way to shake up the famous "we've always done it this way" than to demonstrate through example the relevance of alternative approaches on a new playing field?


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