WHAT'S MISSING IN LOAN ALGORITHMS

Last year, the rate of defaults on loans intermediated by financial technology platforms rose.

- PRASANNA TANTRI

AST year, the rate of defaults on loans intermediated by financial technology platforms rose. But more recent numbers show some signs that the cycle of boom and bust in fintech lending may be nearing an end.

I highlight how ignoring some basic economics exasperated this cycle and what lessons can be learned from it.

Let's first understand the broad structure of a typical fintech algorithm. It uses three types of data. The first is 'alternate data', types that a brick-and-mortar bank generally does not use—ranging from shopping details to the type of phone or operating system used. The second source is the banking and investment details of the borrower. Thanks to account aggregators, fintechs now have access to information such as savings account transactions, insurance and mutual fund investments, taxes, etc. These are data points that banks have access to, but may not be using optimally due to either technological or human resource constraints, or inertia. Finally, fintechs also use information about borrowers' past loan performance from the credit bureau, which all formal lenders use.

All these data points are fed into a machine-learning algorithm that's trained on past loan performance. In other words, the algorithm understands the relationship between all the input variables and past loan performance. Having understood the relationship, it can predict loan performance, given the input variables of new applications.