Learning by Doing: The Case of Online Lending

TL;DR

This paper explores optimal online lending strategies, comparing incremental experiments versus single grand experiments, and finds that the best approach depends on the control variables and income variability, with implications for profit and consumer segmentation.

Contribution

It introduces a framework for optimal sequential experimentation in online lending, analyzing when incremental or single experiments are preferable under different conditions.

Findings

Incremental experiments are optimal with exogenous interest rates.

Single grand experiments can be optimal when setting both interest rate and loan amount.

Income variability enhances experimentation effectiveness.

Abstract

Online lending, a phenomenon which is becoming mainstream due to the migration of consumer finance to the Internet and the adoption of AI based lending models, is an example of learning by doing. This paper studies optimal policies for a direct online lender. This is an instance of a more general problem: how should a decision-maker experiment sequentially in the face of unknown customer (or other) information? Conventional wisdom suggests the decision-maker should take advantage of sequential learning opportunities by conducting multiple small, lean experiments, each building incrementally on the results of earlier ones. Can a single grand experiment, uninformed by earlier experiments, do as well? We find that lean incremental experiments are optimal when the interest rate is exogenous. However, when we extend the lender's action space to setting both the interest rate and the loan amount, we find conditions under which a single grand experiment is optimal. In both cases, income variability can benefit the lender by enabling more effective experimentation. We also study the consumer segmentation associated with each strategy and show that the lender cannot achieve more than half the profit obtained under perfect information.