Learning to Bid the Interest Rate in Online Unsecured Personal Loans

摘要

The unsecured personal loan (UPL) market is a multi-billion dollar market where numerous financial institutions compete. Due to the development of online banking, loan applicants start to compare numerous loan products. They aim for high loan limits and low interest rates. Since loan applicants have a desired loan amount, institutions instead focus on adjusting interest rates. Despite the importance of determining optimal interest strategies, institutions have traditionally relied on heuristic methods by human experts to set interest rates. This is done by adding a target return on assets (ROA) to the applicant's expected default probability predicted by a credit scoring system (CSS) such as the FICO score. We conceptualize the UPL market dynamics as a repeated auction scenario, where loan applicants (akin to sellers) seek the lowest interest rates, while financial institutions (akin to bidders) aim to maximize profits through higher interest rates. To the best of our knowledge, this is the first time anyone has approached the UPL market through the viewpoint of a repeated auction. While there are several research done in learning to bid in repeated auctions, those works cannot be directly applied to the UPL market due to the lack of any feedback about other bidders' strategies and the need to satisfy the bidder's target loan volume and profit variance. We present an algorithm named AutoInterest, which is a modification of the dual gradient descent algorithm. In addition, we provide a framework to evaluate interest rate bidding strategies on a benchmark dataset and the credit bureau dataset of actual loan applicants in South Korea. We evaluate AutoInterest on this framework and show higher cumulative profit compared to other common online algorithms and the current fixed strategy used by real institutions.