Three population covariate shift for mobile phone-based credit scoring

Abstract

Mobile money platforms are gaining traction across developing markets as a convenient way of sending and receiving money over mobile phones. Recent joint collaborations between banks and mobile-network operators leverage a customer’s past mobile phone transactions in order to create a credit score for the individual. In this work, we address the problem of launching a mobile-phone based credit scoring system in a new market without the marginal distribution of features of borrowers in the new market. This challenge rules out traditional transfer learning approaches such as a direct covariate shift. We apply a market-based re-weighting scheme of Original Market Borrowers that accounts for the differences in the original and new markets. The goal of applying this generalized covariate shift to three populations is to understand the repayment behavior of a fourth: New Market Borrowers who will self-select into a loan product when it becomes available. To test the approach we use real-world data sets from two Sub-Saharan countries in Africa consisting of 200,000 customers’ telephone records. Our results demonstrate that the market-based re-weighting scheme improves the credit scoring model in the new market compared to other more direct methods.