Improving Loan Servicing in Microfinance: A Mobility-Network Approach

Abstract

Microfinance organizations have emerged as a lasting solution to the financial exclusion problem in several emerging economies. With a rapidly increasing penetration of mobile phones, and Internet services in these countries, organizations are now exploring novel sources of information for servicing micro-loans and assessing creditworthiness. In this paper, we emphasize the importance of leveraging the network of borrowers in predicting defaults for loan servicing and collection. Specifically, we propose a modeling technique that uses the mobility pattern of borrowers, following the loan approval, to create representation of co-located network among the borrowers. Through collaboration with a large consumer finance marketplace in China, we illustrate the statistical power of such network representations at predicting loan repayment behavior, for a selected sample of borrowers. Our results highlight a novel strategy for modeling credit risk, and improving loan servicing mechanisms, in a scalable and privacy-preserving manner.