Sharing Credit Data While Respecting Privacy—A Digital Platform for Fairer Financing of MSMEs
Pooling credit information enables the construction of a more informative credit model for MSMEs, which can then serve as a common good among lenders.
Lending institutions’ reluctance to lend to MSMEs or to offer them competitive interest rates stems from the relatively costly information acquisition for small loans. The central idea is to bridge the information gap between the demand and the supply side by creating a credit analytics sharing infrastructure through federated learning, which completely respects data privacy. Pooling credit information across multiple lending institutions, particularly rare default events, enables the construction of a more informative credit model for MSMEs, which can then serve as a common good among lenders. The technology also allows for lender-specific models, which in essence share the model’s parameters on the common prediction variables while differing in their respective alternative data fields. The lenders in the MSME space can work like a coopetition and continue to compete with their varying risk appetites, loan rates, and banking services. We use real MSME credit data to demonstrate the feasibility of the sharing technology and to study the impact of the COVID-19 pandemic via a portfolio that we assembled from four hypothetical banks operating in six ASEAN countries.
WORKING PAPER NO: 1280