Machine Learning Methods for Credit Risk Assessment in Microfinance: A Review

Abstract

Microfinance institutions lend small amounts to borrowers who usually have no formal credit history, and they have long relied on group guarantees and the judgement of loan officers to manage the risk of default. As loan books grow and more data is captured digitally, statistical and machine learning models are increasingly used to support these decisions. This review examines the methods that have been applied to credit risk assessment, from logistic regression and discriminant analysis to decision trees, random forests, gradient boosting and neural networks, and considers how well they suit the conditions of microfinance. It also discusses three practical issues that often decide whether a model is useful: the imbalance between repaid and defaulted loans, the choice of evaluation measures, and the need to explain decisions to borrowers, loan officers and regulators. The literature shows that ensemble methods usually rank borrowers more accurately than single models, but the gains are often modest, and they come at a cost in transparency. For Indian microfinance and self-help group lending, the review argues that a well-specified logistic regression should remain the baseline, that tree ensembles are worth adopting where data volumes allow, and that explanation tools and careful validation matter as much as the choice of algorithm. Gaps include the scarcity of public microfinance datasets, limited use of group-level information, and few studies from India.