Loan Default Prediction in Microfinance Group Lending with Machine Learning

Abstract

Microfinance fintech enables the unbanked and underbanked communities to access credit by offering small, no collateral loans. Microfinance institutions (MFI) usually use credit scoring to filter out risky borrowers. Credit scoring method for individual loans has been widely studied. However, none are for group lending where members are women micro-entrepreneurs in a developing country, and jointly responsible for loan repayment. This research try to build a credit default prediction model for microfinance group lending using machine learning techniques. We examine six different machine learning methods, including XGBoost, logistic regression, linear discriminant analysis (LDA), decision trees, k-nearest neighbour (KNN) and random forest. The XGBoost model performs the best during the first modeling phase. With an accuracy of 0.97 and an AUC score of 0.85, it performs better than other models. Decision tree and random forest give comparable outcomes, with AUCs of 0.81 and 0.80 and accuracies of 0.81, 0.95, and 0.97. In an effort to increase performance, class balancing is performed. The XGBoost model's performance was successfully enhanced, resulting in an increase in AUC from 0.85 to 0.89. Its accuracy stays the same as 0.97. False positive and false negative rates for this model are both low (2.05% and 1.38%, respectively). Consequently, the model has been effectively developed and is capable of differentiating between bad and good loans.