Responsible Machine Learning for Credit Scoring in Microfinance: Balancing Repayment Risk and Financial Inclusion

Abstract

The microfinance institutions are facing issues when assessing the risks of repayments and keeping equal access to financing for all potential borrowers. Traditional methods of assessing risks based on credit scoring do not include those borrowers who lack formal financial records. Machine learning is a chance to enhance risk assessments; however, biases in the data and lack of transparency may contribute to the problem of financial exclusion. The present research investigates the use of responsible machine learning to balance repayment risks and financial inclusion. Comparative analysis is performed using logistic regression, decision trees, random forests, and gradient boosting. Model evaluation takes into account predictive performance, fairness, loan approval, and explainability. The results show that the accuracy of gradient boosting is 86%, while the approval rate is 74%. Accuracy of random forest is 84%, while its approval rate is 72%.The suggested methodology encompasses data pre-processing, risk prediction, fairness analysis, explainability, human-in-the-loop, and monitoring. Nevertheless, the numerical findings are demonstrative and need to be validated with actual microfinance data sets. Therefore, the numerical findings provide an illustration of a proposed approach for evaluation but not empirical evidence of the superiority of the models. Responsible application would ensure that the predictive accuracy, fair lending decisions, interpretability, and affordability are considered together.