Evaluating Financial Risk Management Practices in Microfinance Institutions Based on Borrower Profiles and Loan Repayment Performance Trends

Abstract

Microfinance Institutions (MFIs) have been the focus of financial inclusion, particularly in emerging markets. The sustainability of MFIs is, however, under threat from the growing sophistication of financial risk arising from borrower heterogeneity and stochastic loan repayment behavior. This paper presents mathematically informed analysis of financial risk management protocols in MFIs via borrower profile modeling and analysis of historical repayment patterns based on real loan performance data. The research integrates statistical classification techniques and risk estimation models to estimate default probabilities of credit, categorize borrower risk classes, and portfolio vulnerability analysis. Logistic regression and credit scoring models based on verified data obtained from the MixMarket and the Microfinance Information Exchange (MIX) are employed to quantify risk exposure and repayment predictability for MFI borrowers. The results single out predictive variables—e.g., income regularity, loan tenor, and area—especially affecting repayment behavior. Empirical estimates validate that whendemographic-experiential borrower stratification is included in models, the prediction of repayment outcomes is improved significantly. This research contributes a validated and replicable methodology to help MFIs redevelop risk measurement frameworks to improve financial solidity and limit default rates. The contemplated model not only enhances credit allocation policy but also makes possible enhanced financial management with mathematically-directed borrower analysis.