Artificial Intelligence, Operational Efficiency, and Credit Risk in Microfinance Institutions

Abstract

Bangladesh boasts one of the world's biggest and most developed microfinance markets, with institutions providing services to more than 30 million customers. However, credit risk remains a persistent challenge due to informal data, manual risk assessments, and limited predictive tools. The effect of AI-Driven FinTech solutions on credit risk management in a subset of Bangladeshi microfinance institutions (MFIs) is examined in this study. The study investigates whether AI-based technologies increase portfolio sustainability, lower default rates, and improve borrower evaluation. Survey data from three MFIs (ASA, BRAC, and Grameen Bank) were taken during 2024-2025 from retail borrowers. Regression analysis, paired t-tests, and descriptive statistics were used to assess how AI deployment affected credit risk indicators. The results emphasize the necessity for MFIs to make investments in AI-powered technology. By adding concrete data from an emerging economy, this study enhances the body of knowledge on FinTech adoption and shows how AI is revolutionizing microfinance credit risk management.