Effect of ai-driven fintech solution on credit risk management in micro finance institutes

Abstract

Bangladesh has one of the world’s largest and most developed microfinance markets, with institutions serving more than 30 million customers. Despite this growth, credit risk remains a persistent challenge due to the reliance on informal data, manual risk assessments, and limited predictive tools. This study examines the impact of AI-driven FinTech solutions on credit risk management in selected microfinance institutions (MFIs) in Bangladesh. Specifically, it investigates whether AI-based technologies improve borrower evaluation, reduce default rates, and enhance portfolio sustainability. A mixed-methods approach was employed, combining secondary financial data with survey responses collected from three MFIs. Regression analysis, paired *t*-tests, and descriptive statistics were used to evaluate the impact of AI adoption on key credit risk indicators. The findings highlight the importance of investing in AI-powered technologies to strengthen credit risk management. By providing empirical evidence from an emerging economy, the study contributes to the growing literature on FinTech adoption and demonstrates the transformative potential of artificial intelligence in improving credit risk management within the microfinance sector.