Reimagining Microfinance: A Smart Framework for Next-Gen Micro-Lending

Abstract

Sustainable microfinance, which offers savings, small loans, insurance, and credit to people without access to traditional financial intermediaries, is growing in developing countries. Due to non-performing loans, many MFIs face financial risk and loan losses to balance economic survival and benefit to the unserved and low-income people. These loans are given using poor decision-making, which may damage investors on borrowers' investments.  This study analyzes credit risk mitigation techniques and creates a cutting-edge machine learning framework to revolutionize microfinance credit evaluation for MFI sustainability and profit maximization. Our machine learning statistical model employing decision trees, linear regression, and logistic regression will simplify and communicate creditworthiness assessment information, unlike current risk assessment approaches. Advanced credit limit optimization is used in the model. The research uses expert linear and quadratic programming to reduce risk and lend. Optimization and machine learning improve sustainable microlending in our resilient, adaptable technique. This research analyzes current data to show that the suggested machine learning technique may identify and reduce microfinance dangers. The study shows that machine learning can revolutionize microfinance credit rating and risk assessment.