Promoting Financial Inclusion by Optimising Financial Interest Rates Based on Artificial Intelligence in Microfinance Institutions

Abstract

In recent years, the financial sustainability and survival of microfinance institutions (MFIs) have been seriously threatened by factors such as the reduction in donations, cooperation funds and international aid, and increased competition from commercial banks. Faced with this hostile scenario, which may limit access to credit for disadvantaged groups, MFIs must apply techniques to improve their efficiency, viability, lending capacity and survival. The objective of this study is to design a microcredit pricing model based on the Internal Ratings-Based approach, Basel III and probability of default to enhance access to credit for disadvantaged groups. We analysed a sample of 4550 microcredit transactions and 30 influential variables (25 idiosyncratic and 5 systemic). Our empirical results reveal that the IRB system is more equitable for borrowers and more efficient for MFIs, as it allows lower interest rates to be applied to borrowers with better credit histories. The application of the proposed IRB model can improve the sustainability, competitiveness and viability of MFIs by promoting operational efficiency and reducing default rates, thus contributing to financial inclusion by increasing supply.