The Machine Learning Methods For Micro-Credit Scoring: The Case Of Micro-Financing In Mongolia

Abstract

As a result of growing digital technologies in the financial sector, the traditional slow lending process is being replaced by fast and easy digital lending systems that can make decisions in real time. Both lenders and borrowers have experienced the benefits of digital lending, the activities of microfinance institutions have expanded rapidly and the volume of digital microloans has increased significantly worldwide, including Mongolia. At the same time with the growing volume of digital microloans in Mongolia, the rationality of credit risk management has been becoming more critical. Credit quality is the most important factor in optimal credit risk management. It depends on determining the customer's creditworthiness and making accurate credit decisions. This research focuses on a credit scoring system to improve the digital loan evaluation system of the Mongolian microfinance institute. This study aims to contribute to the development of possible credit scoring systems for Mongolian microfinance institutions by comparing several machine-learning approaches based on loan datasets of a non-banking microfinance institute in Mongolia. The result shows the ensemble methods Random Forest and XGBoost Tree's accuracies are higher than other machine learning models for the microloan borrowers' repayment status prediction.