Design of a machine learning model for predicting credit risk in microfinance using environmental data

Abstract

Agricultural microfinance is a sector that is significantly impacted by climate-related risks, such as temperature fluctuation, soil degradation, and irregular rainfall. These environmental factors have not only impact on crop yield but also results in influencing borrowers’ ability to repay agricultural loans. Traditional credit scoring models lack in predicting due to the complex interplay between environmental and borrower-specific variables. This research study proposes a new predictive machine learning model based on XGBoost for assessing the credit risks in agricultural microfinance. This model utilizes environmental indicators, borrower characteristics, and loan attributes for computing the continuous credit risk score. The model was trained utilizing a real-world dataset of 142,017 loan applications with a 70/30 split. When compared with other traditional models, the results of the model showcases an accuracy of 99%, a recall of 84%, a precision of 89%, and an F1-score of 86%, outperforming traditional algorithms such as logistic regression and decision tree. This model has substantial implications for microfinance organizations. With this model, borrowers can evaluate risk accurately during the loan application stage by utilizing environmental data, resulting in better loan targeting, enhanced financial inclusion, and better risk mitigation for vulnerable farming communities in climate-sensitive regions.