Machine Learning-Based Credit Risk Assessment for Predicting Loan Defaulters in Ethiopian Banking industry

Abstract Machine Learning is an AI technique, empowers organizations globally to gain insights from their data and achieve success. This study focuses on improving risk management of banking in Ethiopia by employing machine learning techniques to detect and predict loan defaulters, ultimately mitigating bad loans. The research aims to distinguish borrowers who repay loans promptly from those who don't, forecast potential defaults, and assess the creditworthiness of prospective customers without overhauling existing systems and data. To showcase the study's results, classification models are constructed to reveal significant patterns among customer attributes. The primary objective is to apply cutting-edge machine learning algorithms to classify bank loan defaulters. Several machine learning algorithms, including Random Forest, Decision Tree, Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP), are employed in the process. Various data split ratios, such as 70/30, 60/40, and 90/10, are explored, alongside the 80/20 data split, to build classification models for each classifier algorithm. Based on the experiment's outcomes, the 70/30 split is selected for its superior accuracy. The study evaluates the performance of these algorithms using metrics like accuracy, precision, recall, and F1-score. The results indicate that the Gradient Boosting classification technique surpasses other algorithms, achieving a training accuracy of 98.7% and a testing accuracy of 97.8%.