Predicting of Credit Risk Using Machine Learning Algorithms

Credit risk management is one of the key processes for banks and is crucial to ensuring the bank’s stability and success. However, due to the need for more rigid forecasting models with strong mapping abilities, credit risk prediction has become challenging for the banking industry. Therefore, this paper attempts to predict commercial banks’ credit risk (CR) by using various machine learning algorithms. Machine learning algorithms, namely linear regression, KNN, SVR, DT, RF, XGB, and MLP, are compared with and without feature selection and feature extraction techniques to examine their prediction capabilities. Various determinants of credit risk (features) have been extracted to predict credit risk, and these features have been used to train machine learning models. Findings revealed that the decision tree algorithm had the highest performance, with the lowest mean absolute error (MSE) value of 0.1637 and the lowest root mean squared error (RMSE) value of 0.2158.