Revolutionizing risk management in banking: Implementation of AI/ML-based gradient boosting machines (GBM) and random forest models for credit risk management

Abstract

This paper is aimed at explaining the Gradient Boosting Machine (GBM) and Random Forest model's role in the banking industry's credit risk management. Starting with collecting and cleaning the required data, which entails demographic data, financial information, loan details, and economic indicators, the report explains the training and assessment of gradient boosting machine (GBM) and random forest models. Measures like accuracy, precision, recall, F1-score, and area under the ROC curve are employed to validate the efficiency of a model. After that, the practical implications of using GBM and Random Forest models in a banking operation are inspected regarding decision-making process improvements, fewer defaults, and higher banking profit.