Leveraging Machine Learning and Forecasting Techniques to Enhance Credit Risk Analysis and Prediction

This research presents a novel data-driven framework to improve credit risk assessment and minimize loan defaults. The framework leverages a combination of machine learning techniques, including Random Forest and XGBoost, to analyze a comprehensive dataset from Axis Bank spanning 2007-2015. The study focuses on key factors such as loan amount, debt-to-income ratio, interest rate, and debt burden to predict the likelihood of default. By employing oversampling techniques to address data imbalance and integrating ensemble methods, the framework enhances prediction accuracy. The results demonstrate the superior performance of the XGBoost model in accurately classifying loans as default or non-default. This research contributes to the advancement of credit risk management by providing a powerful tool for financial institutions to make informed lending decisions and mitigate financial risks.