A Machine Learning and Deep Learning Approach to Predicting Loan Default Through Credit Risk Analysis

Abstract:

Loan default risk is a significant issue for financial institutions across the globe. It directly affects the profitability and financial soundness of financial institutions. The timely and accurate detection of potential defaulters is essential for efficient credit risk management. This paper presents an intelligent credit risk forecasting system that combines conventional machine learning classifiers with the most recent advancements in deep learning models. The data set includes past credit, financial, and demographic information gathered from previous loan applications. Various preprocessing methods, feature engineering solutions, and class imbalance problems solved using the Synthetic Minority Over-sampling Technique.(SMOTE) are used as techniques to enhance the quality of the data as well as the robustness of the models. Various predictive models, such as Logistic Regression, Random Forest, XGBoost, and the TabNet deep learning model, are compared. In addition, ensemble learning techniques are also used to mitigate misclassification and improve the generalization of the models. The experimental outcome shows that the TabNet and XGBoost models have the highest recall and accuracy in predicting default instances, thus minimizing the occurrence of false negatives. The hybrid model combines the interpretability of traditional machine learning models with the representation learning ability of deep learning models, providing a robust solution for real-time credit risk evaluation in the current banking system.

Keywords — Loan Default Prediction; Credit Risk Analytics; Machine Learning (ML); Deep Learning (DL); XGBoost; TabNet; SMOTE; Ensemble Learning; Financial Risk Management; Class Imbalance Handling; Predictive Modeling.