A Novel Ensemble-Deep Learning Approach for Accurate Credit Risk Prediction in Imbalanced Financial Datasets

Abstract

The accurate prediction of credit risk is a crucial endeavor in the banking and finance sector which is impeded by the complexity and inaccessibility of financial data. This paper will use the Australian and German Credit data, and the preprocessing methods that will be applied are the management of class imbalance through SMOTEENN and feature extraction to identify relevant features. Various machine learning and deep learning models such as Convolutional Neural Networks (CNN), Multi-Layer Perceptron (MLP), Random Forest, and Logistic Regression are analyzed and evaluated using different performance indicators such as accuracy, precision, recall, F1-score, sensitivity, specificity, and confusion matrices. The approach suggested is a Stacking Classifier, which has strong generalization and predictive power in both data sets. A Voting Classifier (which combines Bagging with RF and AdaBoost with DT) is used to increase the accuracy, therefore, increasing the resilience and the generalization as a whole. The Voting Classifier achieves 100 percent accuracy on the German dataset (original and resampled), 98.3 percent on the resampled Australian dataset and 89.1 percent on original Australian dataset. To understand the model predictions and determine the significance of features, explainable AI approaches like LIME and SHAP are applied, therefore, improving the transparency and reliability. The models and preprocessing artifacts are finally made available through a Flask web application, where users can make real-time credit risk predictions with help of interpretability