A Study on Credit Default Prediction Using Hybrid AI Models Combining Neural Architectures and Econometric Features

Credit default prediction plays a vital role in risk management, lending strategies, and financial stability. While traditional econometric models offer interpretability, they often lack the predictive power of contemporary neural networks. This study proposes a novel hybrid approach that integrates deep neural network (DNN) architectures with key econometric indicators to improve the prediction of credit default risk. We develop and evaluate several model configurations, including a hybrid Long Short-Term Memory (LSTM) and logistic regression framework, on publicly available credit datasets. The results show that hybrid models outperform standalone econometric or deep learning models in terms of accuracy, AUC-ROC, and F1-score. The study also explores feature importance to enhance model explainability. Our findings underscore the potential of combining statistical and AI methodologies for more accurate and interpretable financial risk assessments