Transforming Credit Risk Analysis: A Time-Series-Driven ResE-BiLSTM Framework for Post-Loan Default Detection

2025/08/01 by Yue Yang, Yuxiang Lin, Yang, Yue +11
Business, Management and Accounting · Computer Science · Economics, Econometrics and Finance · #Credit Risk and Financial Regulations #FOS: Computer and information sciences #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2508.00415

openalex publication_date 2025/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Prediction of post-loan default is an important task in credit risk management, and can be addressed by detection of financial anomalies using machine learning. This study introduces a ResE-BiLSTM model, using a sliding window technique, and is evaluated on 44 independent cohorts from the extensive Freddie Mac US mortgage dataset, to improve prediction performance. The ResE-BiLSTM is compared with five baseline models: Long Short-Term Memory (LSTM), BiLSTM, Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), across multiple metrics, including Accuracy, Precision, Recall, F1, and AUC. An ablation study was conducted to evaluate the contribution of individual components in the ResE-BiLSTM architecture. Additionally, SHAP analysis was employed to interpret the underlying features the model relied upon for its predictions. Experimental results demonstrate that ResE-BiLSTM achieves superior predictive performance compared to baseline models, underscoring its practical value and applicability in real-world scenarios.

Citations

• Are Self-Attentions Effective for Time Series Forecasting?
• Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction
• A Survey on Explainable Anomaly Detection
• Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique
• A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM
• Financial series prediction using Attention LSTM
• An LSTM Network for Highway Trajectory Prediction
• Attention Is All You Need
• A Unified Approach to Interpreting Model Predictions
• Layer Normalization
• Bidirectional recurrent neural networks

Related

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• A machine learning workflow to address credit default prediction
• A comparative analysis of machine learning algorithms for predicting probabilities of default
• Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction
• A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios