用时序模型提升房贷违约预测，效果优于传统方法。
Transforming Credit Risk Analysis: A Time-Series-Driven ResE-BiLSTM Framework for Post-Loan Default Detection
- 结合残差与双向LSTM，滑动窗口捕捉贷款时序异常。
- 在44个独立数据集上，准确率、召回率等指标均领先基准模型。
- 通过SHAP分析可解释关键影响因素，适合金融风控场景。
贷款后违约预测是信用风险管理的重要任务，可通过机器学习检测财务异常来实现。本研究提出一种基于滑动窗口的ResE-BiLSTM模型，在涵盖44个独立样本的弗里蒙特·麦克房贷数据集上进行评估。该模型与LSTM、BiLSTM、GRU、CNN和RNN五种基线模型在准确率、精确率、召回率、F1值和AUC等多个指标上进行了对比。通过消融实验分析了模型各组件的贡献，并利用SHAP方法解析了模型决策所依赖的关键特征。实验结果表明，ResE-BiLSTM在各项指标上均显著优于基线模型，验证了其在真实场景中的实用价值。
原文摘要 · Abstract (English)
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.
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