DTD-VAE：用于信用风险预测的解耦时间依赖变分自编码器

中文摘要 AI 辅助

评估客户信用价值对零售银行业务至关重要，其影响营销策略、客户关系管理及信用风险控制。传统方法难以捕捉复杂的时间依赖关系，也无法从客户数据中提取用于准确风险评估的关键信息，具体而言，它们无法区分指示信用风险的时间模式与反映客户一般行为或偏好的模式，导致风险预测效果不佳。本研究提出了Disentangled Temporal Dependencies Variational Autoencoder（DTD-VAE），这是对传统变分自编码器（VAE）的改进，旨在解耦时间依赖关系并区分与信用风险相关的特征和过去的客户偏好。DTD-VAE的特征推理模块融入了自回归时间依赖学习机制，能够熟练捕捉潜在变量间的时间依赖关系，增强模型对固有数据结构的理解；特征生成模块则采用逐元素门控机制，为专家模型的每个维度分配独立权重，实现潜在变量（尤其是与信用风险预测相关的变量）更精细的解耦。在六个真实数据集上开展的大量实验表明，该框架始终优于现有方法，在ROC-AUC上实现了3.2%-4.86%的性能提升，在准确率比率上实现了6.41%-9.71%的提升。

英文摘要

Evaluating customer creditworthiness is crucial for retail banking operations, as it impacts marketing strategies, customer relationship management, and credit risk control. Traditional methods often struggle to capture complex temporal dependencies and extract pertinent information from customer data, crucial for accurate risk assessment. Specifically, they fail to differentiate between temporal patterns indicative of credit risk and those reflecting general customer behavior or preferences, leading to suboptimal risk predictions. In this study, we introduce the Disentangled Temporal Dependencies Variational Autoencoder (DTD-VAE), an advancement over conventional VAE, designed to disentangle temporal dependencies and distinguish credit risk-related features from past customer preferences. The feature inference module of the DTD-VAE incorporates an autoregressive temporal dependency learning mechanism that adeptly captures the temporal dependencies among latent variables, enriching the model's comprehension of the inherent data structure. Furthermore, the feature generative module utilizes an element-wise gating mechanism that assigns independent weights to each dimension of the expert models, enabling a finer-grained disentanglement of latent variables, particularly those relevant to credit risk prediction. Extensive experiments on six real-world datasets demonstrate that the proposed framework consistently outperforms existing methods, achieving performance gains of 3.2%-4.86% in ROC-AUC and 6.41%-9.71% in Accuracy Ratio.

发表机构

• China Minsheng Bank（中国民生银行）
• Longying Zhida (Beijing) Technology（龙影智达（北京）科技）
• Cyberspace Institute of Advanced Technology, Guangzhou University（广州大学先进技术与网络空间研究院）
• College of Computer Science, Beijing University of Technology（北京工业大学计算机学院）

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