代理标签信用风险预测中监督漂移的决策支持审计协议
A Decision-Support Audit Protocol for Supervision Drift in Proxy-Labeled Credit-Risk Prediction
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中文总结 AI 辅助
本研究提出一个锁定、多信号的审计协议，用于检测代理标签信用风险预测中的监督漂移，通过五层诊断在LendingClub数据上验证，识别出患病率与概率尺度不匹配为主要时间信号。
中文摘要 AI 辅助
信用风险模型基于代理标签进行训练，并在时间和细分市场变化下部署，然而没有任何单一的迁移指标能够区分基础率偏移、概率尺度偏移和特征-标签关系变化。我们贡献了一个设计科学产物：一个锁定、多信号的审计协议，用于代理标签信用风险预测中的监督漂移。五层（迁移性能、预言机差距探针、校准诊断、特征-标签稳定性以及合成阳性对照）、阈值和决策规则在解释之前被锁定；有界解读是设计的结果。在公开的LendingClub数据集上（时间上从2013年到2016年以及跨细分市场迁移），排名稳定且预言机差距较小；最清晰的时间信号是患病率和概率尺度不匹配，仅截距诊断重新校准在很大程度上减少了这种不匹配，尽管其原因无法从现有发布中识别。阳性对照仅对较大的注入偏移有响应；更细微的漂移无法排除。将诊断模式映射到治理行动是概念性指导，此处未经验证。
英文摘要
Credit-risk models are trained on proxy labels and deployed under temporal and segment change, yet no single transfer metric separates base-rate shift, probability-scale shift, and feature-label relationship change. We contribute a design-science artifact: a locked, multi-signal audit protocol for supervision drift in proxy-labeled credit-risk prediction. Five layers (transfer performance, an oracle-gap probe, a calibration diagnostic, feature-label stability, and a synthetic positive control), thresholds, and decision rules were locked before interpretation; a bounded reading is a designed outcome. On a public LendingClub dataset (temporal 2013 to 2016 and cross-segment transfer), ranking is stable and oracle gaps are small; the clearest temporal signal is a prevalence and probability-scale mismatch that intercept-only diagnostic recalibration largely reduces, though its cause is not identifiable from the available release. The positive control responds only to larger injected shifts; subtler drift cannot be excluded. Mapping diagnostic patterns to governance actions is conceptual guidance, not validated here.
发表机构
- University of Central Florida（中佛罗里达大学）
- Northeastern University（东北大学）
机构由 AI 辅助整理，请以论文原文为准。
