Problem
Research questions and friction points this paper is trying to address.
credit-risk prediction
proxy labels
supervision drift
temporal change
segment change
A Decision-Support Audit Protocol for Supervision Drift in Proxy-Labeled Credit-Risk Prediction
🤖 AI Summary
本文提出一个多信号审计协议，用于检测信用风险预测中的监督漂移问题，通过五个层次的诊断来识别和处理不同类型的漂移。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
supervision drift
proxy-labeled credit-risk prediction
multi-signal audit protocol
oracle-gap probe
calibration diagnostic
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