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A Decision-Support Audit Protocol for Supervision Drift in Proxy-Labeled Credit-Risk Prediction

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

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 ou

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