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

• URL: http://arxiv.org/abs/2609.16102v1
• Date: Mon, 14 Sep 2026 17:20:35 GMT
• Title: A Decision-Support Audit Protocol for Supervision Drift in Proxy-Labeled Credit-Risk Prediction
• Abstract summary: Credit-risk models are trained on proxy labels but no single metric separates base-rate shift, probability-scale shift, and feature-label relationship change.<n>We contribute a design-science artifact: a locked, multi-signal audit protocol for supervision drift in proxy-labeled credit-risk prediction.
• Score: 2.0804785116579425
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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