A Decision-Support Audit Protocol for Supervision Drift in Proxy-Labeled Credit-Risk Prediction
cs.LG, cs.AI
Submitted: 2026-09-14
Updated: 2026-09-14
Comments: 10 pages, 2 figures, 3 tables
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: 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
Terminology
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.
Sources
- Modeling and Controlling Deployment Reliability under Temporal Distribution Shift
- Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring
- Learning Stable Predictors from Weak Supervision under Distribution Shift
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