A Decision-Support Audit Protocol for Supervision Drift in Proxy-Labeled Credit-Risk Prediction
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Abstract (arXiv)
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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References · 10
- 2017On Calibration of Modern Neural Networkscited by 4,960
- 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,375
- 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 372
- 2018Detecting and Correcting for Label Shift with Black Box Predictorscited by 366
- 2016Data Programming: Creating Large Training Sets, Quicklycited by 246
- 2022Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoringcited by 6
- 2019Shallow Self-Learning for Reject Inference in Credit Scoringcited by 5
- 2021Managing dataset shift by adversarial validation for credit scoringcited by 3
- 2026Modeling and Controlling Deployment Reliability under Temporal Distribution Shiftcited by 1
- 2026Learning Stable Predictors from Weak Supervision under Distribution Shiftcited by 1
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