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arXiv AI By Mehrdad Shoeibi, Muhammad Shabanpour, Waldemar Karwowski, Niloofar Yousefi

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

Read the original on arXiv AI →

The paper presents a locked, multi‑signal audit protocol designed to detect supervision drift in credit‑risk models that use proxy labels. It comprises five layers—transfer performance, an oracle‑gap probe, a calibration diagnostic, feature‑label stability, and a synthetic positive control—each with predefined thresholds and decision rules. Applied to a public LendingClub dataset, the protocol shows stable ranking, small oracle gaps, and identifies a prevalence and probability‑scale mismatch that recalibration largely mitigates, though its root cause remains unclear.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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