Machine learning for journal entry testing: A type-aware evaluation of anomaly detectors under a review budget

Excerpt

arXiv:2610.06133v1 Announce Type: cross Abstract: Journal entry anomaly detectors are commonly evaluated on the full population with ROC-AUC, precision and recall, ignoring the review budget and which anomaly types are found. We propose a type-aware evaluation combining per-type recall, fair-share type recall (FSR), which caps each type's credit at its budget share, type coverage and first-hit rank. We evaluate nine unsupervised detectors, a supervised reference and feedback-driven Deep Semi-Sup