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

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