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

Jan Gronewald, Michel Scherer, Nijat Mehdiyev

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 p…