The Cost of Fraud Prediction Errors

ABSTRACT We compare seven fraud prediction models with a cost-based measure that nets the benefits of correctly anticipating instances of fraud against the costs borne by incorrectly flagging non-fraud firms. We find that even the best models trade off false to true positives at rates exceeding 100:1. Indeed, the high number of false positives makes all seven models considered too costly for auditors to implement, even in subsamples where misreporting is more likely. For investors, M-Score and, at higher cut-offs, the F-Score, are the only models providing a net benefit. For regulators, several models are economically viable as false positive costs are limited by the number of investigations regulators can initiate, and by the relatively low market value loss a “falsely accused” firm would bear in denials of requests under the Freedom of Information Act (FOIA). Our results are similar whether we consider fraud or two alternative restatement samples. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G31; G32; G34; M40.