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Chakrabarthy, B, Moulton, PC, Pugachev, L and Wang, X (2019)

Catch Me If You Can: Improving the Scope and Accuracy of Fraud Prediction

E-print available at SSRN:, March 13, 2019.

ISSN/ISBN: Not available at this time. DOI: 10.2139/ssrn.3352667

Abstract: We propose a parsimonious metric – the Adjusted Benford score (AB-score) – to improve the detection of financial misstatements. Based on Benford’s Law, which predicts the leading-digit distribution of naturally occurring numbers, the AB-score estimates a firm-year’s likelihood of financial statement manipulation, compared to its peers and controlling for time-series trends. The AB-score requires less data than the leading accounting-based misstatement metric (the F-score) and can be computed for many more firm-years, including for financial firms. For firm-years with all data available, combining the AB-score and F-score variables into one model yields higher accuracy in predicting misstatements in- and out-of-sample.

@misc{, AUTHOR = {Bidisha Chakrabarty and Pamela C. Moulton and Leonid Pugachev and Xu (Frank) Wang}, TITLE = {Catch Me If You Can: Improving the Scope and Accuracy of Fraud Prediction}, HOWPUBLISHED = {\url{}}, YEAR = {2019}, DOI = {10.2139/ssrn.3352667}, NOTE = {last accessed Jun 7, 2019}, }

Reference Type: E-Print

Subject Area(s): Accounting