A Comparative Analysis of the Bootstrap versus Traditional Statistical Procedures Applied to Digital Analysis Based on Benford's Law

Authors

Ik Seon Suh, Marquette UniversityFollow
Todd C. Headrick, Southern Illinois University Carbondale

Comments

Published in the Journal of Forensic & Investigative Accounting, 2010, Vol. 2, No. 2, pp. 144-175

Abstract

This study develops a bootstrap procedure applied to digital analysis based on Benford’s Law. It shows that the developed procedure provides accurate diagnoses of fraud as opposed to traditional statistical procedures. The traditional procedures such as the chi-square goodness-of-fit test exhibit the problem of excessive power as the volume of transactions becomes large. This problem may lead auditors to expend unnecessary fraud investigation costs. In contrast, applications of the proposed bootstrap procedure to reported annual earnings of S&P 1500 companies, Federal Election Commission data, and extremely fraudulent data demonstrate the robustness of the proposed procedure over different periods of time and across small or large financial data sets.

Recommended Citation

Suh, Ik Seon and Headrick, Todd C. "A Comparative Analysis of the Bootstrap versus Traditional Statistical Procedures Applied to Digital Analysis Based on Benford's Law." (Jul 2010).