Abstract
ABSTRACTFalse positives or “Type I errors,” wherein test results indicate fraud where none actually exists, have been described as a costly “cry wolf problem” in auditing practice. Benford's Law, which is used as one tool among many in screening for financial statement manipulation, is especially prone to false positives when applied to small and moderately sized datasets. Relying in part on Monte Carlo simulations, we describe with greater precision than extant literature the mathematical correlation between N and Mean Absolute Deviation (MAD), a statistic increasingly used for assessing deviation from Benford's Law. We recommend replacing MAD with an alternative, Excess MAD, which explicitly adjusts for N in estimating deviation from Benford's Law. Applying nonparametric, generalized additive modeling to public company financial statement numbers, we demonstrate the differing outcomes expected from Excess MAD and MAD and produce evidence suggesting that, despite Sarbanes-Oxley and Dodd-Frank legislation, Benford's Law conformity of public company financial statement numbers remained relatively stable across four decades beginning in 1970.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.