An ANN-based auditor decision support system using Benford's law

Sukanto Bhattacharya*, Dongming Xu, Kuldeep Kumar

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

31 Citations (Scopus)


While there is a growing professional interest on the application of Benford's law and "digit analysis" in financial fraud detection, there has been relatively little academic research to demonstrate its efficacy as a decision support tool in the context of an analytical review procedure pertaining to a financial audit. We conduct a numerical study using a genetically optimized artificial neural network. Building on an earlier work by others of a similar nature, we assess the benefits of Benford's law as a useful classifier in segregating naturally occurring (i.e. non-concocted) numbers from those that are made up. Alongside the frequency of the first and second significant digits and their mean and standard deviation, a posited set of 'non-digit' input variables categorized as "information theoretic", "distance-based" and "goodness-of-fit" measures, help to minimize the critical classification errors that can lead to an audit failure. We come up with the optimal network structure for every instance corresponding to a 3 × 3 Manipulation-Involvement matrix that is drawn to depict the different combinations of the level of sophistication in data manipulation by the perpetrators of a financial fraud and also the extent of collusive involvement.

Original languageEnglish
Pages (from-to)576-584
Number of pages9
JournalDecision Support Systems
Issue number3
Early online date18 Aug 2010
Publication statusPublished - Feb 2011


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