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

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• 作者：Sukanto Bhattacharya^a ; ^{sukanto.bhattacharya@deakin.edu.au} ; Dongming Xu^b ; Kuldeep Kumar^c [Author vitae]
• 关键词：Benford's law ; Auditor decision support system ; ARPs ; ANNs ; Genetic optimization
• 刊名：Decision Support Systems
• 出版年：2011
• 出版时间：February 2011
• 年：2011
• 卷：50
• 期：3
• 页码：576-584
• 全文大小：384 K

文摘

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.