Article ID: | iaor20112095 |
Volume: | 50 |
Issue: | 3 |
Start Page Number: | 576 |
End Page Number: | 584 |
Publication Date: | Feb 2011 |
Journal: | Decision Support Systems |
Authors: | Xu Dongming, Kumar Kuldeep, Bhattacharya Sukanto |
Keywords: | neural networks |
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