Artificial neural networks applied to ratio analysis in the analytical review process
International Journal of Intelligent Systems in Accounting and Finance Management - Special issue on neural networks
Model Validation Using Simulated Data
ASSET '98 Proceedings of the 1998 IEEE Workshop on Application - Specific Software Engineering and Technology
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
An investigation of Zipf's Law for fraud detection (DSS#06-10-1826R(2))
Decision Support Systems
A decision support model for tax revenue collection in Greece
Decision Support Systems
Journal of Information Science
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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 3x3 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.