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This paper explores the application of data mining techniques to fraud detection in the audit of financial statements and proposes a taxonomy to support and guide future research. Currently, the application of data mining to auditing is at an early stage of development and researchers take a scatter-shot approach, investigating patterns in financial statement disclosures, text in annual reports and MD&As, and the nature of journal entries without appropriate guidance being drawn from lessons in known fraud patterns. To develop structure to research in datamining, we create a taxonomy that combines research on patterns of observed fraud schemes with an appreciation of areas that benefit from productive application of data mining. We encapsulate traditional views of data mining that operates primarily on quantitative data, such as financial statement and journal entry data. In addition, we draw on other forms of data mining, notably text and email mining.
5. Conclusions and suggestions for future research
The increasing value of data mining as a financial statement auditing tool is due to the convergence of several factors: (1) increasing emphasis on fraud detection in audits by regulators and standard setters, which provides motivation to identify and use tools to increase auditor productivity; (2) growing use of data mining tools as a forensic tool within accounting firms, which means there is a growing population of people within the firms with data mining experience as well as a general data mining awareness; and (3) the evolution of more robust and easier to use data mining tools. In addition, the expanding use of data mining as a de rigueur part of e-discovery in law suits have provided many examples of how data mining can be used for forensic investigations and, because of competition in the marketplace, e-discovery has accelerated the development of improved data mining tools. The growing general firm-level awareness of data mining and success of data mining in the legal profession may promote the firm-level decision to use data mining in more financial audits.