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Data Mining Techniques for the Detection of Fraudulent Financial Statements
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Financial statements have remained fairly stable in their appearance and content, however, wit increasing uses of technology the preparation and monitoring or measurement of the accuracy of these accounts is becoming more complex. In the US in 2002 more than $600 billion was though to be lost as a result of fraud, and in Europe it appears to be present in as many as 50% of major company failures. The use of data mining may be a way of increasing the potential to detect fraud in financial statements by trawling through the data that make up the accounts testing it. There are already tools used by auditors to test systems and data mining is a potential tool. This 17 paper presents a literature review and methodology that considers how the use of data mining may be tested to see if it is able to detect errors and the way it may be perceived by the accounting and auditing professions. The paper also includes a timetable for the study. The bibliography cites 16 sources.
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Pages:
17
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Filename:TS14_TEfruadatamin.rtf |
Paper Title:
Data Mining Techniques for the Detection of Fraudulent Financial Statements
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