A Solution for Preventing Fraudulent Financial Reporting Using Descriptive Data Mining Techniques

In the present age of scams, financial statement fraud represents enormous cost to user's economy. The deliberate misstatement of numbers in the accounting books with the help of well planned scheme by an intelligent squad of knowledgeable perpetrators in order to deceive the capital market participants is termed as financial statement fraud. In order to reduce fraud risk which comprehends both detection and prevention of financial statement fraud, this paper implements descriptive data mining techniques such as Association rules and clustering as opposed to predictive data mining techniques used in the literature. Each of these techniques is applied on dataset obtained from financial statements namely balance sheet, income statement and cash flow statement of 114 companies.

Provided by: International Journal of Computer Applications Topic: Big Data Date Added: Nov 2012 Format: PDF

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