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A Solution for Preventing Fraudulent Financial Reporting using Descriptive Data Mining Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 58 - Number 1 |
| Year of Publication: 2012 |
| Authors: Rajan Gupta, Nasib Singh Gill |
10.5120/9247-3411
|
Rajan Gupta, Nasib Singh Gill . A Solution for Preventing Fraudulent Financial Reporting using Descriptive Data Mining Techniques. International Journal of Computer Applications. 58, 1 ( November 2012), 22-28. DOI=10.5120/9247-3411
Abstract
In the present age of scams, financial statement fraud represents enormous cost to our 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.
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