A Data Mining Framework for Prevention and Detection of Financial Statement Fraud

Rajan Gupta, Nasib Singh Gill
<span title="2012-07-28">2012</span> <i title="Foundation of Computer Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a> </i> &nbsp;
Financial statement fraud has reached the epidemic proportion globally. Recently, financial statement fraud has dominated the corporate news causing debacle at number of companies worldwide. In the wake of failure of many organisations, there is a dire need of prevention and detection of financial statement fraud. Prevention of financial statement fraud is a measure to stop its occurrence initially whereas detection means the identification of such fraud as soon as possible. Fraud detection is
more &raquo; ... equired only if prevention has failed. Therefore, a continuous fraud detection mechanism should be in place because management may be unaware about the failure of prevention mechanism. In this paper we propose a data mining framework for prevention and detection of financial statement fraud.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/7789-0889">doi:10.5120/7789-0889</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3tcdhglr7batpmkirbg3ndo7li">fatcat:3tcdhglr7batpmkirbg3ndo7li</a> </span>
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