Fraud Detection and Corporate Filings

Fraud Detection and Corporate Filings

Sunita Goel (Siena College, USA)
Copyright: © 2014 |Pages: 18
DOI: 10.4018/978-1-4666-4999-6.ch018
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This chapter is focused on detection of fraud in organizations by using content-based analysis on the annual reports issued by firms. Unlike a variety of previous work on fraud detection that have used quantitative financial information, this research examines qualitative textual content in annual reports to decipher evidence of fraud embedded in these reports through careful examination of the tone, content, and emphasis across reports. The basic premise of this research is that organizations tend to camouflage negative findings to sound less damaging. The real intent of the writer is hidden in content but can be revealed through structured content analysis. Using a corpus of annual reports of companies where fraud has occurred and juxtaposed with companies where fraud has not been detected, this study systematically examines the differences in the use of language. The results of this study reveal that fraudulent annual reports exhibit themes of optimism, variety, complexity, activity, and passivity. On the other hand, nonfraudulent annual reports exhibit themes of certainty and realism.
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The early studies on the use of qualitative textual content of corporate disclosures were hamstrung by the lack of large databases of financial statements, the lack of software for linguistic analysis, and lack of hardware powerful enough to support latest advances in multivariate statistical techniques in text and data mining. For the same reasons, there were virtually no uses of such qualitative analysis in the detection of fraud. We next present a brief account of the various studies that involves qualitative analysis of textual content of financial reports.

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