An Exploratory Study into Deception Detection in Text-Based Computer-Mediated Communication
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track1 - Volume 1
Segmenting documents by stylistic character
Natural Language Engineering
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
Following linguistic footprints: automatic deception detection in online communication
Communications of the ACM - Enterprise information integration: and other tools for merging data
Making words work: Using financial text as a predictor of financial events
Decision Support Systems
Identification of fraudulent financial statements using linguistic credibility analysis
Decision Support Systems
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Unlike previous fraud detection research, a vast majority of which has focused primarily on the use of quantitative financial information to predict fraud, in this study we examine qualitative textual content in annual reports to predict fraud and see whether there are discernible differences in the writing and presentation style between companies that committed fraud and those that did not. We believe that while numeric financial information in the annual reports can hide details of fraud, textual information relating to writing and presentation styles in such reports provides valuable clues pertaining to the existence of fraud. In this study we use the chi-square test to analyse our data and test hypotheses about predictors of fraud that may explain linguistic feature variations in fraudulent and nonfraudulent annual reports. We provide new results on the usefulness of the qualitative content of annual reports in detecting fraud. Copyright © 2012 John Wiley & Sons, Ltd.