Beyond the Numbers: Mining the Annual Reports for Hidden Cues Indicative of Financial Statement Fraud

Abstract

SUMMARY 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.

Predicting Fraud from Quarterly Conference Calls: A Small-Sample Study of Scripted Language

“…While financial indicators remain a popular and powerful way to detect fraud, new research has suggested that linguistic analysis of the management discussion and analysis (MD&A) section of 10-K or 10-Q financial statements contains meaningful information to market participants. This type of analysis also has the ability to predict fraud (Cecchini et al 2010a;Glancy and Yadav 2011;Goel and Gangolly 2012;Goel et al 2010;Humpherys et al 2011). There is also meaningful information in news coverage about firms (Tetlock et al 2008).…”

“…Prior work in this area of fraud detection has explored the language of financial statements (Cecchini et al 2010a;Goel and Gangolly 2012;Goel et al 2010;Humpherys et al 2011) and the verbal (Larcker and Zakolyukina 2012) and non-verbal (Hobson et al 2012;Mayew and Venkatachalam 2012) behavior of executives in the conference calls. This paper is interested in integrating these two data sources to uncover instances of financial fraud.…”

Text mining to detect indications of fraud in annual reports worldwide

“…For example, in the UK the amount of text increased by 375% between 1965 and 2004 (Beattie et al, 2008). Goel and Gangolly (2012) reported an increase in the median numbers of 15.991 in 1994 to the median of 55.000 words in 2007 for 10-K annual reports. The increase includes regulatory as well as voluntary information, with the largest part of the increase attributable to the latter.…”

“…They demonstrated that a subset of the cues with the machine learning methods 'Decision Tree' and 'Naïve Bayes' contribute to the detection of fraud. Goel and Gangolly (2012) systematically tested linguistic cues of Humpherys et al (2011) and additional linguistic cues on 10-K annual reports and concluded that significant differences exist between fraudulent and non fraudulent reports in terms of sentence complexity, reading difficulty and the use of negative words, passive voice, uncertainty markers and adverbs.…”

“…The research concerning fraud detection in 10-K annual reports also includes several word groups. Goel and Gangolly (2012) used function words in their machine learning model to detect fraudulent 10-K reports. Furthermore, this model included adverbs as a key feature since a greater use of adverbs indicates the presence of fraud.…”