Identifying Fraud from the Language of Financial Reports

Abstract

We compare the effectiveness of financial statement variables and pre-determined lists of suspicious words with an alternative approach to fraud detection. Using the text of the Management Discussion and Analysis section from 10-Q and 10-K reports, we allow the data to identify words most strongly associated with financial misrepresentation. Based on the identified words, we assign a probability that a particular financial report is truthful within the time series of reports for the same firm. We compare this probability of truthfulness to predictions of fraud based on the F-Score from Dechow et al. (2011) and pre-defined word lists designed to capture deception, negativity, uncertainty and litigious activity. We establish that the data-generated word list can be a useful complement to detection methods based on financial statement variables by dramatically reducing the number of false positive predictions. In addition, our approach produces higher correct classification rates than textual analysis using alternative pre-determined word lists. Particularly interesting is our finding that word lists designed to reflect conscious deception have virtually no ability to correctly identify financial misrepresentation. This suggests that many of the individuals involved in drafting financial reports, including employees, auditors, and legal counsel may be completely unaware that the fraud is occurring. The data-generated word list continues to outperform alternative detection methods in a second sample representing a broad cross-section of firms.