Session Title: Linguistic Modeling and Feature Extraction in Fraudulent Reporting
Presentation Date: Wednesday August 10, 2011
Presentation Time: 10:15 am-11:45 am
Are Quarterly Reports More Informative Than Annual Reports in Fraud Detection? A Linguistic Analysis
Sunita Goel, Siena College
ABSTRACT: This study examines qualitative textual content of quarterly reports and explores linguistic features that can successfully distinguish fraudulent quarterly reports from non-fraudulent quarterly reports. Previously we have used linguistic approach to fraud detection on dataset of annual reports. For this study we expanded our dataset to include quarterly reports. We wanted to find out if examination of quarterly reports would provide new insights on how companies portray themselves when they are committing fraud. Furthermore, we wanted to determine if the evidence from these experiments is consistent with our previous findings that we obtained as a result of examining annual reports. In this paper, we provide a detailed description of our methodology and results.
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