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Detecting Deception In Asynchronous Text, Fletcher Glancy
Detecting Deception In Asynchronous Text, Fletcher Glancy
Annual ADFSL Conference on Digital Forensics, Security and Law
Glancy and Yadav (2010) developed a computational fraud detection model (CFDM) that successfully detected financial reporting fraud in the text of the management’s discussion and analysis (MDA) portion of annual filings with the United States Securities and Exchange Commission (SEC). This work extends the use of the CFDM to additional genres, demonstrates the generalizability of the CFDM and the use of text mining for quantitatively detecting deception in asynchronous text. It also demonstrates that writers committing fraud use words differently from truth tellers.