Enhancement of fraud detection for narratives in annual reports

Yuh-Jen Chen, Chun-Han Wu, Yuh-Min Chen, Hsin-Ying Li and Huei-Kuen Chen

International Journal of Accounting Information Systems, 2017, vol. 26, issue C, 32-45

Abstract: Annual reports present the activities of a listed company in terms of its operational performance, financial conditions, and social responsibilities. These reports are a valuable reference for numerous investors, creditors, and other accounting information end users. However, many annual reports exaggerate enterprise activities to raise investors' capital and support from financial institutions, thereby diminishing the usefulness of such reports. Effectively detecting fraud in the annual report of a company is thus a priority concern during an audit.

Keywords: Narratives; Annual reports; Fraud detection; Natural language processing (NLP); Queen genetic algorithm (QGA); Support vector machine (SVM) (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijoais:v:26:y:2017:i:c:p:32-45

DOI: 10.1016/j.accinf.2017.06.004

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