Enhancement of fraud detection for narratives in annual reports

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

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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. Therefore, this work integrates natural language processing (NLP), queen genetic algorithm (QGA) and support vector machine (SVM) to develop a fraud detection method for narratives in annual reports, such as reports to shareholders, and thereby enhance the fraud detection accuracy and reduce investors' investment risks. To achieve the above-mentioned objective, a process of fraud detection for narratives in annual reports is first designed. Techniques related to fraud detection for the narratives in annual reports are then developed. Finally, the proposed fraud detection method is demonstrated and evaluated.

Original languageEnglish
Pages (from-to)32-45
Number of pages14
JournalInternational Journal of Accounting Information Systems
Volume26
DOIs
Publication statusPublished - 2017 Aug 1

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Accounting
  • Finance
  • Information Systems and Management

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