Enhancement of fraud detection for narratives in annual reports
| Autor: | Yuh-Jen Chen, Huei Kuen Chen, Chun Han Wu, Yuh-Min Chen, Hsin Ying Li |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Information Systems and Management
business.industry Creditor End user 05 social sciences Accounting 050201 accounting 02 engineering and technology Annual report Audit Management Information Systems Work (electrical) Shareholder 0502 economics and business Accounting information system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Business Social responsibility Finance |
| Zdroj: | International Journal of Accounting Information Systems. 26:32-45 |
| ISSN: | 1467-0895 |
| DOI: | 10.1016/j.accinf.2017.06.004 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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