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Fraud detection for financial statements of business groups

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  • Chen, Yuh-Jen
  • Liou, Wan-Ching
  • Chen, Yuh-Min
  • Wu, Jyun-Han

Abstract

Investors rely on companies' financial statements and economic data to inform their investment decisions. However, many businesses manipulate financial statements to raise more capital from investors and financial institutions, which reduces the practicality of financial statements. The modern business environment is highly information-oriented, and firms' information systems and activities are complex and dynamic. Technology for avoiding fraud detection is continually updated. Recent studies have focused on detecting financial statement fraud within a single business, but not within a business group. Development of methods for using diverse data to detect financial statement fraud in business groups is thus a high priority in the advancement of fraud detection.

Suggested Citation

  • Chen, Yuh-Jen & Liou, Wan-Ching & Chen, Yuh-Min & Wu, Jyun-Han, 2019. "Fraud detection for financial statements of business groups," International Journal of Accounting Information Systems, Elsevier, vol. 32(C), pages 1-23.
    • Handle: RePEc:eee:ijoais:v:32:y:2019:i:c:p:1-23
    DOI: 10.1016/j.accinf.2018.11.004
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    References listed on IDEAS

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    Cited by:

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    3. Kumar, Satish & Marrone, Mauricio & Liu, Qi & Pandey, Nitesh, 2020. "Twenty years of the International Journal of Accounting Information Systems: A bibliometric analysis," International Journal of Accounting Information Systems, Elsevier, vol. 39(C).
    4. You-Shyang Chen & Chien-Ku Lin & Chih-Min Lo & Su-Fen Chen & Qi-Jun Liao, 2021. "Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry," Mathematics, MDPI, vol. 9(20), pages 1-26, October.
    5. Luis Alfonso Dau & Randall Morck & Bernard Yin Yeung, 2021. "Business groups and the study of international business: A Coasean synthesis and extension," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 52(2), pages 161-211, March.
    6. Ahmad Hammami & Mohammad Hendijani Zadeh, 2022. "Predicting earnings management through machine learning ensemble classifiers," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1639-1660, December.
    7. Wei Sun & Alisher Tohirovich Dedahanov & Ho Young Shin & Ki Su Kim, 2020. "Switching intention to crypto-currency market: Factors predisposing some individuals to risky investment," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-16, June.
    8. Kocsis, David, 2019. "A conceptual foundation of design and implementation research in accounting information systems," International Journal of Accounting Information Systems, Elsevier, vol. 34(C), pages 1-1.

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