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Fraudulent financial reporting detection and business failure prediction models: a comparison

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  • Fen-May Liou

Abstract

Purpose - The purpose is to explore the differences and similarities between fraudulent financial reporting detection and business failure prediction (BFP) models, especially in terms of which explanatory variables and methodologies are most effective. Design/methodology/approach - In total, 52 financial variables were identified from previous studies as potentially significant. A number of Taiwanese firms experienced financial distress or were accused of fraudulent reporting in 2005. Data on these firms and their contemporaries were obtained from the Findings - Many of the variables are effective at both detecting fraudulent financial reporting and predicting business failures. In terms of overall accuracy, logistic regression outperforms the other two algorithms for detecting fraudulent financial reporting. Whether logistic regression or a decision tree is best for BFP depends on the relative opportunity cost of misclassifying failing and healthy firms. Originality/value - The financial factors used to detect fraudulent reporting are helpful for predicting business failure.

Suggested Citation

  • Fen-May Liou, 2008. "Fraudulent financial reporting detection and business failure prediction models: a comparison," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(7), pages 650-662, July.
  • Handle: RePEc:eme:majpps:v:23:y:2008:i:7:p:650-662
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    References listed on IDEAS

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    Cited by:

    1. Shirley Wong & Sitalakshmi Venkatraman, 2015. "Financial Accounting Fraud Detection Using Business Intelligence," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 5(11), pages 1187-1207, November.
    2. Mary Jane Lenard & Karin A. Petruska & Pervaiz Alam & Bing Yu, 2012. "Indicators of audit fees and fraud classification: impact of SOX," Managerial Auditing Journal, Emerald Group Publishing, vol. 27(5), pages 500-525, May.
    3. Feng, Wenjun & Wang, Yiming & Zhang, Zhengjun, 2018. "Informed trading in the Bitcoin market," Finance Research Letters, Elsevier, vol. 26(C), pages 63-70.
    4. Harlan L. Etheridge & Kathy H. Y. Hsu, 2015. "Minimizing the Costs of Using Models to Assess the Financial Health of Banks," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 5(11), pages 9-18, November.
    5. Steven Liew Woon Choy & Jayaraman Munusamy & Shankar Chelliah & Ally Mandari, 2011. "Effects of Financial Distress Condition on the Company Performance: A Malaysian Perspective," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 85-99, August.
    6. Abdul Ghafoor & Rozaimah Zainudin & Nurul Shahnaz Mahdzan, 2019. "Factors Eliciting Corporate Fraud in Emerging Markets: Case of Firms Subject to Enforcement Actions in Malaysia," Journal of Business Ethics, Springer, vol. 160(2), pages 587-608, December.
    7. Amani, Farzaneh A. & Fadlalla, Adam M., 2017. "Data mining applications in accounting: A review of the literature and organizing framework," International Journal of Accounting Information Systems, Elsevier, vol. 24(C), pages 32-58.
    8. Victor Munteanu & Lavinia Copcinschi & Carmen Luschi & Anda Laceanu, 2017. "Internal Audit – Determinant Factor In Preventing And Detecting Fraud Related Activity To Public Entities Financial Accounting," Knowledge Horizons - Economics, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 9(1), pages 55-63, March.
    9. Harlan L. Etheridge & Kathy H. Y. Hsu, 2015. "Minimizing the Costs of Using Models to Assess the Financial Health of Banks," International Journal of Business and Social Research, LAR Center Press, vol. 5(11), pages 9-18, November.
    10. Gullkvist, Benita & Jokipii, Annukka, 2013. "Perceived importance of red flags across fraud types," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 24(1), pages 44-61.

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