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Beyond The Numbers: Mining The Annual Reports For Hidden Cues Indicative Of Financial Statement Fraud

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  • Sunita Goel
  • Jagdish Gangolly

Abstract

Unlike previous fraud detection research, a vast majority of which has focused primarily on the use of quantitative financial information to predict fraud, in this study we examine qualitative textual content in annual reports to predict fraud and see whether there are discernible differences in the writing and presentation style between companies that committed fraud and those that did not. We believe that while numeric financial information in the annual reports can hide details of fraud, textual information relating to writing and presentation styles in such reports provides valuable clues pertaining to the existence of fraud. In this study we use the chi‐square test to analyse our data and test hypotheses about predictors of fraud that may explain linguistic feature variations in fraudulent and nonfraudulent annual reports. We provide new results on the usefulness of the qualitative content of annual reports in detecting fraud. Copyright © 2012 John Wiley & Sons, Ltd.

Suggested Citation

  • Sunita Goel & Jagdish Gangolly, 2012. "Beyond The Numbers: Mining The Annual Reports For Hidden Cues Indicative Of Financial Statement Fraud," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 75-89, April.
    • Handle: RePEc:wly:isacfm:v:19:y:2012:i:2:p:75-89
    DOI: 10.1002/isaf.1326
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    References listed on IDEAS

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    Cited by:

    1. Ochiai, Tomoshiro & Nacher, Jose C., 2022. "Unveiling the directional network behind financial statements data using volatility constraint correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    2. Jaeschke, Reemda & Lopatta, Kerstin & Yi, Cheong, 2018. "Managers’ use of language in corrupt firms’ financial disclosures: Evidence from FCPA violators," Scandinavian Journal of Management, Elsevier, vol. 34(2), pages 170-192.
    3. Zhang, Yi & Hu, Ailing & Wang, Jiahua & Zhang, Yaojie, 2022. "Detection of fraud statement based on word vector: Evidence from financial companies in China," Finance Research Letters, Elsevier, vol. 46(PB).
    4. 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.
    5. Sunita Goel & Ozlem Uzuner, 2016. "Do Sentiments Matter in Fraud Detection? Estimating Semantic Orientation of Annual Reports," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 215-239, July.
    6. YAN, Beibei & AERTS, Walter, 2014. "Rhetorical impression management in corporate narratives and institutional environment," Working Papers 2014014, University of Antwerp, Faculty of Business and Economics.
    7. Tomoshiro Ochiai & Jose C. Nacher, 2020. "Unveiling the directional network behind the financial statements data using volatility constraint correlation," Papers 2008.07836, arXiv.org, revised Jun 2023.
    8. Elias Zavitsanos & Dimitris Mavroeidis & Konstantinos Bougiatiotis & Eirini Spyropoulou & Lefteris Loukas & Georgios Paliouras, 2023. "Financial misstatement detection: a realistic evaluation," Papers 2305.17457, arXiv.org.
    9. Senave, Elseline & Jans, Mieke J. & Srivastava, Rajendra P., 2023. "The application of text mining in accounting," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).
    10. Fahd Alduais & Nashat Ali Almasria & Abeer Samara & Ali Masadeh, 2022. "Conciseness, Financial Disclosure, and Market Reaction: A Textual Analysis of Annual Reports in Listed Chinese Companies," IJFS, MDPI, vol. 10(4), pages 1-22, November.

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