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Detection of fraud statement based on word vector: Evidence from financial companies in China

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  • Zhang, Yi
  • Hu, Ailing
  • Wang, Jiahua
  • Zhang, Yaojie

Abstract

This paper aims to detect plausible frauds of financial companies via text analysis of annual and quarterly reports of China's listed companies. The Management Discussion and Analysis (MD&A) is digitized as vectors. The empirical results indicate that compared with various vector indexes, the bag-of-words (BoW) model and machine learning algorithm have a prediction effect and the ability to recognize frauds where the BoW model can correctly recognize 77% of the fraud reports. This would be helpful for audit authorities to identify fraud reports.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:finlet:v:46:y:2022:i:pb:s1544612321004578
    DOI: 10.1016/j.frl.2021.102477
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    References listed on IDEAS

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    1. Bonsall, Samuel B. & Leone, Andrew J. & Miller, Brian P. & Rennekamp, Kristina, 2017. "A plain English measure of financial reporting readability," Journal of Accounting and Economics, Elsevier, vol. 63(2), pages 329-357.
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    Cited by:

    1. Lei, Yu-Tian & Ma, Chao-Qun & Ren, Yi-Shuai & Chen, Xun-Qi & Narayan, Seema & Huynh, Anh Ngoc Quang, 2023. "A distributed deep neural network model for credit card fraud detection," Finance Research Letters, Elsevier, vol. 58(PC).
    2. Zhang, Yi & Liu, Tianxiang & Li, Weiping, 2024. "Corporate fraud detection based on linguistic readability vector: Application to financial companies in China," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    3. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
    4. Maria Tragouda & Michalis Doumpos & Constantin Zopounidis, 2024. "Identification of fraudulent financial statements through a multi‐label classification approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    5. Shi, Feifen & Zhao, Chuanjun, 2023. "Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information," Finance Research Letters, Elsevier, vol. 58(PB).
    6. Li, Jing & Li, Nan & Xia, Tongshui & Guo, Jinjin, 2023. "Textual analysis and detection of financial fraud: Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 126(C).

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