IDEAS home Printed from https://ideas.repec.org/a/bla/acctfi/v66y2026i1p165-177.html

Financial Statement Fraud Detection by Integrating Supervisory Punishment Reports Into Machine Learning Methods: Evidence From China

Author

Listed:
  • Meng Luo
  • Chaoqun Ma
  • Dongqing Chen
  • Xianhua Mi

Abstract

This study proposes a novel text analysis framework for supervisory punishment reports (SPR) to enhance financial statement fraud detection (FSFD). By analysing 41,146 samples of Chinese listed companies with 11,711 SPR (2007–2021), the results show significant gains: 7.125% sensitivity, 12.85% specificity, 10.75% accuracy and 10.025% AUC improvement. The number of punishments was identified as the most critical factor. Furthermore, SPR also improves serious fraud detection accuracy and one‐year‐ahead prediction accuracy. This research provides methodological innovations and mechanistic insights for financial fraud analysis, highlighting the information value of textual regulatory disclosures in FSFD.

Suggested Citation

  • Meng Luo & Chaoqun Ma & Dongqing Chen & Xianhua Mi, 2026. "Financial Statement Fraud Detection by Integrating Supervisory Punishment Reports Into Machine Learning Methods: Evidence From China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 66(1), pages 165-177, March.
    • Handle: RePEc:bla:acctfi:v:66:y:2026:i:1:p:165-177
    DOI: 10.1111/acfi.70097
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/acfi.70097
    Download Restriction: no
    File URL: https://libkey.io/10.1111/acfi.70097?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:acctfi:v:66:y:2026:i:1:p:165-177. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/aaanzea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.