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
    ---><---

    References listed on IDEAS

    as
    1. Chao Fu & Xiuyuan Deng & Hongfei Tang, 2023. "Who cares about corporate fraud? Evidence from cross-border mergers and acquisitions of Chinese companies," Review of Quantitative Finance and Accounting, Springer, vol. 60(2), pages 747-789, February.
    2. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 199-235, March.
    3. Zhang, Zejun & Wang, Zhao & Cai, Lixin, 2025. "Predicting financial fraud in Chinese listed companies: An enterprise portrait and machine learning approach," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    4. Li, Guowen & Wang, Shuai & Feng, Yuyao, 2024. "Making differences work: Financial fraud detection based on multi-subject perceptions," Emerging Markets Review, Elsevier, vol. 60(C).
    5. 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).
    6. Wang, Yang & Ashton, John K. & Jaafar, Aziz, 2019. "Money shouts! How effective are punishments for accounting fraud?," The British Accounting Review, Elsevier, vol. 51(5).
    7. Liuyang Ren & Xi Zhong & Liangyong Wan, 2022. "Missing Analyst Forecasts and Corporate Fraud: Evidence from China," Journal of Business Ethics, Springer, vol. 181(1), pages 171-194, November.
    8. Xiaowei Chen & Cong Zhai, 2023. "Bagging or boosting? Empirical evidence from financial statement fraud detection," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5093-5142, December.
    9. Aimin Qian & Dapeng Zhu, 2019. "Financial report similarity and the likelihood of administrative punishment:based on the empirical evidence of textual analysis," China Journal of Accounting Studies, Taylor & Francis Journals, vol. 7(2), pages 147-169, April.
    10. Lu, Pu & Wang, Yong & Li, Bing, 2024. "Short selling and corporate financial fraud: Empirical evidence from China," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1569-1582.
    11. Li, Jingyu & Guo, Ce & Lv, Sijia & Xie, Qiwei & Zheng, Xiaolong, 2024. "Financial fraud detection for Chinese listed firms: Does managers' abnormal tone matter?," Emerging Markets Review, Elsevier, vol. 62(C).
    12. Achakzai, Muhammad Atif Khan & Peng, Juan, 2023. "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, vol. 89(C).
    13. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    14. Ruijie Sun & Feng Liu & Yinan Li & Rongping Wang & Jing Luo, 2024. "Machine Learning for Predicting Corporate Violations: How Do CEO Characteristics Matter?," Journal of Business Ethics, Springer, vol. 195(1), pages 151-166, November.
    15. Zhou, Ying & Xiao, Zhi & Gao, Ruize & Wang, Chang, 2024. "Using data-driven methods to detect financial statement fraud in the real scenario," International Journal of Accounting Information Systems, Elsevier, vol. 54(C).
    16. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 237-291, March.
    17. 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).
    18. Wei, Li & Peng, Ming & Wu, Weixing, 2021. "Financial literacy and fraud detection——Evidence from China," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 478-494.
    19. Rong Liu & Jujun Huang & Zhongju Zhang, 2023. "Tracking disclosure change trajectories for financial fraud detection," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 584-602, February.
    20. Lin Liao & Guanting Chen & Dengjin Zheng, 2019. "Corporate social responsibility and financial fraud: evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(5), pages 3133-3169, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Guowen & Wang, Shuai & Feng, Yuyao, 2024. "Making differences work: Financial fraud detection based on multi-subject perceptions," Emerging Markets Review, Elsevier, vol. 60(C).
    2. Zhaolong Zhang & Zhenyu Liu & Fengmin Xu & Xiangyu Chang, 2025. "Detecting Accounting Fraud in China A‐Share Market With PU Learning," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 65(4), pages 3361-3378, December.
    3. Xiaoqian Zhu & Huidong Wu & Yanpeng Chang & Jianping Li, 2025. "Accounting fraud detection through textual risk disclosures in annual reports: From the perspective of SEC guidelines," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 65(2), pages 1837-1862, June.
    4. Yunchuan Sun & Xiaoping Zeng & Ying Xu & Hong Yue & Xipu Yu, 2024. "An intelligent detecting model for financial frauds in Chinese A‐share market," Economics and Politics, Wiley Blackwell, vol. 36(2), pages 1110-1136, July.
    5. Achakzai, Muhammad Atif Khan & Peng, Juan, 2023. "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, vol. 89(C).
    6. Lu, Hanglin & Zhang, Yongjie & Xu, Jinchang, 2025. "Extraction of characteristic information from financial super-long texts and prediction of corporate violations," Research in International Business and Finance, Elsevier, vol. 79(C).
    7. Qi, Yu & Su, Hang, 2025. "Can artificial intelligence mitigate corporate fraud? Exploring the influence of institutional cross-holdings and financial misallocation," Pacific-Basin Finance Journal, Elsevier, vol. 92(C).
    8. Zhou, Yong & Liu, Siting & Song, Wei, 2025. "Bankruptcy court establishment and corporate financial fraud," International Review of Economics & Finance, Elsevier, vol. 100(C).
    9. Md Jahidur Rahman & Hongtao Zhu, 2023. "Predicting accounting fraud using imbalanced ensemble learning classifiers – evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3455-3486, September.
    10. 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.
    11. Wei, Mingye & Zhang, Min & Wei, Lu & Chen, Meiqi, 2025. "IPOhelper: Mining features in registration statements for listing prediction of technological innovation companies," Emerging Markets Review, Elsevier, vol. 68(C).
    12. Ruijie Sun & Feng Liu & Yinan Li & Rongping Wang & Jing Luo, 2024. "Machine Learning for Predicting Corporate Violations: How Do CEO Characteristics Matter?," Journal of Business Ethics, Springer, vol. 195(1), pages 151-166, November.
    13. Liu, Zhengxu & Xie, Jing, 2025. "How does executive pay gap affect enterprises' financial fraud?," Finance Research Letters, Elsevier, vol. 85(PC).
    14. So-Jin Yu & Jin-Sung Rha, 2021. "Research Trends in Accounting Fraud Using Network Analysis," Sustainability, MDPI, vol. 13(10), pages 1-26, May.
    15. Xiaowei Chen & Cong Zhai, 2023. "Bagging or boosting? Empirical evidence from financial statement fraud detection," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5093-5142, December.
    16. Anas Alqudah & Safa Jaradat & Lara Al-Haddad, 2025. "Financial Consequences of Fraud in Amman Stock Exchange Firms," Advances in Decision Sciences, Asia University, Taiwan, vol. 29(1), pages 83-111, March.
    17. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    18. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    19. Çiğdem Özari & Esin Nesrin Can & Özge Demirkale, 2025. "Financial Fraud Detection with Altman Z-Score and Beneish M-Score via Random Forest: Verified by Borsa Istanbul Fines (2018–2022)," SAGE Open, , vol. 15(4), pages 21582440251, October.
    20. Chen, Yunyan & Wu, Shinong & Zhou, Yucheng & Huo, Di, 2023. "Gambling culture and corporate violations: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 80(C).

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.