IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v89y2023ics1057521923003435.html
   My bibliography  Save this article

Detecting financial statement fraud using dynamic ensemble machine learning

Author

Listed:
  • Achakzai, Muhammad Atif Khan
  • Peng, Juan

Abstract

Our study uses Machine learning to develop an advanced fraud detection model that can detect fraudulent firms. We build our model using raw financial and non-financial variables following prior literature. In addition, we introduce the Dynamic Ensemble Selection algorithm to the fraud detection literature, which combines individual classifiers dynamically to make a final prediction. Using several performance evaluation metrics, we find that our model can outperform several machine learning models used in recent studies.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:finana:v:89:y:2023:i:c:s1057521923003435
    DOI: 10.1016/j.irfa.2023.102827
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521923003435
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2023.102827?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Graham, John R. & Li, Si & Qiu, Jiaping, 2008. "Corporate misreporting and bank loan contracting," Journal of Financial Economics, Elsevier, vol. 89(1), pages 44-61, July.
    2. Martin J. Conyon & Lerong He, 2016. "Executive Compensation and Corporate Fraud in China," Journal of Business Ethics, Springer, vol. 134(4), pages 669-691, April.
    3. Lynnette Purda & David Skillicorn, 2015. "Accounting Variables, Deception, and a Bag of Words: Assessing the Tools of Fraud Detection," Contemporary Accounting Research, John Wiley & Sons, vol. 32(3), pages 1193-1223, September.
    4. Yi Wei & Jianguo Chen & Carolyn Wirth, 2017. "Detecting fraud in Chinese listed company balance sheets," Pacific Accounting Review, Emerald Group Publishing Limited, vol. 29(3), pages 356-379, August.
    5. 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.
    6. Zhou, Zhong-guo & Hussein, Monica & Deng, Qi, 2021. "ChiNext IPOs' initial returns before and after the 2013 stock market reform: What can we learn?," Emerging Markets Review, Elsevier, vol. 48(C).
    7. Jeremy Bertomeu, 2020. "Machine learning improves accounting: discussion, implementation and research opportunities," Review of Accounting Studies, Springer, vol. 25(3), pages 1135-1155, September.
    8. Wenfeng Wu & Sofia A. Johan & Oliver M. Rui, 2016. "Institutional Investors, Political Connections, and the Incidence of Regulatory Enforcement Against Corporate Fraud," Journal of Business Ethics, Springer, vol. 134(4), pages 709-726, April.
    9. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
    10. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    11. Chen, Gongmeng & Firth, Michael & Gao, Daniel N. & Rui, Oliver M., 2006. "Ownership structure, corporate governance, and fraud: Evidence from China," Journal of Corporate Finance, Elsevier, vol. 12(3), pages 424-448, June.
    12. Mark Cecchini & Haldun Aytug & Gary J. Koehler & Praveen Pathak, 2010. "Detecting Management Fraud in Public Companies," Management Science, INFORMS, vol. 56(7), pages 1146-1160, July.
    13. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 237-291, March.
    14. Firth, Michael & Rui, Oliver M. & Wu, Wenfeng, 2011. "Cooking the books: Recipes and costs of falsified financial statements in China," Journal of Corporate Finance, Elsevier, vol. 17(2), pages 371-390, April.
    15. 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, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    16. Jeremy Bertomeu & Edwige Cheynel & Eric Floyd & Wenqiang Pan, 2021. "Using machine learning to detect misstatements," Review of Accounting Studies, Springer, vol. 26(2), pages 468-519, June.
    17. Mark L. DeFond & K. Raghunandan & K.R. Subramanyam, 2002. "Do Non–Audit Service Fees Impair Auditor Independence? Evidence from Going Concern Audit Opinions," Journal of Accounting Research, Wiley Blackwell, vol. 40(4), pages 1247-1274, September.
    18. Karpoff, Jonathan M. & Lee, D. Scott & Martin, Gerald S., 2008. "The Cost to Firms of Cooking the Books," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(3), pages 581-611, September.
    19. Luo, Jin-hui & Peng, Chenchen & Zhang, Xin, 2020. "The impact of CFO gender on corporate fraud: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 63(C).
    20. 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.
    21. Chunxin Jia & Shujun Ding & Yuanshun Li & Zhenyu Wu, 2009. "Fraud, Enforcement Action, and the Role of Corporate Governance: Evidence from China," Journal of Business Ethics, Springer, vol. 90(4), pages 561-576, December.
    22. Niu, Geng & Yu, Li & Fan, Gang-Zhi & Zhang, Donghao, 2019. "Corporate fraud, risk avoidance, and housing investment in China," Emerging Markets Review, Elsevier, vol. 39(C), pages 18-33.
    23. Canan C. Mutlu & Marc van Essen & Mike W. Peng & Sabrina F. Saleh & Patricio Duran, 2018. "Corporate Governance in China : A Meta-Analysis," Post-Print hal-02312124, HAL.
    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. 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.
    2. Zhang, Chanyuan (Abigail) & Cho, Soohyun & Vasarhelyi, Miklos, 2022. "Explainable Artificial Intelligence (XAI) in auditing," International Journal of Accounting Information Systems, Elsevier, vol. 46(C).
    3. Patrick Velte, 2023. "The link between corporate governance and corporate financial misconduct. A review of archival studies and implications for future research," Management Review Quarterly, Springer, vol. 73(1), pages 353-411, February.
    4. Dan Amiram & Zahn Bozanic & James D. Cox & Quentin Dupont & Jonathan M. Karpoff & Richard Sloan, 2018. "Financial reporting fraud and other forms of misconduct: a multidisciplinary review of the literature," Review of Accounting Studies, Springer, vol. 23(2), pages 732-783, June.
    5. Lars Helge Hass & Monika Tarsalewska & Feng Zhan, 2016. "Equity Incentives and Corporate Fraud in China," Journal of Business Ethics, Springer, vol. 138(4), pages 723-742, November.
    6. CAO, Ning & McGUINNESS, Paul B. & XI, Chao, 2021. "Does securities enforcement improve disclosure quality? An examination of Chinese listed companies' restatement activities," Journal of Corporate Finance, Elsevier, vol. 67(C).
    7. 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.
    8. Lukui Huang & Alan Abrahams & Peter Ractham, 2022. "Enhanced financial fraud detection using cost‐sensitive cascade forest with missing value imputation," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 133-155, July.
    9. 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.
    10. Wu, Fang & Cao, June & Zhang, Xiaosan, 2023. "Do non-executive employees matter in curbing corporate financial fraud?," Journal of Business Research, Elsevier, vol. 163(C).
    11. Achakzai, Muhammad Atif Khan & Juan, Peng, 2022. "Using machine learning Meta-Classifiers to detect financial frauds," Finance Research Letters, Elsevier, vol. 48(C).
    12. Elias Zavitsanos & Dimitris Mavroeidis & Konstantinos Bougiatiotis & Eirini Spyropoulou & Lefteris Loukas & Georgios Paliouras, 2023. "Financial misstatement detection: a realistic evaluation," Papers 2305.17457, arXiv.org.
    13. Yasheng Chen & Zhuojun Wu, 2022. "Financial Fraud Detection of Listed Companies in China: A Machine Learning Approach," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    14. Zhong, Xi & Ren, Liuyang & Song, Tiebo, 2021. "Different effects of internal and external tournament incentives on corporate financial misconduct: Evidence from China," Journal of Business Research, Elsevier, vol. 134(C), pages 329-341.
    15. Zou, Na, 2020. "Anticorruption efforts and corporate fraud," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224619, Verein für Socialpolitik / German Economic Association.
    16. Niu, Geng & Yu, Li & Fan, Gang-Zhi & Zhang, Donghao, 2019. "Corporate fraud, risk avoidance, and housing investment in China," Emerging Markets Review, Elsevier, vol. 39(C), pages 18-33.
    17. 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.
    18. Marie Herly & Nikolaj Niebuhr Lambertsen, 2023. "Restatement costs and reporting bias," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 50(1-2), pages 91-117, January.
    19. Haß, Lars Helge & Müller, Maximilian A. & Vergauwe, Skrålan, 2015. "Tournament incentives and corporate fraud," Journal of Corporate Finance, Elsevier, vol. 34(C), pages 251-267.
    20. 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.

    More about this item

    Keywords

    Fraud; Detection; Dynamic ensemble selection; Machine learning;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

    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:eee:finana:v:89:y:2023:i:c:s1057521923003435. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    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.