IDEAS home Printed from https://ideas.repec.org/a/eee/bracre/v57y2025i1s0890838925000101.html
   My bibliography  Save this article

Reprint of: The use of machine learning algorithms to predict financial statement fraud

Author

Listed:
  • Lokanan, Mark
  • Sharma, Satish

Abstract

No abstract is available for this item.

Suggested Citation

  • Lokanan, Mark & Sharma, Satish, 2025. "Reprint of: The use of machine learning algorithms to predict financial statement fraud," The British Accounting Review, Elsevier, vol. 57(1).
    • Handle: RePEc:eee:bracre:v:57:y:2025:i:1:s0890838925000101
    DOI: 10.1016/j.bar.2025.101560
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0890838925000101
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.bar.2025.101560?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

    for a different version of it.

    References listed on IDEAS

    as
    1. repec:eme:maj000:maj-02-2015-1163 is not listed on IDEAS
    2. 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.
    3. Xiang Gao & Junhao Wen & Cheng Zhang, 2019. "An Improved Random Forest Algorithm for Predicting Employee Turnover," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, April.
    4. Lidia Ceriani & Paolo Verme, 2012. "The origins of the Gini index: extracts from Variabilità e Mutabilità (1912) by Corrado Gini," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(3), pages 421-443, September.
    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. Mitchell, A. & Sikka, P. & Willmott, H., 1998. "Sweeping it under the carpet: The role of accountancy firms in moneylaundering," Accounting, Organizations and Society, Elsevier, vol. 23(5-6), pages 589-607.
    7. Kirsty Rae & Nava Subramaniam, 2008. "Quality of internal control procedures: Antecedents and moderating effect on organisational justice and employee fraud," Managerial Auditing Journal, Emerald Group Publishing, vol. 23(2), pages 104-124, January.
    8. Borislava Petrova Vrigazova & Ivan Ganchev Ivanov, 2020. "The bootstrap procedure in classification problems," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 12(4), pages 428-446.
    9. repec:hal:journl:halshs-03162185 is not listed on IDEAS
    10. Rodney L. Crawford & Thomas R. Weirich, 2011. "Fraud guidance for corporate counsel reviewing financial statements and reports," Journal of Financial Crime, Emerald Group Publishing Limited, vol. 18(4), pages 347-360, October.
    11. van der Heijden, Hans, 2022. "Predicting industry sectors from financial statements: An illustration of machine learning in accounting research," The British Accounting Review, Elsevier, vol. 54(5).
    12. Patrick Kelly & Carol A. Hartley, 2010. "Casino gambling and workplace fraud: a cautionary tale for managers," Management Research Review, Emerald Group Publishing Limited, vol. 33(3), pages 224-239, March.
    13. Davis, Jon S. & Pesch, Heather L., 2013. "Fraud dynamics and controls in organizations," Accounting, Organizations and Society, Elsevier, vol. 38(6), pages 469-483.
    14. 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.
    15. Olukayode Abayomi Sorunke, 2016. "Personal Ethics and Fraudster Motivation: The Missing Link in Fraud Triangle and Fraud Diamond Theories," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, vol. 6(2), pages 159-165, February.
    16. Dellaportas, Steven, 2013. "Conversations with inmate accountants: Motivation, opportunity and the fraud triangle," Accounting forum, Elsevier, vol. 37(1), pages 29-39.
    17. Pamela Murphy & M. Dacin, 2011. "Psychological Pathways to Fraud: Understanding and Preventing Fraud in Organizations," Journal of Business Ethics, Springer, vol. 101(4), pages 601-618, July.
    18. Harold Hassink & Roger Meuwissen & Laury Bollen, 2010. "Fraud detection, redress and reporting by auditors," Managerial Auditing Journal, Emerald Group Publishing, vol. 25(9), pages 861-881, October.
    19. 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.
    20. Aldo Levy & Riad Baha, 2021. "Credit risk assessment: a comparison of the performances of the linear discriminant analysis and the logistic regression," International Journal of Entrepreneurship and Small Business, Inderscience Enterprises Ltd, vol. 42(1/2), pages 169-186.
    21. 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.
    22. Shane A. Johnson & Harley E. Ryan & Yisong S. Tian, 2009. "Managerial Incentives and Corporate Fraud: The Sources of Incentives Matter," Review of Finance, European Finance Association, vol. 13(1), pages 115-145.
    23. Clinton Free & Pamela R. Murphy, 2015. "The Ties that Bind: The Decision to Co†Offend in Fraud," Contemporary Accounting Research, John Wiley & Sons, vol. 32(1), pages 18-54, March.
    24. Mark E. Lokanan, 2015. "Challenges to the fraud triangle: Questions on its usefulness," Accounting Forum, Taylor & Francis Journals, vol. 39(3), pages 201-224, September.
    25. James L. Bierstaker & Inshik Seol & Patrick Kelly & Carol A. Hartley, 2010. "Casino gambling and workplace fraud: a cautionary tale for managers," Management Research Review, Emerald Group Publishing Limited, vol. 33(3), pages 224-239, March.
    26. Sawsan Saadi Halbouni & Nada Obeid & Abeer Garbou, 2016. "Corporate governance and information technology in fraud prevention and detection: Evidence from the UAE," Managerial Auditing Journal, Emerald Group Publishing, vol. 31(6/7), pages 589-628, June.
    27. Power, Michael, 2013. "The apparatus of fraud risk," Accounting, Organizations and Society, Elsevier, vol. 38(6), pages 525-543.
    28. Gullkvist, Benita & Jokipii, Annukka, 2013. "Perceived importance of red flags across fraud types," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 24(1), pages 44-61.
    29. Sawsan Saadi Halbouni & Nada Obeid & Abeer Garbou, 2016. "Corporate governance and information technology in fraud prevention and detection," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 31(6/7), pages 589-628, June.
    30. Xin‐Ping Song & Zhi‐Hua Hu & Jian‐Guo Du & Zhao‐Han Sheng, 2014. "Application of Machine Learning Methods to Risk Assessment of Financial Statement Fraud: Evidence from China," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 611-626, December.
    31. repec:eme:maj000:02686901011080044 is not listed on IDEAS
    32. Craja, Patricia & Kim, Alisa & Lessmann, Stefan, 2020. "Deep Learning application for fraud detection in financial statements," IRTG 1792 Discussion Papers 2020-007, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    33. Harold Hassink & Roger Meuwissen & Laury Bollen, 2010. "Fraud detection, redress and reporting by auditors," Managerial Auditing Journal, Emerald Group Publishing Limited, vol. 25(9), pages 861-881, October.
    34. Murphy, Pamela R., 2012. "Attitude, Machiavellianism and the rationalization of misreporting," Accounting, Organizations and Society, Elsevier, vol. 37(4), pages 242-259.
    35. Steven Dellaportas, 2013. "Conversations with inmate accountants: Motivation, opportunity and the fraud triangle," Accounting Forum, Taylor & Francis Journals, vol. 37(1), pages 29-39, March.
    36. Aldo Lévy & Riad Baha, 2021. "Credit risk assessment: a comparison of the performances of the linear discriminant analysis and the logistic regression [Évaluation du risque de crédit des PME: Une approche comparative des perfor," Post-Print hal-04553429, HAL.
    37. Simon Deakin & Suzanne J. Konzelmann, 2004. "Learning from Enron," Corporate Governance: An International Review, Wiley Blackwell, vol. 12(2), pages 134-142, April.
    38. Rabi’u Abdullahi & Noorhayati Mansor, 2015. "Fraud Triangle Theory and Fraud Diamond Theory. Understanding the Convergent and Divergent For Future Research," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 5(4), pages 38-45, October.
    39. Alexander Schuchter & Michael Levi, 2015. "Beyond the fraud triangle: Swiss and Austrian elite fraudsters," Accounting Forum, Taylor & Francis Journals, vol. 39(3), pages 176-187, September.
    40. Patrick Kelly & Carol A. Hartley, 2010. "Casino gambling and workplace fraud: a cautionary tale for managers," Management Research Review, Emerald Group Publishing Limited, vol. 33(3), pages 224-239, March.
    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. Mark E. Lokanan & Prerna Sharma, 2023. "Two Decades of Accounting Fraud Research: The Missing Meso-Level Analysis," SAGE Open, , vol. 13(3), pages 21582440231, September.
    2. Monica Ramos Montesdeoca & Agustín J. Sánchez Medina & Felix Blázquez Santana, 2019. "Research Topics in Accounting Fraud in the 21st Century: A State of the Art," Sustainability, MDPI, vol. 11(6), pages 1-31, March.
    3. 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.
    4. Saffet A. Uygur & Christopher J. Napier, 2024. "Understanding Fraud in the Not-For-Profit Sector: A Stakeholder Perspective for Charities," Journal of Business Ethics, Springer, vol. 190(3), pages 569-588, March.
    5. V.U. Imagbe & T.O. Abiloro & G.A. Saheed, 2019. "Fraud Diamond and Financial Crimes in Nigerian Banking Industries," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 9(4), pages 294-303, October.
    6. Yin, Lei & Sun, Guanglin & Kong, Tao, 2025. "Regional big data development and corporate financial fraud," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    7. Hengky Latan & Charbel Jose Chiappetta Jabbour & Ana Beatriz Lopes de Sousa Jabbour, 2019. "‘Whistleblowing Triangle’: Framework and Empirical Evidence," Journal of Business Ethics, Springer, vol. 160(1), pages 189-204, November.
    8. Rahman, Md Jahidur & Zhu, Hongtao, 2024. "Detecting accounting fraud in family firms: Evidence from machine learning approaches," Advances in accounting, Elsevier, vol. 64(C).
    9. 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.
    10. Waseem Yousaf & Raheel Jamil, 2020. "Does Moral Intensity Affect the Whistleblowing Intentions?," iRASD Journal of Management, International Research Alliance for Sustainable Development (iRASD), vol. 2(2), pages 84-99, December.
    11. Zhao, Dongshuai & Wang, Zhongli & Schweizer-Gamborino, Florian & Sornette, Didier, 2025. "Polytope Fraud Theory," International Review of Financial Analysis, Elsevier, vol. 97(C).
    12. 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.
    13. Bhattacharya, Indranil & Mickovic, Ana, 2024. "Accounting fraud detection using contextual language learning," International Journal of Accounting Information Systems, Elsevier, vol. 53(C).
    14. 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.
    15. Said, Jamaliah & Alam, Md. Mahmudul & Ramli, Masitah & Rafidi, Marhamah, 2020. "Integrating Ethical Values into Fraud Triangle Theory in Assessing Employee Fraud: Evidence from the Malaysian Banking Industry," SocArXiv xpqrs_v1, Center for Open Science.
    16. 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).
    17. 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).
    18. 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.
    19. Dayah Abdi Kulmie, 2023. "Financial Crimes in Somali Public Sector: Causes and Consequences," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 15(4), pages 1-18, April.
    20. 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.

    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:eee:bracre:v:57:y:2025:i:1:s0890838925000101. 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: https://www.journals.elsevier.com/the-british-accounting-review .

    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.