Skip to main content

Artificial Intelligence and Fraud Detection

  • Chapter
  • First Online:
Innovative Technology at the Interface of Finance and Operations

Part of the book series: Springer Series in Supply Chain Management ((SSSCM,volume 11))

Abstract

Fraud exists in all walks of life and detecting and preventing fraud represents an important research question relevant to many stakeholders in society. With the rise in big data and artificial intelligence, new opportunities have arisen in using advanced machine learning models to detect fraud. This chapter provides a comprehensive overview of the challenges in detecting fraud using machine learning. We use a framework (data, method, and evaluation criterion) to review some of the practical considerations that may affect the implementation of machine-learning models to predict fraud. Then, we review select papers in the academic literature across different disciplines that can help address some of the fraud detection challenges. Finally, we suggest promising future directions for this line of research. As accounting fraud constitutes an important class of fraud, we will discuss all of these issues within the context of accounting fraud detection.

We thank Kai Guo for research assistance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.wsj.com/articles/borrower-beware-credit-card-fraud-attempts-rise-during-the-coronavirus-crisis-11,590,571,800

  2. 2.

    https://www.technologyreview.com/2019/11/18/131912/6-essentials-for-fighting-fraud-with-machine-learning/

  3. 3.

    See Boute et al. (2022) for a more in-depth discussion on the use of AI in financial services.

  4. 4.

    In a 10-year review of corporate accounting fraud commissioned by the Committee of Sponsoring Organization of the Treadway Commission (COSO), Beasley et al. (2010) find that the total cumulative misstatement or misappropriation of nearly $120 billion across 300 fraud cases with available information (mean of nearly $400 million per case) (Beasley et al., 1999).

  5. 5.

    See SAS no. 99 (American Institute of Certified Public Accountants, 2002) for a discussion of this issue in a U.S. context.

  6. 6.

    https://www.federalreserve.gov/publications/files/changes-in-us-payments-fraud-from-2012-to-2016-20181016.pdf

  7. 7.

    See Zhang et al. (2015) for a good discussion of these issues.

  8. 8.

    https://spectrum.ieee.org/riskfactor/computing/software/michigans-midas-unemployment-system-algorithm-alchemy-that-created-lead-not-gold

  9. 9.

    https://www.freep.com/story/news/local/michigan/2017/07/30/fraud-charges-unemployment-jobless-claimants/516332001/

  10. 10.

    http://www.eurofinas.org/uploads/documents/Non-visible/Eurofinas-Accis_ReportOnFraud_WEB.pdf

  11. 11.

    https://www.corporatecomplianceinsights.com/the-growing-problem-of-corporate-fraud/

  12. 12.

    https://technode.com/2019/12/19/tencent-xiaomi-apps-called-out-for-illegal-data-collection/

  13. 13.

    Supervised models “learn” from labeled data. To train a supervised model, one presents both fraudulent and non-fraudulent records that have been labeled as such. Unsupervised models ask the model to “learn” the data structure on its own.

  14. 14.

    https://nvlpubs.nist.gov/nistpubs/ir/2019/NIST.IR.8280.pdf

  15. 15.

    https://www.ftc.gov/news-events/blogs/business-blog/2020/04/using-artificial-intelligence-algorithms

  16. 16.

    https://mit-insights.ai/6-essentials-for-fighting-fraud-with-machine-learning/

  17. 17.

    This section heavily relies on Bao et al. (2020).

  18. 18.

    https://www.technologyreview.com/2020/05/11/1001563/covid-pandemic-broken-ai-machine-learning-amazon-retail-fraud-humans-in-the-loop/

  19. 19.

    https://arxiv.org/abs/1808.03305

  20. 20.

    This section borrows heavily from the online appendix of Bao et al. (2020).

  21. 21.

    Recurrent neural networks are artificial neural networks where connections between nodes form a directed graph along a temporal sequence.

  22. 22.

    https://www.cio.com/article/3525877/serious-fraud-office-cto-ben-denison-reveals-how-ai-is-transforming-legal-work.html

  23. 23.

    https://customerthink.com/why-85-of-the-artificial-intelligence-projects-fail/

References

  • Abbasi, A., Albrecht, C., Vance, A., & Hansen, J. (2012). Metafraud: A meta-learning framework for detecting financial fraud. MIS Quarterly, 1293–1327.

    Google Scholar 

  • American Institute of Certified Public Accountants (2002) Consideration of fraud in a financial statement audit. Statement on Auditing Standards No. 99. New York.

    Google Scholar 

  • Amiram, D., Bozanic, Z., & Rouen, E. (2015). Financial statement errors: Evidence from the distributional properties of financial statement numbers. Review of Accounting Studies, 20, 1540–1593.

    Article  Google Scholar 

  • Ashton, R. H. (1974). Behavioral implications of information overload in managerial accounting reports. Cost and Management, 48(4), 37–40.

    Google Scholar 

  • Baltrušaitis, T., Ahuja, C., & Morency, L. P. (2018). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443.

    Article  Google Scholar 

  • Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting accounting fraud in publicly traded US firms using a machine learning approach. Journal of Accounting Research, 58(1), 199–235.

    Article  Google Scholar 

  • Beasley, M. S. (1996). An empirical analysis of the relation between the board of director composition and financial statement fraud. The Accounting Review, 71, 443–465.

    Google Scholar 

  • Beasley, M. S., Carcello, J. V., and Hermanson, D. R. (1999). Fraudulent financial reporting: 1987–1997: An Analysis of U.S. Public Companies. Sponsored by the Committee of Sponsoring Organizations of the Treadway Commission (COSO).

    Google Scholar 

  • Beasley, M. S., Carcello, J. V., Hermanson, D. R., and Neal, T. L. (2010). Fraudulent financial reporting: 1998–2007: An Analysis of U.S. Public Companies.” Sponsored by the Committee of Sponsoring Organizations of the Treadway Commission (COSO).

    Google Scholar 

  • Bekker, J., & Davis, J. (2020). Learning from positive and unlabeled data: A survey. Machine Learning, 109(4), 719–760.

    Article  Google Scholar 

  • Beneish, M. D. (1997). Detecting GAAP violation: Implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy, 16, 271–309.

    Article  Google Scholar 

  • Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55, 24–36.

    Article  Google Scholar 

  • Benbasat, I., & Taylor, R. N. (1982). Behavioral aspects of information processing for the design of management information systems. IEEE Transactions on Systems, Man, and Cybernetics, 12(4), 439–450.

    Article  Google Scholar 

  • Beutel, A., Akoglu, L., & Faloutsos, C. (2015). Graph-based user behavior modeling: from prediction to fraud detection. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. pp. 2309–2310.

    Google Scholar 

  • Brazdil, P., Carrier, C. G., Soares, C., & Vilalta, R. (2008). Metalearning: Applications to data mining. Springer Science & Business Media.

    Google Scholar 

  • Boute, R. N., Gijsbrechts, J., & Van Mieghem, J. A. (2022). Digital lean operations: Smart automation and artificial intelligence in financial services. In V. Babich, J. Birge, & G. Hilary (Eds.), Innovative technology at the interface of finance and operations. Springer Series in Supply Chain Management. Springer Nature.

    Google Scholar 

  • Brazel, J. F., Jones, K. L., & Zimbelman, M. F. (2009). Using nonfinancial measures to assess fraud risk. Journal of Accounting Research, 47(5), 1135–1166.

    Article  Google Scholar 

  • Brown, N. C., Crowley, R. M., & Elliott, W. B. (2020). What are you saying? Using topic to detect financial misreporting. Journal of Accounting Research, 58, 237–291.

    Article  Google Scholar 

  • Burns, N., & Kedia, S. (2006). The impact of performance-based compensation on misreporting. Journal of Financial Economics, 79, 35–67.

    Article  Google Scholar 

  • Cao, S., Yang, X., Chen, C., Zhou, J., Li, X., & Qi, Y. (2019). TitAnt: Online real-time transaction fraud detection in ant financial. arXiv. preprint arXiv:1906.07407.

    Google Scholar 

  • Chen, X., Hilary, G. and Tian, X. (2020). Mandatory data breach transparency and insider trading, working paper.

    Google Scholar 

  • Cecchini, M., Aytug, H., Koehler, G. J., & Pathak, P. (2010). Making words work: Using financial text as a predictor of financial events. Decision Support Systems, 50(1), 164–175.

    Article  Google Scholar 

  • Citron, D. K. (2008). Technological due process. Wash UL Rev, 85, 1249.

    Google Scholar 

  • Darrough, M., Huang, R., & Zhao, S. (2020). Spillover effects of fraud allegations and investor sentiment. Contemporary Accounting Research, 37, 982–1014.

    Article  Google Scholar 

  • Davidson, R., Dey, A., & Smith, A. (2015). Executives’ Boff-the-job^ behavior, corporate culture, and financial reporting risk. Journal of Financial Economics, 117(1), 5–28.

    Article  Google Scholar 

  • de Roux, D., Perez, B., Moreno, A., Villamil, M. D. P., & Figueroa, C. (2018) Tax fraud detection for under-reporting declarations using an unsupervised machine learning approach. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 215–222.

    Google Scholar 

  • Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. The Accounting Review, 70(2), 193–226.

    Google Scholar 

  • Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1996). Causes and consequences of earnings manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary Accounting Research, 13, 1–36.

    Article  Google Scholar 

  • Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011). Predicting material accounting misstatements. Contemporary Accounting Research, 28(1), 17–82.

    Article  Google Scholar 

  • Dong, W., Liao, S., & Zhang, Z. (2018). Leveraging financial social media data for corporate fraud detection. Journal of Management Information Systems, 35(2), 461–487.

    Article  Google Scholar 

  • Dutta, I., Dutta, S., & Raahemi, B. (2017). Detecting financial restatements using data mining techniques. Expert Systems with Applications, 90, 374–393.

    Article  Google Scholar 

  • Dyck, A., Morse, A., & Zingales, L. (2020). How pervasive is corporate fraud. University of Toronto. working paper.

    Google Scholar 

  • Efendi, J., Srivastava, A., & Swanson, E. P. (2007). Why do corporate managers misstate financial statements? The role of option compensation and other factors. Journal of Financial Economics, 85, 667–708.

    Article  Google Scholar 

  • Ernst & Young (2010). Driving ethical growth—New markets, new challenges. 11th Global Fraud Survey. from https://linomartins.files.wordpress.com/2011/12/2011th_global_fraud_survey.pdf.

  • Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27, 861–874.

    Article  Google Scholar 

  • Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448–455.

    Article  Google Scholar 

  • Fletcher, H., Glancy, & Yadav, S. B. (2011). A computational model for financial reporting fraud detection. Decision Support Systems, 50(3), 595–601.

    Article  Google Scholar 

  • Garip, F. (2020). What failure to predict life outcomes can teach us. Proceedings of the National Academy of Sciences, 117(15), 8234–8235.

    Article  Google Scholar 

  • Green, P., & Choi, J. H. (1997). Assessing the risk of management fraud through neural network technology. Auditing: A Journal of Practice & Theory, 16, 14–29.

    Google Scholar 

  • Guo, J., Liu, G., Zuo, Y., & Wu, J. (2018). Learning sequential behavior representations for fraud detection. 2018 IEEE international conference on data mining (ICDM). IEEE, pp. 127–136.

    Google Scholar 

  • Hajek, P., & Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud–a comparative study of machine learning methods. Knowledge-Based Systems, 128, 139–152.

    Article  Google Scholar 

  • Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning. Springer.

    Book  Google Scholar 

  • He, H., & Ma, Y. (2013). Imbalanced learning: Foundations, algorithms, and applications. Wiley.

    Book  Google Scholar 

  • Healy, P. M. (1985). The effect of bonus schemes on accounting decisions. Journal of Accounting and Economics, 7(1), 85–107.

    Article  Google Scholar 

  • Hobson, J. L., Mayew, W. J., & Venkatachalam, M. (2012). Analyzing speech to detect financial misreporting. Journal of Accounting Research, 50(2), 349–392.

    Article  Google Scholar 

  • Hoi, S. C., Sahoo, D., Lu, J., & Zhao, P. (2018). Online learning: A comprehensive survey. arXiv preprint arXiv:1802.02871.

    Google Scholar 

  • Hu, B., Zhang, Z., Shi, C., Zhou, J., Li, X., & Qi, Y. (2019). Cash-out user detection based on attributed heterogeneous information network with a hierarchical attention mechanism. Proceedings of the AAAI Conference on Artificial Intelligence. pp. 946–953.

    Google Scholar 

  • Humpherys, S. L., Moffitt, K. C., Burns, M. B., Burgoon, J. K., & Felix, W. F. (2011). Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems, 50(3), 585–594.

    Article  Google Scholar 

  • Iselin, E. R. (1988). The effects of information load and information diversity on decision quality in a structured decision task. Accounting, Organizations and Society, 13(2), 147–164.

    Article  Google Scholar 

  • Järvelin, K., & Kekäläinen, J. (2002). Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems, 20, 422–446.

    Article  Google Scholar 

  • Johnson, S. A., Ryan, H. E., & Tian, Y. S. (2009). Managerial incentives and corporate fraud: The sources of incentives matter. Review of Finance, 13, 115–145.

    Article  Google Scholar 

  • Karpoff, J. M., Lee, D. S., & Martin, G. S. (2008). The costs to firms of cooking the books. Journal of Financial and Quantitative Analysis, 43(03), 581–612.

    Article  Google Scholar 

  • Karpoff, J. M., Koester, A., Lee, D. S., & Martin, G. S. (2017). Proxies and databases in financial misconduct research. The Accounting Review, 92(6), 129–163.

    Article  Google Scholar 

  • Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction policy problems. American Economic Review: Papers & Proceedings, 105(5), 491–495.

    Article  Google Scholar 

  • KPMG. Peat Marwick (1998). Fraud Survey. KPMG Peat Marwick.

    Google Scholar 

  • Larcker, D. F., Richardson, S. A., & Tuna, I. (2007). Corporate governance, accounting outcomes, and organizational performance. The Accounting Review, 82(4), 963–1008.

    Article  Google Scholar 

  • Larcker, D., & Zakolyukina, A. A. (2012). Detecting deceptive discussion in conference calls. Journal of Accounting Research, 50, 495–540.

    Article  Google Scholar 

  • Li, H., Liu, B., Mukherjee, A., & Shao, J. (2014). Spotting fake reviews using positive-unlabeled learning. Computación y Sistemas, 18(3), 467–475.

    Article  Google Scholar 

  • Liang, C., Liu, Z., Liu, B., Zhou, J., Li, X., and Yang, S. (2019). Uncovering Insurance Fraud Conspiracy with Network Learning. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 1181–1184.

    Google Scholar 

  • Lin, J., Hwang, M., & Becker, J. (2003). A fuzzy neural network for assessing the risk of fraudulent financial reporting. Managerial Auditing Journal, 18, 657–665.

    Article  Google Scholar 

  • Liu, S., Hooi, B., & Faloutsos, C. (2019). A contrast metric for fraud detection in rich graphs. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2235–2248.

    Article  Google Scholar 

  • Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569.

    Article  Google Scholar 

  • Oentaryo, R., Lim, E.-P., Finegold, M., Lo, D., Zhu, F., Phua, C., et al. (2014). Detecting click fraud in online advertising: A data mining approach. The Journal of Machine Learning Research, 15(1), 99–140.

    Google Scholar 

  • Perols, J. L., Bowen, R. M., Zimmermann, C., & Samba, B. (2017). Finding needles in a haystack: Using data analytics to improve fraud prediction. The Accounting Review, 92, 221–245.

    Article  Google Scholar 

  • Purda, L., & Skillicorn, D. (2015). Accounting variables, deception, and a bag of words: Assessing the tools of fraud detection. Contemporary Accounting Research, 32(3), 1193–1223.

    Article  Google Scholar 

  • Salganik, M., Lundberg, I., Kindel, A., Ahearn, C., Al-Ghoneim, K. Almaatouq, A., Altschul, D., Brand, J., Carnegie, N., Compton, R, Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B., Jahani, E., Kashyap, R., Kirchner, A., Mckay, S. (2020). Measuring the predictability of life outcomes with a scientific mass collaboration. Proceedings of the National Academy of Sciences. 117.

    Google Scholar 

  • Shah, N., Lamba, H., Beutel, A., & Faloutsos, C. (2017). The many faces of link fraud. 2017 IEEE International Conference on Data Mining (ICDM). IEEE, pp. 1069–1074.

    Google Scholar 

  • Shmueli, G. (2010). To explain or to predict. Statistical Science, 25, 289–310.

    Article  Google Scholar 

  • Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2017). Gotcha! Network-based fraud detection for social security fraud. Management Science, 63(9), 3090–3110.

    Article  Google Scholar 

  • Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28, 3–28.

    Article  Google Scholar 

  • Wang, D., Lin, J., Cui, P., Jia, Q., Wang, Z., Fang, Y., et al. (2019a). A Semi-supervised Graph Attentive Network for Financial Fraud Detection. 2019 IEEE International Conference on Data Mining (ICDM). IEEE, pp. 598–607.

    Google Scholar 

  • Wang Y., Wang L., Li Y., He D., Chen W., Liu T.-Y. (2013). A Theoretical Analysis of NDCG Ranking Measures. In Proceedings of the 26th Annual Conference on Learning Theory.

    Google Scholar 

  • Wang, J., Wen, R., Wu, C., Huang, Y., & Xion, J. (2019b). Fdgars: Fraudster detection via graph convolutional networks in online app review system. Companion Proceedings of The 2019 World Wide Web Conference. pp. 310–316.

    Google Scholar 

  • Wang, Y., & Xu, W. (2018). Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decision Support Systems, 105, 87–95.

    Article  Google Scholar 

  • Whiting, D. G., Hansen, J. V., McDonald, J. B., Albrecht, C., & Albrecht, W. S. (2012). Machine learning methods for detecting patterns of management fraud. Computational Intelligence, 28, 505–527.

    Article  Google Scholar 

  • Xu, C., Zhang, J., & Sun, Z. (2017). Online reputation fraud campaign detection in user ratings. IJCAI, 3873–3879.

    Google Scholar 

  • Yuan, S., Wu, X., Li, J., & Lu, A. (2017) Spectrum-based deep neural networks for fraud detection. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. pp. 2419–2422.

    Google Scholar 

  • Zhang, J., Yang, X., & Appelbaum, D. (2015). Toward effective big data analysis in continuous auditing. Accounting Horizons, 29(2), 469–476.

    Article  Google Scholar 

  • Zhang, Y.-L., Zhou, J., Zheng, W., Feng, J., Li, L., Liu, Z., et al. (2019). Distributed deep forest and its application to automatic detection of cash-out fraud. ACM Transactions on Intelligent Systems and Technology (TIST), 10(5), 1–19.

    Google Scholar 

  • Zheng, P., Yuan, S., Wu, X., Li, J., & Lu, A. (2019) One-class adversarial nets for fraud detection. Proceedings of the AAAI Conference on Artificial Intelligence. pp. 1286–1293.

    Google Scholar 

  • Zhong, Q., Liu, Y., Ao, X., Hu, B., Feng, J., Tang, J., et al. (2020). Financial defaulter detection on online credit payment via multi-view attributed heterogeneous information network. Proceedings of The Web Conference 2020. pp. 785–795.

    Google Scholar 

  • Zhu, Y., Xi, D., Song, B., Zhuang, F., Chen, S., Gu, X., et al. (2020) Modeling Users’ Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection. Proceedings of The Web Conference 2020. pp. 928–938.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Editors

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bao, Y., Hilary, G., Ke, B. (2022). Artificial Intelligence and Fraud Detection. In: Babich, V., Birge, J.R., Hilary, G. (eds) Innovative Technology at the Interface of Finance and Operations. Springer Series in Supply Chain Management, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-75729-8_8

Download citation

Publish with us

Policies and ethics