Fraud Analytics as a Sequential Decision System: Integrating Machine Learning, Optimization, and Adversarial Learning

Published Online:https://doi.org/10.1287/educ.2026.0299

References

  • [1] H. Bastani, J. Goh, and M. Bayati. Evidence of upcoding in pay-for-performance programs. Management Science 65(3):1042–1060, 2018.Link, Google Scholar
  • [2] J. O. Berger. Statistical Decision Theory and Bayesian Analysis. Springer Science & Business Media, New York, 2013.Google Scholar
  • [3] B. Biggio and F. Roli. Wild patterns: Ten years after the rise of adversarial machine learning. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. ACM, New York, 2154–2156, 2018.Google Scholar
  • [4] D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research 3:993–1022, 2003. Google Scholar
  • [5] R. Bolton and D. Hand. Statistical fraud detection: A review. Statistical Science 17(3):235–249. 2002.Crossref, Google Scholar
  • [6] L. Breiman. Random forests. Machine Learning 45(1):5–32, 2001.Crossref, Google Scholar
  • [7] G. Brown, M. Carlyle, J. Salmerón, and K. Wood. Defending critical infrastructure. Interfaces 36(6):530–544, 2006.Link, Google Scholar
  • [8] M. Butler, Y. Fan, and C. Faloutsos. Fraudfox: Adaptable fraud detection in the real world. G. Wang, A. Ciptadi, and A. Ahmadzadeh, eds. Proceedings of the International Workshop on Deployable Machine Learning for Security Defense. Springer, Cham, Switzerland, 47–65, 2020.Crossref, Google Scholar
  • [9] L. F. Carvalho, C. H. Teixeira, W. Meira, M. Ester, O. Carvalho, and M. H. Brandao. Provider-consumer anomaly detection for healthcare systems. Proc. 2017 IEEE International Conference on Healthcare Informatics. IEEE, Piscataway, NJ, 229–238, 2017.Google Scholar
  • [10] P. K. Chan, W. Fan, A. L. Prodromidis, and S. J. Stolfo. Distributed data mining in credit card fraud detection. IEEE Intelligent Systems 14(6):67–74, 1999.Crossref, Google Scholar
  • [11] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–357, 2002.Crossref, Google Scholar
  • [12] A. E. Cinà, K. Grosse, A. Demontis, S. Vascon, W. Zellinger, B. A. Moser, A. Oprea, et al. Wild patterns reloaded: A survey of machine learning security against training data poisoning. ACM Computing Surveys 55(13s):1–39, 2023.Crossref, Google Scholar
  • [13] CMS. CMS NHE Fact Sheet. Access April 1, 2026, https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data/nhe-fact-sheet, 2026.Google Scholar
  • [14] CMS (2014) Report to congress fraud prevention system second implementation year. Accessed March 10, https://www.cms.gov/About-CMS/Components/CPI/Widgets/Fraud_Prevention_System_2ndYear.pdf.Google Scholar
  • [15] L. Delamaire, H. Abdou, and J. Pointon. Credit card fraud and detection techniques: A review. Banks and Bank Systems 4(2):57–68, 2009.Google Scholar
  • [16] Y. Deng, J. Liu, and S. Sen. Coalescing data and decision sciences for analytics. E. Gel and L. Ntaimo, eds. Recent Advances in Optimization and Modeling of Contemporary Problems, INFORMS, Catonsville, MD, 20–49, 2018.Link, Google Scholar
  • [17] G. Dionne, F. Giuliano, and P. Picard. Optimal auditing with scoring: Theory and application to insurance fraud. Management Science 55(1):58–70, 2009.Link, Google Scholar
  • [18] C. Durtschi, W. Hillison, and C. Pacini. The effective use of Benford’s law to assist in detecting fraud in accounting data. Journal of Forensic Accounting 5(1):17–34, 2004.Google Scholar
  • [19] T. Ekin. Statistics and Health Care Fraud: How to Save Billions. CRC Press, Boca Raton, FL, 2019.Crossref, Google Scholar
  • [20] T. Ekin. Adversarial outlier detection methods for health care fraud. Proceedings of the 29th Americas Conference on Information Systems. AIS Electronic Library (AISeL), 1–5, 2023.Google Scholar
  • [21] T. Ekin and K. S. Mandadapu. Red-teaming for health care fraud detection: A RAG-based AI approach. Proceedings of the 2025 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), IEEE, Piscataway, NJ, 1–6, 2025.Google Scholar
  • [22] T. Ekin and R. Musal. Integrated statistical and decision models for multi-stage health care audit sampling. Journal of Applied Statistics 49(9):2307–2325, 2022.Crossref, Google Scholar
  • [23] T. Ekin, L. Frigau, and C. Conversano. Health care fraud classifiers in practice. Applied Stochastic Models in Business and Industry 37(6):1182–1199, 2021.Crossref, Google Scholar
  • [24] T. Ekin, G. Lakomski, and R. M. Musal. An unsupervised Bayesian hierarchical method for medical fraud assessment. Statistical Analysis and Data Mining: The ASA Data Science Journal 12(2):116–124, 2019.Crossref, Google Scholar
  • [25] T. Ekin, R. Naveiro, and J. M. C. Rodriguez. Adversarial forecasting through adversarial risk analysis within a DDDAS framework. E. Blasch, F. Darema, and A. Aved, eds. Proceedings of the International Conference on Dynamic Data Driven Applications Systems. Springer, Cham, Switzerland, 293–301, 2022.Google Scholar
  • [26] T. Ekin, L. Shaw, and V. Surya Bellamkonda. Adversarial attacks and data-driven dynamic outlier detection systems. E. Blasch, F. Darema, and D. Metaxas, eds. Proceedings of the International Conference on Dynamic Data Driven Applications Systems. Springer Nature, Cham, Switzerland, 208–215, 2024.Google Scholar
  • [27] T. Ekin, L. Shaw, and S. Bellamkonda (2026) Proactive defenses for data-driven dynamic outlier detection. F. Darema, E. P. Blasch, S. Ravela, and A. J. Aved, eds. Handbook of Dynamic Data Driven Applications Systems, vol. 4. Springer Nature, Cham, Switzerland, 1–18.Google Scholar
  • [28] T. Ekin, F. Ieva, F. Ruggeri, and R. Soyer. On the use of the concentration function in medical fraud assessment. The American Statistician 71(3):236–241, 2017.Crossref, Google Scholar
  • [29] T. Ekin, F. Ieva, F. Ruggeri, and R. Soyer. Statistical medical fraud assessment: Exposition to an emerging field. International Statistical Review 86(3):379–402, 2018.Crossref, Google Scholar
  • [30] T. Ekin, R. Naveiro, D. R. Insua, and A. Torres-Barrán. Augmented probability simulation methods for sequential games. European Journal of Operational Research 306(1):418–430, 2023.Crossref, Google Scholar
  • [31] A. Fernández, S. del Río, N. V. Chawla, and F. Herrera. An insight into imbalanced big data classification: Outcomes and challenges. Complex & Intelligent Systems 3(2):105–120, 2017.Crossref, Google Scholar
  • [32] J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics 29(5):1189–1232, 2001.Google Scholar
  • [33] G. Haixiang, L. Yijing, J. Shang, G. Mingyun, H. Yuanyue, and G. Bing. Learning from class-imbalanced data: Review of methods and applications. Expert Systems with Applications 73:220–239, 2017.Crossref, Google Scholar
  • [34] C. Haseltine and L. A. Albert. Modeling with attack graphs for securing cyber-physical systems. B. Denton, N. Noyan, and Y. Xie, eds. Tutorials in Operations Research: Advances in Analytics and Operations Research: Improving Decisions to Secure the Future. INFORMS Catonsville, MD, 1–27, 2025.Link, Google Scholar
  • [35] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media, New York, 2009.Crossref, Google Scholar
  • [36] H. He and E. A. Garcia, Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 21(9):1263–1284, 2009.Crossref, Google Scholar
  • [37] T. Hillerman, J. C. F. Souza, A. C. B. Reis, and R. N. Carvalho. Applying clustering and AHP methods for evaluating suspect healthcare claims. Journal of Computational Science 19:97–111, 2017.Crossref, Google Scholar
  • [38] N. Justin, Q. Sun, A. Gómez, and P. Vayanos. Responsible machine learning via mixed-integer optimization. B. Denton, N. Noyan, and Y. Xie, eds. Tutorials in Operations Research: Advances in Analytics and Operations Research: Improving Decisions to Secure the Future. INFORMS, Catonsville, MD, 184–230, 2025.Link, Google Scholar
  • [39] P. Kalb. Health care fraud and abuse. The Journal of the American Medical Association 282(12):1163–1168, 1999.Crossref, Google Scholar
  • [40] S. Kapoor, A. Gil, S. Bhaduri, A. Mittal, and R. Mulkar. Qualitative insights tool (qualit): LLM enhanced topic modeling. Preprint, submitted September 24, https://arxiv.org/abs/2409.15626, 2024.Google Scholar
  • [41] Y. Kim, A. Benvenuti, B. Chen, M. Karabag, A. Kulkarni, N. D. Bastian, U. Topcu, et al.. Defining and measuring deception in sequential decision systems: Application to network defense. Proceedings of the MILCOM 2024-2024 IEEE Military Communications Conference, IEEE, Piscataway, NJ, 1–6, 2024.Crossref, Google Scholar
  • [42] B. Krawczyk, Learning from imbalanced data: Open challenges and future directions. Progress in Artificial Intelligence 5(4):221–232, 2016.Crossref, Google Scholar
  • [43] N. Kumaraswamy, M. K. Markey, T. Ekin, J. C. Barner, and K. Rascati. Healthcare fraud data mining methods: A look back and look ahead. Perspectives in Health Information Management 19(1), Article no. 1i, 2022.Google Scholar
  • [44] H. Lakkaraju, J. Kleinberg, J. Leskovec, J. Ludwig, and S. Mullainathan. The selective labels problem: Evaluating algorithmic predictions in the presence of unobservables. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, 275–284, 2017.Google Scholar
  • [45] D. V. Lindley. On a measure of the information provided by an experiment. The Annals of Mathematical Statistics 27(4):986–1005, 1956.Crossref, Google Scholar
  • [46] Y. Lucas and J. Jurgovsky. Credit card fraud detection using machine learning: A survey. Preprint, submitted October 13, https://arxiv.org/abs/2010.06479, 2020.Google Scholar
  • [47] B. Luo, Z. Zhang, Q. Wang, A. Ke, S. Lu, and B. He. AI-powered fraud detection in decentralized finance: A project life cycle perspective. ACM Computing Surveys 57(4):1–38, 2024.Crossref, Google Scholar
  • [48] A. Luque, A. Carrasco, A. Martín, and A. de las Heras. The impact of class imbalance in classification performance metrics based on the binary confusion matrix. Pattern Recognition 91:216–231, 2019.Crossref, Google Scholar
  • [49] A. Mead, T. Lewris, S. Prasanth, S. Adams, P. Alonzi, and P. Beling. Detecting fraud in adversarial environments: A reinforcement learning approach. Proceedings of the 2018 Systems and Information Engineering Design Symposium. IEEE, Piscataway, NJ, 118–122, 2018.Google Scholar
  • [50] E. Modak, M. Bakshi, B. K. Dey, and V. M. Prabhakaran. Sequential adversarial hypothesis testing. Proceedings of the 2024 IEEE International Symposium on Information Theory. IEEE, Piscataway, NJ, 1474–1479, 2024.Google Scholar
  • [51] Y. Mu, C. Dong, K. Bontcheva, and X. Song. Large language models offer an alternative to the traditional approach of topic modelling. N. Calzolari, M. -Y. Kan, V. Hoste, A. Lenci, S. Sakti, and M. Xue, eds. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation. ELRA and ICCL, Torino, Italia, 10160–10171, 2024.Google Scholar
  • [52] National Health Care Anti-Fraud Association. The challenge of health care fraud. Accessed April 1, 2026, https://www.nhcaa.org/tools-insights/about-health-care-fraud/the-challenge-of-health-care-fraud/, 2025.Google Scholar
  • [53] R. Naveiro, A. Redondo, D. R. Insua, and F. Ruggeri. Adversarial classification: An adversarial risk analysis approach. International Journal of Approximate Reasoning 113:133–148, 2019.Crossref, Google Scholar
  • [54] E. Ngai, Y. Hu, Y. Wong, Y. Chen, and X. Sun. 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, 2011.Crossref, Google Scholar
  • [55] C. Phua, V. Lee, K. Smith, and R. Gayler. A comprehensive survey of data mining-based fraud detection research. Preprint, submitted September 30, https://arxiv.org/abs/1009.6119, 2010.Google Scholar
  • [56] W. B. Powell. Clearing the jungle of stochastic optimization. A. Newman and J. Leung, eds. Tutorials in Operations Research: Bridging Data and Decisions. INFORMS, Catonsville, MD, 109–137, 2014.Link, Google Scholar
  • [57] M. Qi and Z. J. Shen. Integrating prediction/estimation and optimization with applications in operations management. M. C. Chou, H. Gibson, and B. R. Staats, eds. Tutorials in Operations Research: Emerging and Impactful Topics in Operations. INFORMS, Catonsville, MD, 36–58, 2022.Link, Google Scholar
  • [58] D. Rios Insua, J. Rios, and D. Banks. Adversarial risk analysis. Journal of the American Statistical Association 104(486):841–854, 2009.Crossref, Google Scholar
  • [59] M. E. Roberts, B. M. Stewart, D. Tingley, E. M. Airoldi, et al. The structural topic model and applied social science. Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation, vol. 4. Harrahs and Harveys, Lake Tahoe, 1–20, 2013.Google Scholar
  • [60] N. J. Sarna, F. A. Rithen, U. S. Jui, S. Belal, A. Amin, T. K. Oishee, and A. M. Islam. AI driven fraud detection models in financial networks: A review. IEEE Access 13:141204–141233, 2025.Crossref, Google Scholar
  • [61] L. Shaw and T. Ekin. Robust sentiment analysis in service systems: Enhancing XLNet with adversarial training. T. X. Bui, ed. Proceedings of the 59th Hawaii International Conference on System Sciences. ScholarSpace, University of Hawaiʻi, Mānoa, Honolulu, 1043–1052, 2026.Google Scholar
  • [62] L. Shaw, M. W. Ansari, and T. Ekin. Adversarial natural language processing: Overview, challenges, and policy implications. Data & Policy 7:e64, 2025.Crossref, Google Scholar
  • [63] M. Sparrow. License to Steal: Why Fraud Plagues America’s Health Care System. Westview Press, Boulder, CO, 1996.Google Scholar
  • [64] A. Srivastava, A. Kundu, S. Sural, and A. Majumdar. Credit card fraud detection using hidden Markov model. IEEE Transactions on Dependable and Secure Computing 5(1):37–48, 2008.Crossref, Google Scholar
  • [65] X. Tang, A. Marques, P. Kamalaruban, and I. Bogunovic. Adversarially robust decision transformer. Advances in Neural Information Processing systems 37:29697–29722, 2024.Crossref, Google Scholar
  • [66] M. Tang, B. S. U. Mendis, D. W. Murray, Y. Hu, and A. Sutinen. Unsupervised fraud detection in Medicare Australia. P. Vamplew, A. Stranieri, K. -L. Ong, P. Christen, and P. J. Kennedy, eds. Proceedings of the Ninth Australasian Data Mining Conference-Volume 121. Australian Computer Society, Inc. Sydney, Australia, 103–110, 2011.Google Scholar
  • [67] L. Torgo and E. Lopes. Utility-based fraud detection. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 2. AAAI Press, Menlo Park, CA, 1517–1522, 2011.Google Scholar
  • [68] J. W. Ulvila and J. E. Gaffney Jr. A decision analysis method for evaluating computer intrusion detection systems. Decision Analysis 1(1):35–50, 2004.Link, Google Scholar
  • [69] G. van Capelleveen, M. Poel, R. M. Mueller, D. Thornton, and J. van Hillegersberg. Outlier detection in healthcare fraud: A case study in the Medicaid dental domain. International Journal of Accounting Information Systems 21:18–31, 2016.Crossref, Google Scholar
  • [70] S. Wasserkrug, L. Boussioux, and W. Sun. Combining large language models and OR/MS to make smarter decisions. V. Babich, P. Dewan, and J. Pender, eds. Tutorials in Operations Research: Smarter Decisions for a Better World. INFORMS, Catonsville, MD, 1–49, 2024.Link, Google Scholar
  • [71] B. Woodard. Fighting healthcare fraud with statistics. Significance 12(3):22–25, 2015.Crossref, Google Scholar
  • [72] B. Zafari and T. Ekin. Topic modelling for medical prescription fraud and abuse detection. Journal of the Royal Statistical Society: Series C (Applied Statistics) 68(3):751–769, 2019.Crossref, Google Scholar
  • [73] B. Zafari, T. Ekin, and F. Ruggeri. Explainable rank-based anomaly detection. The American Statistician 1–10, 2026.Crossref, Google Scholar
  • [74] Y. Zhao. Teaching supply chain analytics—From problem solving to problem discovery. M. C. Chou, H. Gibson, and B. R. Staats, eds. Tutorials in Operations Research: Emerging and Impactful Topics in Operations. INFORMS, Catonsville, MD, 191–212, 2022.Google Scholar
  • [75] H. Zhou, D. Ralston, X. Yang, and R. Hu, Adversarial decision-making in partially observable multi-agent systems: A sequential hypothesis testing approach. IEEE Transactions on Control of Network Systems 13(2):1218–1229, 2026.Crossref, Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.