Machine learning techniques in healthcare provider fraud detection and analysis: A systematic literature review
Abstract
Healthcare fraud is a growing concern, resulting in substantial financial losses and threatening the quality and trustworthiness of healthcare delivery. According to the National Health Care Anti-Fraud Association (NHCAA), healthcare fraud costs the economy tens of billions of dollars annually. Fraudulent activities, including upcoding, billing for unprovided services, and illegal kickbacks, contribute to rising healthcare costs, increased insurance premiums, and reduced quality of patient care. Combating healthcare fraud requires advanced detection systems, strict regulatory enforcement, and greater awareness among providers and patients. Machine learning (ML), a field within artificial intelligence, has emerged as a critical tool in healthcare fraud detection. This literature review examines the most recent scholarly articles on ML applications in fraud analytics, with a focus on (1) identifying and categorizing ML models used for provider fraud detection, (2) evaluating the effectiveness and challenges of ML-based approaches, and (3) exploring emerging trends and future advancements in fraud analytics. The findings reveal that supervised learning models such as Logistic Regression, decision trees, deep neural networks, and unsupervised techniques like anomaly detection and clustering are widely used to identify fraudulent patterns. Hybrid approaches that combine multiple ML models have demonstrated improved detection accuracy. Blockchain technology is an advanced database mechanism that, along with ML, can be used to improve the security, efficiency, and interoperability of healthcare data management and fraud detection. Nonetheless, there are still issues, including problems with data quality and standardization, data imbalance, evolving fraud tactics, and privacy concerns. This review study aims to assist researchers, professionals, and policymakers in implementing and managing machine learning models for fraud detection by providing insights into the key factors influencing these models. Understanding these factors will enhance decision-making in research projects and organizational operations, ultimately contributing to more effective fraud mitigation solutions in healthcare using state-of-the-art machine learning techniques.
References
1. NHCAA. The Challenge of Health Care Fraud. Available online: https://www.nhcaa.org/tools-insights/about-health-care-fraud/the-challenge-of-health-care-fraud (accessed on 5 March 2025).
2. The Office of Public Affairs. National Health Care Fraud Enforcement Action Results in 193 Defendants Charged and Over $2.75 Billion in False Claims. Available online: https://www.justice.gov/archives/opa/pr/national-health-care-fraud-enforcement-action-results-193-defendants-charged-and-over-275-0 (accessed on 5 March 2025).
3. Kelly JE. 2023 Health Care Fraud and Abuse Control Program Report Reveals $3.4 Billion in Fraud Recovery. Available online: https://natlawreview.com/article/2023-health-care-fraud-and-abuse-control-program-report-reveals-34-billion-fraud (accessed on 5 March 2025).
4. United States Sentencing Commission. Health Care Fraud. Available online: https://www.ussc.gov/research/quick-facts/health-care-fraud (accessed on 5 March 2025).
5. Oversight.gov. Medicaid Fraud Control Units Fiscal Year 2023 Annual Report. Available online: https://www.oversight.gov/sites/default/files/documents/reports/2024-10/OEI-09-24-00200-1.pdf (accessed on 5 March 2025).
6. GAO Highlights. Medicare and Medicaid Additional Actions Needed to Enhance Program Integrity and Save Billions. Available online: https://www.gao.gov/assets/gao-24-107487-highlights.pdf (accessed on 5 March 2025).
7. Al Hosani M, Aljaberi SS, Nobanee H. Academic Trends in Insurance Fraud Research. SSRN Electronic Journal. 2024. doi: 10.2139/ssrn.4978584
8. du Preez A, Bhattacharya S, Beling P, et al. Fraud detection in healthcare claims using machine learning: A systematic review. Artificial Intelligence in Medicine. 2025; 160: 103061. doi: 10.1016/j.artmed.2024.103061
9. Ali O, Abdelbaki W, Shrestha A, et al. A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge. 2023; 8(1): 100333. doi: 10.1016/j.jik.2023.100333
10. Iqbal MS, Abd-Alrazaq A, Househ M. Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review. Advances in Informatics, Management and Technology in Healthcare; 2022. doi: 10.3233/shti220649
11. Guntoro AS, Arifin S, Noor MS, et al. Fraud in The Implementation of the Health Insurance Program: A Systematic Literature Review. Pakistan Journal of Life and Social Sciences (PJLSS). 2024; 22(1). doi: 10.57239/pjlss-2024-22.1.00315
12. Gandhar A, Gupta K, Pandey AK, et al. Fraud Detection Using Machine Learning and Deep Learning. SN Computer Science. 2024; 5(5). doi: 10.1007/s42979-024-02772-x
13. Mazumder MSA, Rahman MA, Chakraborty D. Patient Care and Financial Integrity in Healthcare Billing Through Advanced Fraud Detection Systems. Academic Journal on Business Administration, Innovation & Sustainability. 2024; 4(2): 82-93. doi: 10.69593/ajbais.v4i2.74
14. Guo Y. Application of Machine Learning in Insurance Fraud Detection: Achievements and Future Prospects. Advances in Intelligent Systems Research; 2024. doi: 10.2991/978-94-6463-512-6_65
15. Nabrawi E, Alanazi A. Fraud Detection in Healthcare Insurance Claims Using Machine Learning. Risks. 2023; 11(9): 160. doi: 10.3390/risks11090160
16. Odufisan OI, Abhulimen OV, Ogunti EO. Harnessing artificial intelligence and machine learning for fraud detection and prevention in Nigeria. Journal of Economic Criminology. 2025; 7: 100127. doi: 10.1016/j.jeconc.2025.100127
17. Azad T, William P. Fraud detection in healthcare billing and claims. International Journal of Science and Research Archive. 2024; 13(2): 3376-3395. doi: 10.30574/ijsra.2024.13.2.2606
18. Sayem MA, Taslima N, Sidhu SG, et al. A quantitative analysis of healthcare fraud and utilization of ai for mitigation. International journal of business and management sciences. 2024; 4(7): 13-36. doi: 10.55640/ijbms-04-07-03
19. Hamid Z, Khalique F, Mahmood S, et al. Healthcare insurance fraud detection using data mining. BMC Medical Informatics and Decision Making. 2024; 24(1). doi: 10.1186/s12911-024-02512-4
20. Parshuram HP, Joshi SG. A Comprehensive Analysis of Provider Fraud Detection through Machine Learning. International Journal of Advanced Research in Science, Communication and Technology. 2023; 3(2): 139-149. doi: 10.48175/ijarsct-14217
21. Settipalli L, Gangadharan GR. WMTDBC: An unsupervised multivariate analysis model for fraud detection in health insurance claims. Expert Systems with Applications. 2023; 215: 119259. doi: 10.1016/j.eswa.2022.119259
22. Sharma C, Vaid A, Kumar Saini M. Artificial Intelligence Driven Fraud Detection in SAP for Retail and Healthcare. International Journal of Science and Research (IJSR). 2024; 13(11): 312-315. doi: 10.21275/sr24119111713
23. Lekkala LR. Importance of Machine Learning Models in Healthcare Fraud Detection. Voice of the Publisher. 2023; 9(4): 207-215. doi: 10.4236/vp.2023.94017
24. Das S, Krishna Bhat A. Leveraging Artificial Intelligence for Early Fraud Detection in Insurance: Focusing on Intake and Claims Processing. International Journal of Science and Research (IJSR). 2024; 13(11): 1121-1124. doi: 10.21275/sr241119105452
25. Shungube PS, Bokaba T, Ndayizigamiye P, et al. A Deep Learning Approach for Healthcare Insurance Fraud Detection. Research Square; 2024. doi: 10.21203/rs.3.rs-5453482/v1
26. De Meulemeester H, De Smet F, van Dorst J, et al. Explainable unsupervised anomaly detection for healthcare insurance data. BMC Medical Informatics and Decision Making. 2025; 25(1). doi: 10.1186/s12911-024-02823-6
27. Mayaki MZA, Riveill M. Multiple Inputs Neural Networks for Fraud Detection. In: Proceedings of the 2022 International Conference on Machine Learning, Control, and Robotics (MLCR); 2022. doi: 10.1109/mlcr57210.2022.00011
28. Fursov I, Kovtun E, Rivera-Castro R, et al. Sequence Embeddings Help Detect Insurance Fraud. IEEE Access. 2022; 10: 32060-32074. doi: 10.1109/access.2022.3149480
29. Dey R, Roy A, Akter J, et al. AI-Driven Machine Learning for Fraud Detection and Risk Management in U.S. Healthcare Billing and Insurance. Journal of Computer Science and Technology Studies. 2025; 7(1): 188-198. doi: 10.32996/jcsts.2025.7.1.14
30. Devaguptam S, Gorti SS, Akshaya TL, et al. Automated Health Insurance Processing Framework with Intelligent Fraud Detection, Risk Classification and Premium Prediction. SN Computer Science. 2024; 5(5). doi: 10.1007/s42979-024-02801-9
31. Johnson JM, Khoshgoftaar TM. Data-Centric AI for Healthcare Fraud Detection. SN Computer Science. 2023; 4(4). doi: 10.1007/s42979-023-01809-x
32. Chirchi KE, Kavya B. Unraveling Patterns in Healthcare Fraud through Comprehensive Analysis. In: Proceedings of the 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom); 2024. doi: 10.23919/indiacom61295.2024.10498727
33. Narne H. Machine Learning for Health Insurance Fraud Detection: Techniques, Insights, and Implementation Strategies. International Journal of Research and Analytical Reviews. 2024.
34. Hancock JT, Bauder RA, Wang H, et al. Explainable machine learning models for Medicare fraud detection. Journal of Big Data. 2023; 10(1). doi: 10.1186/s40537-023-00821-5
35. Islam Prova NN. Healthcare Fraud Detection Using Machine Learning. In: Proceedings of the 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI); 2024. doi: 10.1109/icoici62503.2024.10696476
36. El-Enen MAA, Tbaishat D, Sahlol AT, et al. Fraud Detection in Medical Insurance Claims Using Majority Voting of Multiple Unsupervised Algorithms. Procedia Computer Science. 2024; 244: 9-22. doi: 10.1016/j.procs.2024.10.173
37. Talukder MdA, Khalid M, Uddin MA. An integrated multistage ensemble machine learning model for fraudulent transaction detection. Journal of Big Data. 2024; 11(1). doi: 10.1186/s40537-024-00996-5
38. Wang Z, Chen X, Wu Y, et al. An Interpretable Model for Health-care Insurance Fraud Detection. Research Square; 2024. doi: 10.21203/rs.3.rs-5012877/v1
39. Yao J, Yu S, Wang C, et al. Medicare Fraud Detection Using WTBagging Algorithm. In: Proceedings of the 2021 7th International Conference on Computer and Communications (ICCC); 2021. doi: 10.1109/iccc54389.2021.9674545
40. Hancock JT, Wang H, Khoshgoftaar TM, et al. Data reduction techniques for highly imbalanced medicare Big Data. Journal of Big Data. 2024; 11(1). doi: 10.1186/s40537-023-00869-3
41. Tajrobehkar M, Guo X, Nguyen D, et al. Utilization Analysis and Fraud Detection in Medicare via Machine Learning. Cold Spring Harbor Laboratory; 2025. doi: 10.1101/2024.12.30.24319784
42. Wang Z, Chen X, Wu Y, et al. A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud. Scientific Reports. 2025; 15(1). doi: 10.1038/s41598-024-82062-x
43. Kumaraswamy N, Ekin T, Park C, et al. Using a Bayesian Belief Network to detect healthcare fraud. Expert Systems with Applications. 2024; 238: 122241. doi: 10.1016/j.eswa.2023.122241
44. Mardani S, Moradi H. Using Graph Attention Networks in Healthcare Provider Fraud Detection. IEEE Access. 2024; 12: 132786-132800. doi: 10.1109/access.2024.3425892
45. Yoo Y, Shin D, Han D, et al. Medicare fraud detection using graph neural networks. In: Proceedings of the 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET); 2022. doi: 10.1109/icecet55527.2022.9872963
46. Agarwal S. Graph-Based Social Network Analysis for Uncovering Fraudulent Patterns in Health Insurance. In: Proceedings of the 2024 IEEE 15th International Conference on Software Engineering and Service Science (ICSESS); 2024. doi: 10.1109/icsess62520.2024.10719395
47. Deprez B, Vandervorst F, Verbeke W, et al. Network analytics for insurance fraud detection: a critical case study. European Actuarial Journal. 2024; 14(3): 965-990. doi: 10.1007/s13385-024-00384-6
48. Gandra a. Optimize Fraud Detection in Health Insurance Claims by Integrating Graph Analytics and Machine Learning Models. International Journal for Multidisciplinary Research. 2024. doi: 10.36948/ijfmr.2024.v06i05.27381
49. Yoo Y, Shin J, Kyeong S. Medicare Fraud Detection Using Graph Analysis: A Comparative Study of Machine Learning and Graph Neural Networks. IEEE Access. 2023; 11: 88278-88294. doi: 10.1109/access.2023.3305962
50. Lu J, Lin K, Chen R, et al. Health insurance fraud detection by using an attributed heterogeneous information network with a hierarchical attention mechanism. BMC Medical Informatics and Decision Making. 2023; 23(1). doi: 10.1186/s12911-023-02152-0
51. Jena SK, Kumar B, Mohanty B, et al. An advanced blockchain-based hyperledger fabric solution for tracing fraudulent claims in the healthcare industry. Decision Analytics Journal. 2024; 10: 100411. doi: 10.1016/j.dajour.2024.100411
52. Kapadiya K, Ramoliya F, Gohil K, et al. Blockchain-assisted healthcare insurance fraud detection framework using ensemble learning. Computers and Electrical Engineering. 2025; 122: 109898. doi: 10.1016/j.compeleceng.2024.109898
53. Selvamuthu CM, Lavaraju B, Sundaram A. A Novel Approach of Streamlining Claims Processing and Fraud Prevention in Health Insurance through Blockchain Technology. In: Proceedings of the 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC); 2024. doi: 10.1109/i-smac61858.2024.10714863
54. Guerar M, Migliardi M, Russo E, et al. SSI-MedRx: A Fraud-Resilient Healthcare System based on Blockchain and SSI. TechRxiv; 2024. doi: 10.36227/techrxiv.172055493.30957383/v1
55. Gupta G, Mandal BK, Dwivedi V, et al. Integrating Blockchain with Machine Learning for Fraud Detection in Health Insurance Claims Management. International Journal of Intelligent Systems and Applications. 2024.
56. Shruthi K, Poornima AS, Ankitha D, et al. Healthcare Insurance Fraud Detection Powered by Blockchain and Machine Learning: An Analysis and Framework. In: Proceedings of the 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE); 2024. doi: 10.1109/icspcre62303.2024.10675307
57. Mani C, Ajay C, Harish J, et al. Block chain and AI-empowered healthcare insurance fraud detection: An analysis, architecture and future prospects. Challenges in Information, Communication and Computing Technology; 2024. doi: 10.1201/9781003559092-72
58. Samara B. Using Binary Logistic Regression to Detect Health Insurance Fraud. Pakistan Journal of Life and Social Sciences. 2024. doi: 10.57239/PJLSS-2024-22.2.00848.
59. Duman E. Implementation of Xgboost Method for Healthcare Fraud Detection. DergiPark (Istanbul University); 2022.
60. Surjuse A, Deshmukh S. Securing Healthcare Finances: AI Approach to Insurance Fraud Detection. Computer Research and Development; 2024.
61. Kumaraswamy N, Markey MK, Ekin T, et al. Healthcare Fraud Data Mining Methods: A Look Back and Look Ahead. PubMed; 2022.
62. Halimeh A. Integrating information quality in visual analytics [PhD thesis]. University of Arkansas; 2011
63. Arafat MS, Desai K, et al. Cybersecurity Challenges in Healthcare IT: Business Strategies for Mitigating Data Breaches and Enhancing Patient Trust. The American Journal of Engineering and Technology. 2025; 07(05): 15-38. doi: 10.37547/tajet/volume07issue05-03
64. Jillo G. Advances and Challenges in Fraud Detection in Medical Insurance. Available online: https://ssrn.com/abstract=4907327 (accessed on 5 March 2025).
65. Zitouni I, Postema J, van Es R. Explainable AI in fraud detection. Available online: https://www.milliman.com/en/insight/explainable-ai-in-fraud-detection (accessed on 5 March 2025).
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