Research Article
Peer ReviewedAt least two reviewers
Open Access
Detecting Fraud, Waste, and Abuse in Healthcare Claims using AI: Applying Isolation Forest to Claim Analytics
1University of The Cumberlands
2Blue Cross Blue Shield
✉ Corresponding author: Naren Chandra · naren1712@gmail.com
References
- Agarwal, S. (2023). An Intelligent Machine learning Approach for fraud Detection in Medical Claim Insurance: A Comprehensive study. Scholars Journal of Engineering and Technology, 11(09), 191-200. https://doi.org/10.36347/sjet.2023.v11i09.003
- Azad, N. T., & William, N. P. (2024). Fraud detection in healthcare billing and claims. International Journal of Science and Research Archive, 13(2), 3376-3395. https://doi.org/10.30574/ljsra.2024.13.2.2606
- Bauder, R. A., & Khoshgoftaar, T. M. (2018). The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider Labels. The Florida AI Research Society, 404-409. https://dblp.uni-trier.de/db/conf/flairs2018.html#BauderK18
- Bharadwaj, P. Leveraging AI for Healthcare Fraud Detection: A Game Changer for CTOs. https://www.clariontech.com. Retrieved July 18, 2025, from https://www.clariontech.com/blog/leveraging-ai-for-healthcare-fraud-detection-a-game-changer-for-ctos
- Copeland, L., Edberg, D., Panorska, A. K., & Wendel, J. (2011). Applying business intelligence concepts to Medicaid claim fraud detection. Journal of Innovation Systems Applied Research, 5(1), 51.http://jisar.org/2012-5/N1/JISARv5n1p51.pdf
- Coustasse, A., Layton, W., Nelson, L., & Walker, V. (2021). UPCODING MEDICARE: IS HEALTHCARE FRAUD AND ABUSE INCREASING? PubMed, 18(4), 1f.https://pubmed.ncbi.nlm.nih.gov/34975355
- Du Preez, A., Bhattacharya, S., Beling, P., & Bowen, E. (2024). Fraud Detection in Healthcare Claims Using Machine Learning: A Systematic review. Artificial Intelligence in Medicine, 160(103061). https://doi.org/10.1016/j.artmed.2024.103061
- Hassan, M., & Alam, M. Automating healthcare claims processing with supervised and unsupervised AI models. Journal of Applied Big Data Analytics, Decision Making, and Predictive Modelling Systems, 1-11.
- Jury convicts home health agency owner in Medicare fraud and identity theft scheme. (2025, May 21). https://www.justice.gov. retrieved July 18, 2025, from https://www.justice.gov/usao-sdtx/pr/jury-convicts-home-health-agency-owner-medicare-fraud-and-identity-theft-scheme
- Li, J., Huang, K., Jin, J., & Shi, J. (2008). A survey on statistical methods for health care fraud detection. Health Care Management Science, 11(3), 275-287. https://doi.org/10.1007/s10729-007-9045-4
- Martin, D. (2023, October 9). Government Agencies Using AI to Detect Healthcare Fraud. https://managedhealthcareexecutive.com. Retrieved July 18, 2025, from https://www.managedhealthcareexecutive.com/view/government-agencies-using -ai-to-detect-healthcare-fraud
- Millman Payment Integrity and MasterCard AI identified $239 million in healthcare fraud, waste and abuse. (2021, June). https://b2b.mastercard.com. Retrieved July 18, 2025, from https://b2b.mastercard.com/media/awejokju/milliman-case-study-for-brighterion-ai.pdf
- Morris, L. (2009). Combating fraud in health care: an essential component of any cost containment strategy. International Journal of Science and Research Archive, 13(2), 3376-3395. https://doi.org/10.30574/ljsra.2024.13.2.2606
- Prova, N. N. I. (2024). Healthcare Fraud Detection Using Machine Learning. IEEE Explore, 1119-1123. https://doi.org/10.1109/icoici62503.2024.10696476
- Ramirez, M. (2024, January 12). Humana:AI Use Cases 2024. https://pitchgrade.com. Retrieved July 18, 2025, from https://pitchgrade.com/companies/humana-ai-use-cases
- Sansone, R., & Sansone, L. (2012). Doctor Shopping. Innovations in Clinical Neuroscience, 9(11-12), 42-46
- Thornton, D., Mueller, R. M., Schoutsen, P., & Van Hillegersberg, J. (2013). Predicting Healthcare Fraud in Medicaid: A Multidimensional data model and analysis Techniques for Fraud Detection. Procedia Technology, 9, 1252-1264. https://doi.org/10.1016/j.protcy.2013.12.140
- Travaille, P., Müller, R. M., Thornton, D., & Van Hillegersberg, J. (2011). Electronic Fraud Detection in the U.S. Medicaid Healthcare Program: Lessons Learned from other Industries. Americas Conference on Information Systems, 1-11. https://ris.utwente.nl/ws/files/5506292/Travaille11electronic.pdf
- AI in Medical Billing: Detecting fraud and Ensuring Compliance in Revenue Cycle Management. (2025, April 15). www.enterhealth. Retrieved July 18, 2025, from https://www.enter.health/post/ai-fraud-detection-medical-billing
Cited by
1 — Crossref — cited-by count (retrieved 2 Oct 2026, source)
Citation counts are shown with their source and retrieval date. They are not an Impact Factor or any journal-level metric.