Authors : Esther .A. Makandah; Ebuka Emmanuel Aniebonam; Similoluwa Blossom Adesuwa Okpeseyi; Oyindamola Ololade Waheed

Volume/Issue : Volume 10 - 2025, Issue 1 - January

Google Scholar : https://tinyurl.com/23ehbevj

Scribd : https://tinyurl.com/4au66f32

DOI : https://doi.org/10.5281/zenodo.14769423

Abstract : The financial stability and operational effectiveness of healthcare systems around the world are threatened by the widespread problem of healthcare fraud. Conventional fraud detection systems, which rely on human investigations and retroactive audits, are unable to adequately meet the complexity and expansion of modern fraud schemes which have evolved over the years. The aim of this research is to examine the potential of AI-driven predictive analytics in preventing healthcare fraud, focusing on the development of proactive initiatives to identify and prevent healthcare-related fraudulent activities. The findings indicate that in terms of accuracy, speed, and adaptability, AI-driven predictive analytics outperforms conventional fraud detection techniques Technologies such as NLP, supervised learning, and deep learning have proven successful in revealing hidden patterns in intricate datasets. Additionally, healthcare organizations can prioritize high-risk cases and react quickly to new threats by integrating real-time fraud detection systems with risk-scoring models. Therefore, AI-driven predictive analytics can proactively support healthcare fraud detection and prevention.

Keywords : Fraud detection, Artificial Intelligence, Predictive Analytics, Healthcare, Risk management.

References :

1. Adekunle T. S., Alabi O. O., Morolake Oladayo Lawrence, Godwin Nse Ebong, Grace Oluwamayowa Ajiboye, & Temitope Abiodun Bamisaye. (2024). The Use of AI to Analyze Social Media Attacks for Predictive Analytics. Journal of Computing Theories and Applications, 2(2), 169–178. https://doi.org/10.62411/jcta.10120
2. Afriyie, J. K., Tawiah, K., Pels, W. A., Addai-Henne, S., Dwamena, H. A., Owiredu, E. O., Ayeh, S. A., & Eshun, J. (2023). A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions. Decision Analytics Journal, 6(100163), 100163. https://doi.org/10.1016/j.dajour.2023.100163
3. Ahmad, H., Kasasbeh, B., Aldabaybah, B., & Rawashdeh, E. (2023). Class balancing framework for credit card fraud detection based on clustering and similarity-based selection (SBS). International Journal of Information Technology. https://doi.org/10.1007/s41870-022-00987-w
4. Alam, S., Rahman, M. A., & Chakraborty, D. (2024). Patient care and financial integrity in healthcare billing through advanced fraud detection systems. patient care and financial integrity in healthcare billing through advanced fraud detection systems, 4(2), 82–93. https://doi.org/10.69593/ajbais.v4i2.74
5. Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2024). AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact. Sustainability, 16(5), 1790. https://doi.org/10.3390/su16051790
6. Ali, T., Al-Khalidi, M., & Al-Zaidi, R. (2024). Information Security Risk Assessment Methods in Cloud Computing: Comprehensive Review. ˜the œJournal of Computer Information Systems, 1–28. https://doi.org/10.1080/08874417.2024.2329985
7. Asghar, J., & Abbas, G. (2024). AI and Predictive Analytics: A New Era of Fraud Detection and AML in Financial Services. https://doi.org/10.13140/RG.2.2.12379.99367
8. Azlaan, M. (2024, May 3). Natural Language Processing (NLP) in Fraud Detection. https://www.researchgate.net/publication/383858793_Natural_Language_Processing_NLP_in_Fraud_Detection
9. Badawy, M. (2023). Healthcare predictive analytics using machine learning and deep learning techniques: a survey. ProQuest, 10(40), 40. https://doi.org/10.1186/s43067-023-00108-y
10. Bello, A., & Olufemi, K. (2024). Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities. Computer Science & IT Research Journal, 5(6), 1505–1520. https://doi.org/10.51594/csitrj.v5i6.1252
11. Bello, O., Folorunso, A., Ejiofor, O., Zainab Budale, F., Adebayo, K., Olayemi, A., Babatunde, & Bello, C. (2023). Machine Learning Approaches for Enhancing Fraud Prevention in Financial Transactions. International Journal of Management Technology, 10(1), 85–109. https://doi.org/10.37745/ijmt.2013/vol10n185109
12. Blais, D. (2022). Strategies for Preventing and Mitigating Counterfeit Medication Strategies for Preventing and Mitigating Counterfeit Medication From Entering the U.S. Supply Chain From Entering the U.S. Supply Chain. https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=14481&context=dissertations
13. Božić V. (2023). AI and Predictive Analytics. https://doi.org/10.13140/RG.2.2.23798.47682
14. Chang, J.-W., Yen, N., & Hung, J. C. (2022). Design of a nlp-empowered finance fraud awareness model: The anti-fraud chatbot for fraud detection and fraud classification as an instance. Journal of Ambient Intelligence and Humanized Computing, 13. https://doi.org/10.1007/s12652-021-03512-2
15. Devan, M., Prakash, S., & Jangoan, S. (2023). Predictive Maintenance in Banking: Leveraging AI for Real-Time Data Analytics. Journal of Knowledge Learning and Science Technology, 2(2), 483–490. https://doi.org/10.60087/jklst.vol2.n2.p490
16. Drabiak, K., & Wolfson, J. (2020). What Should Health Care Organizations Do to Reduce Billing Fraud and Abuse? AMA Journal of Ethics, 22(3), E221-231. https://doi.org/10.1001/amajethics.2020.221
17. Gupta, B., Rawat, A., Jain, A., Arora, A., & Dhami, N. (2017). Analysis of Various Decision Tree Algorithms for Classification in Data Mining. International Journal of Computer Applications, 163(8), 15–19. https://doi.org/10.5120/ijca2017913660
18. Gupta, R., Sharma, A., & Alam, T. (2024). Building Predictive Models with Machine Learning. Studies in Big Data, 39–59. https://doi.org/10.1007/978-981-97-0448-4_3
19. Haleem, A., Javaid, M., Singh, R. P., Suman, R., & Rab, S. (2021). Blockchain Technology Applications in Healthcare: An Overview. International Journal of Intelligent Networks, 2(2), 130–139. https://doi.org/10.1016/j.ijin.2021.09.005
20. Hilal, W., Gadsden, S. A., & Yawney, J. (2021). A Review of Anomaly Detection Techniques and Applications in Financial Fraud. Expert Systems with Applications, 193(1), 116429. https://doi.org/10.1016/j.eswa.2021.116429
21. Huang, Z., Zheng, H., Li, C., & Che, C. (2024). Application of Machine Learning-Based K-means Clustering for Financial Fraud Detection. Academic Journal of Science and Technology, 10(1), 33–39. https://doi.org/10.54097/74414c90
22. Idemudia C., Ebele, E., & None Shadrack Obeng. (2024). Analyzing how data analytics is used in detecting and preventing fraudulent health insurance claims. International Journal of Frontiers in Science and Technology Research, 7(1), 048–056. https://doi.org/10.53294/ijfstr.2024.7.1.0045
23. Kumar, P. R., & Manash, E. B. K. (2019). Deep learning: a branch of machine learning. Journal of Physics: Conference Series, 1228, 012045. https://doi.org/10.1088/1742-6596/1228/1/012045
24. Kumar, V., & Garg, M. L. (2018). Predictive Analytics: A Review of Trends and Techniques. International Journal of Computer Applications, 182(1), 31–37. Researchgate. https://doi.org/10.5120/ijca2018917434
25. Kumaraswamy N., Markey, M. K., Ekin, T., Barner, J. C., & Rascati, K. (2022). Healthcare Fraud Data Mining Methods: A Look Back and Look Ahead. Perspectives in Health Information Management, 19(1), 1i. https://pmc.ncbi.nlm.nih.gov/articles/PMC9013219/
26. Kumari, S., Patil, Y., & Jeble, S. (2016). Role of big data and predictive analytics. International Journal of Automation and Logistics, 2(4), 307. https://doi.org/10.1504/ijal.2016.10001272
27. Legotlo, T. G., & Mutezo, A. (2018). Understanding the types of fraud in claims to South African medical schemes. South African Medical Journal, 108(4), 299. https://doi.org/10.7196/samj.2018.v108i4.12758
28. Mackey, T. K., & Liang, B. A. (2012). Combating healthcare corruption and fraud with improved global health governance. BMC International Health and Human Rights, 12(1). https://doi.org/10.1186/1472-698x-12-23
29. Melam N., Reddy, C., Polavarapu Nagendra Babu, Sai, V., & Velpula Chaitanya. (2024). UPI Fraud Detection Using Convolutional Neural Networks (CNN). Research Square (Research Square). https://doi.org/10.21203/rs.3.rs-4088962/v1
30. Mesioye, O., & Ohiozua, T. (2024). Leveraging Financial Analytics for Fraud Mitigation and Maximizing Investment Returns: A Comparative Analysis of the USA, Africa, and Nigeria. International Journal of Research Publication and Reviews, 5(9), 1136–1152. https://doi.org/10.55248/gengpi.5.0924.2513
31. Mienye, I. D., & Jere, N. (2024). IEEE Xplore Full-Text PDF: Ieee.org. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10595068
32. Mohana M. (2024). Natural Language Processing (NLP). https://doi.org/10.13140/RG.2.2.13534.04169
33. Nabrawi, E., & Alanazi, A. (2023). Fraud Detection in Healthcare Insurance Claims Using Machine Learning. Risks, 11(9), 160. https://doi.org/10.3390/risks11090160
34. Naeem, S., Ali, A., Anam, S., & Ahmed, M. M. (2023). An Unsupervised Machine Learning Algorithms: Comprehensive Review. International Journal of Computing and Digital Systems, 13(1), 911–921. https://doi.org/10.12785/ijcds/130172
35. Nasteski, V. (2017). An overview of the supervised machine learning methods. HORIZONS.B, 4, 51–62. https://doi.org/10.20544/horizons.b.04.1.17.p05
36. Nnamdi, M. (2024). Predictive Analytics in Healthcare. ResearchGate; unknown. https://www.researchgate.net/publication/379478196_Predictive_Analytics_in_Healthcare
37. Odeyemi, O., Mhlongo, N. Z., Nwankwo, E. E., & Soyombo, O. T. (2024). Reviewing the role of AI in fraud detection and prevention in financial services. International Journal of Science and Research Archive, 11(1), 2101–2110. https://doi.org/10.30574/ijsra.2024.11.1.0279
38. Orelaja A., & Adeyemi, A. F. (2024). Developing Real-Time Fraud Detection and Response Mechanisms for Financial Transactions. https://www.researchgate.net/publication/383116473_Developing_Real-Time_Fraud_Detection_and_Response_Mechanisms_for_Financial_Transactions
39. Pan, E. (2024). Machine Learning in Financial Transaction Fraud Detection and Prevention. Transactions on Economics, Business and Management Research, 5, 243–249. https://doi.org/10.62051/16r3aa10
40. Pascarella, G., Rossi, M., Montella, E., Capasso, A., De Feo, G., Botti, G., Nardone, A., Montuori, P., Triassi, M., D’Auria, S., & Morabito, A. (2021). Risk Analysis in Healthcare Organizations: Methodological Framework and Critical Variables. Risk Management and Healthcare Policy, Volume 14(14), 2897–2911. Ncbi. https://doi.org/10.2147/rmhp.s309098
41. Prabin Adhikari, Prashamsa Hamal, & Francis Baidoo Jnr. (2024). Artificial Intelligence in fraud detection: Revolutionizing financial security. International Journal of Science and Research Archive, 13(1), 1457–1472. https://doi.org/10.30574/ijsra.2024.13.1.1860
42. Prova N. (2024). Healthcare Fraud Detection Using ML and AI. https://doi.org/10.13140/RG.2.2.35327.21922
43. Research and Markets. (2024). Healthcare Fraud Analytics Business Research Report 2023-2030: Growing Role of Data Mining and Pattern Recognition Spurs Innovations. GlobeNewswire News Room; Research and Markets. https://www.globenewswire.com/news-release/2024/10/17/2965069/0/en/Healthcare-Fraud-Analytics-Business-Research-Report-2023-2030-Growing-Role-of-Data-Mining-and-Pattern-Recognition-Spurs-Innovations.html
44. Rustagi, M., & Goel, N. (2022). Predictive Analytics: A study of its Advantages and Applications. IARS’ International Research Journal, 12(01), 60–63. https://doi.org/10.51611/iars.irj.v12i01.2022.192
45. Saarela, M., & Podgorelec V. (2024). Recent Applications of Explainable AI (XAI): A Systematic Literature Review. Applied Sciences, 14(19), 8884–8884. https://doi.org/10.3390/app14198884
46. Salloum, S. A., Gaber, T., Vadera, S., & Shaalan, K. (2021). Phishing Email Detection Using Natural Language Processing Techniques: A Literature Survey. Procedia Computer Science, 189, 19–28.
47. Shukla, S. (2023). Real-time Monitoring and Predictive Analytics in Healthcare: Harnessing the Power of Data Streaming. International Journal of Computer Applications, 185(8), 32–37. https://doi.org/10.5120/ijca2023922738
48. Sinha, R. (2024). The role and impact of new technologies on healthcare systems. Discover Health Systems, 3(1). https://doi.org/10.1007/s44250-024-00163-w
49. Soori, M., Arezoo, B., & Dastres, R. (2023). Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics, A Review. Cognitive Robotics, 3(1), 54–70. sciencedirect. https://doi.org/10.1016/j.cogr.2023.04.001
50. Stowell, N., Pacini, C., Wadlinger, N., Crain, J., & Schmidt, M. (2020). Regulations Regulations Repository Citation Repository Citation. William & Mary Business Law Review, 11(2), 11–2019. https://scholarship.law.wm.edu/cgi/viewcontent.cgi?article=1189&context=wmblr
51. Tariqul I, Islam, M., Ankur Sarkar, Rahman, O., Rakesh Paul, & Bari. (2024). Artificial Intelligence in Fraud Detection and Financial Risk Mitigation: Future Directions and Business Applications. International Journal for Multidisciplinary Research, 6(5). https://doi.org/10.36948/ijfmr.2024.v06i05.28496
52. Thaifur, A. Y. B. R., Maidin, M. A., Sidin, A. I., & Razak, A. (2021). How to detect healthcare fraud? “A systematic review.” Gaceta Sanitaria, 35(2), S441–S449. https://doi.org/10.1016/j.gaceta.2021.07.022
53. Thornton, D., Brinkhuis, M., Amrit, C., & Aly, R. (2015). Categorizing and Describing the Types of Fraud in Healthcare. Procedia Computer Science, 64, 713–720. https://doi.org/10.1016/j.procs.2015.08.594
54. Timofeyev, Y., & Jakovljevic, M. (2022). Editorial: Fraud and corruption in healthcare. Frontiers in Public Health, 10(1). https://doi.org/10.3389/fpubh.2022.921254
55. United Nations Office on Drugs and Crime . (2021). COMBATING FALSIFIED MEDICAL PRODUCT-RELATED CR ME A GUIDE TO GOOD LEGISLATIVE PRACTICES. United Nations Office on Drugs and Crime
56. Xu, J., Yang, T., Zhuang, S., Li, H., & Lu, W. (2024). AI-based financial transaction monitoring and fraud prevention with behaviour prediction. Applied and Computational Engineering, 67(1), 76–82. https://doi.org/10.54254/2755-2721/67/2024ma0068