Fraud Detection in Health Insurance Claims Based on Artificial Intelligence (AI)
Downloads
Fraud in health insurance claims has become a significant problem affecting the provision of healthcare globally. In addition to the financial losses incurred, patients who actually need medical treatment also suffer. This is because healthcare providers are not paid on time, as a result of delays in the manual scrutiny of their claims. Health insurance claim fraud is perpetrated through service providers, insurance customers, and insurance companies. The need for the development of a decision support system (DSS) for accurate claims processing that can automatically detect fraud is urgently needed. The purpose of this research is to create a machine learning model that can detect fraud in health insurance claims based on artificial intelligence (AI). The method used is Deep Learning. The accuracy obtained is an accuracy of 86%.
R. A. Sowah et al., “Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs),” J. Eng. (United Kingdom), vol. 2019, no. January 2007, 2019, doi: 10.1155/2019/1432597.
I. Technology, Proceedings of the Eighth Australasian Data Mining Conference (AusDM’09), vol. 101. 2009.
P. V. Mohan, S. Dixit, A. Gyaneshwar, U. Chadha, K. Srinivasan, and J. T. Seo, “Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions,” Sensors, vol. 22, no. 6, 2022, doi: 10.3390/s22062194.
E. Kirkos, C. Spathis, and Y. Manolopoulos, “Data Mining techniques for the detection of fraudulent financial statements,” Expert Syst. Appl., vol. 32, no. 4, pp. 995–1003, 2007, doi: 10.1016/j.eswa.2006.02.016.
H. Joudaki et al., “Using data mining to detect health care fraud and abuse: a review of literature,” Glob. J. Health Sci., vol. 7, no. 1, pp. 194–202, 2015, doi: 10.5539/gjhs.v7n1p194.
H. Joudaki et al., “Improving fraud and abuse detection in general physician claims: A data mining study,” Int. J. Heal. Policy Manag., vol. 5, no. 3, pp. 165–172, 2016, doi: 10.15171/ijhpm.2015.196.
C. W. Lu Wang, “Business failure prediction based on two-stage selective ensemble with manifold learning algorithm and kernel-based fuzzy self-organizing map, Knowledge-Based Systems, Volume 121, 2017, Pages 99-110, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2017.0,” sciencedirect, vol. 121, pp. 99–110, 2017, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S095070511730028X
J. Zhang, J. Yao, L. Wang, Y. Chen, and Y. Pan, “A financial fraud detection model based on organizational impression management strategy,” J. Phys. Conf. Ser., vol. 1616, no. 1, 2020, doi: 10.1088/1742-6596/1616/1/012093.
F. Zamachsari and N. Puspitasari, “Penerapan Deep Learning dalam Deteksi Penipuan Transaksi Keuangan Secara Elektronik,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 203–212, 2021, doi: 10.29207/resti.v5i2.2952.
K. S. M. and A. A. K. C. Dubey, “Credit Card Fraud Detection using Artificial Neural Network and BackPropagation.” Madurai, India, pp. 268–273, 2020. doi: doi: 10.1109/ICICCS48265.2020.9120957.
S. Xuan, G. Liu, Z. Li, L. Zheng, S. Wang, and C. Jiang, “Random forest for credit card fraud detection,” ICNSC 2018 - 15th IEEE Int. Conf. Networking, Sens. Control, pp. 1–6, 2018,
doi: 10.1109/ICNSC.2018.8361343.
X. Yu, X. Li, Y. Dong, and R. Zheng, “A Deep Neural Network Algorithm for Detecting Credit Card Fraud,” in Proceedings - 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2020, China, 2020, pp. 181–183. doi: 10.1109/ICBAIE49996.2020.00045.
V. R. and G. Anuradha, “Fraud detection in health insurance using data mining techniques.” Mumbai, India, pp. 1–5, 2015. doi: 10.1109/ICCICT.2015.7045689.
A. C. Nugraha and M. I. Irawan, “Komparasi Deteksi Kecurangan pada Data Klaim Asuransi Pelayanan Kesehatan Menggunakan Metode Support Vector Machine (SVM) dan Extreme Gradient Boosting (XGBoost),” J. Sains dan Seni ITS, vol. 12, no. 1, 2023, doi: 10.12962/j23373520.v12i1.107032.
R. A. D. and G. D. S. Viaene, “A case study of applying boosting naive Bayes to claim fraud diagnosis.” pp. 612–620, 2004. doi: doi: 10.1109/TKDE.2004.1277822.
S. Vadlakonda and S. Siddam, “ML REGRESSION-BASED PREDICTIVE MODELING FOR DISEASE OUTBREAK THRESHOLD ESTIMATION,” vol. 15, pp. 435–443, 2024, doi: 10.47750/jett.2024.15.05.43.
P. Roddick, John & Li, Jiuyong & Christen, Peter & Kennedy, “Data Mining and Analytics 2008.” 2008.
