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Graph‑enhanced fraud detection in health insurance: integrating centrality metrics and community analysis for improved accuracy | ||
| Journal of Discrete Mathematics and Its Applications | ||
| دوره 11، شماره 2، شهریور 2026، صفحه 131-151 اصل مقاله (3.31 M) | ||
| نوع مقاله: Full Length Article | ||
| شناسه دیجیتال (DOI): 10.22061/jdma.2026.12810.1181 | ||
| نویسندگان | ||
| Mohammad Mahdi Ahmadi1؛ Asma Hamzeh* 2؛ Ali Kamandi1 | ||
| 1Department of Engineering Sciences, University of Tehran, Tehran, I. R. Iran | ||
| 2Department of Modern Insurance Technologies, Insurance Research Center, Tehran, I. R. Iran | ||
| تاریخ دریافت: 20 آذر 1404، تاریخ پذیرش: 25 اسفند 1404 | ||
| چکیده | ||
| The dynamic nature of fraud and the emergence of new fraudulent methods in the insurance industry, along with the insufficient capability of insurance companies to prevent, identify, and combat it, may lead insurance companies toward bankruptcy in the not-too-distant future. Given that insurance companies serve as the main entity providing insurance services and compensating losses, their significant role in preventing and detecting fraud makes the various strategies they employ to counter fraud significant. In the current situation, identifying and analyzing the phenomenon of fraud, which leads to increased costs in the insurance industry, appears to be essential for controlling factors and ensuring the survival of insurance companies. This article examines the use of graph theory for fraud detection in the health insurance industry. By extracting information from relevant databases and constructing a comprehensive network graph, existing patterns are analyzed and suspicious fraudulent cases are identified. The proposed method was implemented on real data, and the results showed that this method is capable of detecting fraud with 95% accuracy, which is an improvement over the existing method. All implementation codes and supplementary materials are openly available on GitHub [https://github.com/MohammadMehdi41/fraudDetection.git]. | ||
| کلیدواژه‌ها | ||
| graph theory؛ health insurance؛ fraud detection | ||
| مراجع | ||
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[1] H. Askari Zadeh, M. J. Tarikh, Fraud detection in health insurance based on data mining approach, in: Proceedings of the International Conference on Modern Research in Management, Economics, and the Capability of the Tourism Industry in Development, 2017. [2] M. Behdad, L. Barone, M. Bennamoun, T. French, Nature-inspired techniques in the context of fraud detection, IEEE Trans. Syst. Man Cybern. C 42 (2012) 1273–1290. https://doi.org/10.1109/TSMCC.2012.2215851 [3] S. Chen, A. Gangopadhyay, A novel approach to uncover health care frauds through spectral analysis, in: IEEE International Conference on Healthcare Informatics (ICHI), IEEE, 2013, pp. 499–504. [4] S. Chen, A. Gangopadhyay, Health care fraud detection with community detection algorithms, in: 2016 IEEE International Conference on Smart Computing (SMARTCOMP), IEEE, 2016, pp. 1–6. https://doi.org/10.1109/SMARTCOMP.2016.7501694 [5] E. A. Duman, S. Sagiroglu, Health care fraud detection methods and new approaches, in: Proceedings of the International Conference on Computer Science and Engineering, 2017. https://doi.org/10.1109/UBMK.2017.8093544 [6] E. Estrada, P. A. Knight, A first course in network theory, Oxford Univ. Press, Oxford, 2015. https:// api.pageplace.de/ preview/ DT0400.9780191039928 _A24313697/ preview-9780191039928 _A24313697.pdf [7] FBI, Investigating insurance fraud, 2012. https://www.fbi.gov/ news/ stories/ investigatinginsurance-fraud [8] S. Fortunato, Community detection in graphs, Phys. Rep. 486 (2010) 75–174. https:// doi.org/ 10.1016/ j.physrep.2009.11.002 [9] Kaggle, Healthcare provider fraud detection analysis. https://www.kaggle.com/ [10] Y. Li, C. Yan, W. Liu, M. Li, A principal component analysis-based random forest with the potential nearest neighbor method for automobile insurance fraud identification, Appl. Soft Comput. 70 (2018) 1000–1009. https://doi.org/10.1016/j.asoc.2017.07.027 [11] J. Liu, E. Bier, A. Wilson, J. A. Guerra-Gomez, T. Honda, K. Sricharan, L. Gilpin, D. Davies, Graph analysis for detecting fraud, waste, and abuse in healthcare data, AI Mag. 37 (2016) 33–46. https://doi.org/10.1609/aimag.v37i2.2630 [12] A.Mohammadbaghi, Preliminary insurance discussion on third-party insurance, New Developments in the World of Insurance (2005) 25–36. [13] M. E. J. Newman, Modularity and community structure in networks, Proc. Natl. Acad. Sci. U.S.A. 103 (2006) 8577–8582. https://doi.org/10.1073/pnas.0601602103 [14] G. K. Orman, V. Labatut, A comparison of community detection algorithms on artificial networks, in: International Conference on Discovery Science, Springer, Berlin, 2009, pp. 242–256. https://doi.org/10.1007/978-3-642-04747-3_20 [15] N. Rahimian, Fraud detection, Official Accountant (2011) 81–92. [16] L. Subelj, S. Furlan, M. Bajec, An expert system for detecting automobile insurance fraud using social network analysis, Expert Syst. Appl. 38 (2011) 1039–1052. https:// doi.org/ 10.1016/ j.eswa.2010.07.143 | ||
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