Investigation of the possibilities for detecting motor insurance fraud using fuzzy inference
Abstract
Insurance fraud occurs when a claimant tries to gain financial benefits by making an unfounded, unjustified claim. These cases can cause serious economic damage. Consequently, fraud detection is a key issue at present, especially in the motor insurance market. Soft computing techniques have emerged in recent decades to model and support problem detection. In the framework of this paper, a theoretical Mamdani-type fuzzy inference system is presented for predicting the assumed probability of insurance fraud using easily determinable parameters: the insurance payout, in Ft; the age of the not faulty participant vehicle, in years; and the payment period of the insurance contract.
References
Stefano, B., & Gisella, F. (2001, Decem-ber). Insurance fraud evaluation: a fuzzy expert system. In 10th IEEE International Conference on Fuzzy Systems.(Cat. No. 01CH37297) (Vol. 3, pp. 1491-1494). IEEE. DOI: https://doi.org/fw6zmq
Artís, M., Ayuso, M., & Guillén, M. (2002). Detection of automobile insurance fraud with discrete choice models and misclassified claims. Journal of Risk and Insurance, 69(3), 325-340. DOI: https://doi.org/cxgk34
Morley, N. J., Ball, L. J., & Ormerod, T. C. (2006). How the detection of insurance fraud succeeds and fails. Psychology, Crime & Law, 12(2), 163-180. DOI: https://doi.org/cfwtfp
Goleiji, L., & Tarokh, M. J. (2016) Fraud detection in the insurance using decision tree, naïve Bayesian and support vector machine data mining algorithms (Case study-automobile's body insurance). Majlesi Journal of Multimedia Processing, 5(1), 29-33.
Nian, K., Zhang, H., Tayal, A., Coleman, T., & Li, Y. (2016). Auto insurance fraud detection using unsupervised spectral ranking for anomaly. The Journal of Financand Data Science, 2(1), 58-75. DOI: https://doi.org/nfrz
Roy, R., & George, K. T. (2017, April). Detecting insurance claims fraud using machine learning techniques. In 2017 international conference on circuit, power and computing technologies (ICCPCT) (pp. 1-6). IEEE. DOI: https://doi.org/gnrfd8
Bécsi, T., Aradi, S., & Fehér, Á. (2020). A gépi tanulás szerepe és hatásai a közlekedés-ben. KÖZLEKEDÉSTUDOMÁNYI SZEM-LE, 70(1), 54-65. DOI: https://doi.org/nfr2
Ghorbani, A., & Farzai, S. (2018). Fraud detection in automobile insurance using a data mining based approach. Interna-tional Journal of Mechatronics, Elektrical and Computer Technology (IJMEC), 8(27), 3764-3771.
Kovács, G., Hőgye-Nagy, Á., & Kurucz, G. (2021). Human Factor Aspects of Situation Awareness in Autonomous Cars–An Overview of Psychological Approaches. Acta Polytechnica Hungarica, 18(7), 7-24. DOI: https://doi.org/gqv7xs
Horváth, Z. C., Buics, L., Földesi, P., & Ba-lassa, B. E. (2022). The Role of Hungarian Traffic Rules Education and Examination System–a Quality Function Deployment Approach. Acta Polytechnica Hungarica, 19(7). DOI: https://doi.org/nfr3
Gomes, C., Jin, Z., & Yang, H. (2021). Insurance fraud detection with unsupervised deep learning. Journal of Risk and Inuurance, 88(3), 591-624. DOI: https://doi.org/nfr4
Hanafy, M., & Ming, R. (2021). Using machine learning models to compare various resampling methods in predicting insurance fraud. J. Theor. Appl. Inf. Technol, 99(12), 2819-2833.
Macedo, A. M., Cardoso, C. V., & Neto, J. S. M. (2021). Car insurance fraud: the role of vehicle repair workshops. Internatio-nal Journal of Law, Crime and Justice, 65, 100456. DOI: https://doi.org/gshcbr
Aslam, F., Hunjra, A. I., Ftiti, Z., Louhichi, W., & Shams, T. (2022). Insurance fraud detection: Evidence from artificial intelligence and machine learning. Rese-arch in International Business and Finance, 62, 101744. DOI: https://doi.org/nfr5
Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical science, 17(3), 235-255. DOI: https://doi.org/cbqv2n
Hassan, A. K. I., & Abraham, A. (2013). Computational intelligence models for insurance fraud detection: a review of a decade of research. Journal of Network and Innovative Computing, 1(2013), 341-347
Zadeh, L. A. (1965). Fuzzy set theory. Information and control, 8(3), 338-353.
Pedrycz, W. (1994). Why triangular membership functions? Fuzzy sets and Systems, 64(1), 21-30.
Barua, A., Mudunuri, L. S., & Kosheleva, O. (2013). Why trapezoidal and triangular membership functions work so well: Towards a theoretical explanation.
Kreinovich, V., Kosheleva, O., & Shahbazova, S. N. (2020). Why triangular and trapezoid membership functions: A simple explanation. Recent Developments in Fuzzy Logic and Fuzzy Sets: Dedicated to Lotfi A. Zadeh, 25-31. DOI: https://doi.org/nfr6
Zhao, J., & Bose, B. K. (2002, November). Evaluation of membership functions for fuzzy logic controlled induction mo-tor drive. In IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02 (Vol. 1, pp. 229-234). IEEE. DOI: https://doi.org/b3xq2d
Ozguney, O. C., & Burkan, R. (2021). Fuzzy-terminal sliding mode control of a flexible link manipulator. Acta Polytechnica Hun-garica, 18(3), 179-195. DOI: https://doi.org/nfr7
Borisov, V., Chernovalova, M., Dulyasova, M., Morozov, D., & Vasiliev, A. (2022). Fuzzy Methods for Comparing Pro-ject Situations and Selecting Precedent Decisions. Acta Polytechnica Hungarica, 19(10). DOI: https://doi.org/nfr8
Mamdani, E. H. (1974, December). Application of fuzzy algorithms for control of simple dynamic plant. In Proceedings of the institution of electrical engineers (Vol. 121, No. 12, pp. 1585-1588). IET Digital Library.
Saade, J. J., & Diab, H. B. (2004). Defuzzification methods and new techniques for fuzzy controllers.
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