Hagyományos versus MI-alapú csalásfelderítés: Költséghatékonyság a gépjármű-biztosítások területén

Szerzői információk:

Benedek Botond: Babeş-Bolyai Tudományegyetem, adjunktus. E-mail: botond.benedek@econ.ubbcluj.ro

Nagy Bálint Zsolt: Babeş-Bolyai Tudományegyetem, egyetemi docens. E-mail: balint.nagy@econ.ubbcluj.ro

Hivatkozás (APA):

Benedek, B., & Nagy, B. Z. (2023). Hagyományos versus MI-alapú csalásfelderítés: Költséghatékonyság a gépjármű-biztosítások területén. Hitelintézeti Szemle, 22(2), 77–100. https://doi.org/10.25201/HSZ.22.2.77

Rovat:

Tanulmány

Journal of Economic Literature (JEL) kódok:

G22, C14, C45

Kulcsszavak:

gépjármű-biztosítás, biztosítási csalás, csalások felderítése, költségérzékeny döntéshozatal, adatbányászat

Felhasznált irodalom:

Ágoston Norbert (2022): Mesterséges intelligencia és gépi tanulási módszerek a vállalati fizetésképtelenség becslésére. Statisztikai Szemle, 100(6): 584–609. https://doi.org/10.20311/stat2022.6.hu0584

Artı́s, M. – Ayuso, M. – Guillén, M. (1999): Modelling different types of automobile insurance fraud behaviour in the Spanish market. Insurance: Mathematics and Economics, 24(1–2): 67–81. https://doi.org/10.1016/S0167-6687(98)00038-9

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. https://doi.org/10.1111/1539-6975.00022

Association of British Insurers (2021): No Time to Lie. https://www.abi.org.uk/news/news-articles/2021/10/detected-fraud-2020/. Letöltés ideje: 2021 november 15.

Badriyah, T. – Rahmaniah, L. – Syarif, I. (2018): Nearest neighbour and statistics method based for detecting fraud in auto insurance. International Conference on Applied Engineering (ICAE), Batam, Indonesia, pp. 1–5. https://doi.org/10.1109/INCAE.2018.8579155

Bánkúty-Balog Lilla (2022): A mesterséges intelligencia elterjedésének geoökonómiai hatásai és Magyarország. Külgazdaság, 66 (7–8): 102–130. https://doi.org/10.47630/KULG.2022.66.7-8.102

Belhadji, E.B. – Dionne, G. – Tarkhani, F. (2000): A Model for the Detection of Insurance Fraud. The Geneva Papers on Risk and Insurance – Issues and Practice, 25(4): 517–538. https://doi.org/10.1111/1468-0440.00080

Benedek Gábor (1999): Mesterséges intelligencia az üzleti világban: Marketingakciók hatékonyságának elemzése statisztikai és Data Mining módszerekkel. Vezetéstudomány-Management and Business Journal, 30(11): 33–36.

Benedek, B. – Ciumas, C. – Nagy, B.Z. (2022): Automobile insurance fraud detection in the age of big data – a systematic and comprehensive literature review. Journal of Financial Regulation and Compliance, 30(4): 503–523. https://doi.org/10.1108/JFRC-11-2021-0102

Benedek, B. – Ciumas, C. – Nagy, B.Z. (megjelenés alatt): On the cost-efficiency of automobile insurance fraud detection methods – A meta-analysis. Global Business Review, közlésre elfogadva, megjelenés alatt.

Bermúdez, L. – Pérez, J.M. – Ayuso, M. – Gómez, E. – Vázquez, F.J. (2008): A Bayesian dichotomous model with asymmetric link for fraud in insurance. Insurance: Mathematics and Economics, 42(2): 779–786. https://doi.org/10.1016/j.insmatheco.2007.08.002

Bhowmik, R. (2011): Detecting auto insurance fraud by data mining techniques. Journal of Emerging Trends in Computing and Information Sciences, 2(4): 156–162.

Csillag J. Balázs – Granát P. Marcell – Neszveda Gábor (2022): A környezeti kérdésekre irányuló médiafigyelem és az ESG-befektetések. Hitelintézeti Szemle, 21(4): 129–151. https://doi.org/10.25201/HSZ.21.4.129

Danyi Pál (2018): A mesterséges intelligencia alkalmazása az árazásban. Marketing & Menedzsment, 52(3–4): 5–18.

Derrig, R. A. – Ostaszewski, K. M. (1995): Fuzzy techniques of pattern recognition in risk and claim classification. Journal of Risk and Insurance, 62(3): 447–482. https://doi.org/10.2307/253819

Farkas Gábor – Magyar Péter – Molnár András – Zubor-Nemes Anna (2020): Adatbányászati módszerek alkalmazása a mezőgazdaságban – a gépi tanulás felhasználási lehetőségei. Gazdálkodás: Scientific Journal on Agricultural Economics, 64(1): 15–24.

Farquad, M.A. – Ravi, V. – Raju, S.B. (2012): Analytical CRM in banking and finance using SVM: a modified active learning-based rule extraction approach. International Journal of Electronic Customer Relationship Management, 6(1): 48–73. https://doi.org/10.1504/IJECRM.2012.046470

Gepp, A. – Wilson, H.J. – Kumar, K. – Bhattacharya, S. (2012): A Comparative Analysis of Decision Trees Vis-a-vis Other Computational Data Mining Techniques in Automotive Insurance Fraud Detection. Journal of Data Science, 10(3): 537–561. https://doi.org/10.6339/JDS.201207_10(3).0010

Green, D.M. – Swets, J. A. (1966): Signal detection theory and psychophysics (1 ed., Vol. 1). New York: Wiley.

Hámori Gábor (2001): A fizetésképtelenség előrejelzése logit-modellel. Bankszemle, 45(1–2): 65–87.

He, H. – Bai, Y. – Garcia, E. – Li, S. (2008): ADASYN: Adaptive synthetic sampling approach for imbalanced learning. IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, pp. 1322–1328. https://doi.org/10.1109/IJCNN.2008.4633969

Hill, H. R. – Sandler, B. – Mokgokong, R. – Lister, S. – Ward, T. – Boyce, R. – Farooqui, U. – Gordon, J. (2020): Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm. Journal Of Medical Economics, 23(4): 386–393. https://doi.org/10.1080/13696998.2019.1706543

III (2019): Insurance Information Institute: Insurance Fact Book. Insurance Information Institute. https://www.iii.org/sites/default/files/docs/pdf/insurance_factbook_2019.pdf

III (2021): Insurance Information Institute: Background on: Insurance fraud. https://www.iii.org/article/background-on-insurance-fraud. Letöltés ideje: 2021 november 20.

Karamizadeh, F. – Zolfagharifar, S. A. (2016): Using the Clustering Algorithms and Rule-based of Data Mining to Identify Affecting Factors in the Profit and Loss of Third Party Insurance, Insurance Company Auto. Indian Journal of Science and Technology, 9(7): 1–9. https://doi.org/10.17485/ijst/2016/v9i7/87846

Lee, A. – Taylor, P. – Kalpathy-Cramer, J. – Tufail, A. (2017): Machine Learning Has Arrived! Ophthalmology, 124(12): 1726–1728. https://doi.org/10.1016/j.ophtha.2017.08.046

LII (2023): Legal Information Institute: Insurance Fraud. Legal Information Institute, Cornell Law School. https://www.law.cornell.edu/wex/insurance_fraud. Letöltés ideje: 2023. április 26.

Li, Y. – Yan, C. – Liu, W. – Li, M. (2018): A principle component analysis-based random forest with the potential nearest neighbor method for automobile insurance fraud identification. Applied Soft Computing, 70(September): 1000–1009. https://doi.org/10.1016/j.asoc.2017.07.027

Massachusetts Regulation (1993): Division of Insurance Regulations. Massachusetts government. https://www.mass.gov/service-details/division-of-insurance-regulations. Letöltés ideje: 2023. április 26.

Muraközy Balázs (2018): Gépi tanulás, predikció és okság a közgazdaság-tudományban. Magyar Tudomány, 179(7): 1027–1037. https://doi.org/10.1556/2065.179.2018.7.10

Nian, K. – Zhang, H. – Tayal, A. – Coleman, T. – Li, Y. (2016): Auto insurance fraud detection using unsupervised spectral ranking for anomaly. The Journal of Finance and Data Science, 2(1): 58–75. https://doi.org/10.1016/j.jfds.2016.03.001

Padmaja, T. M. – Dhulipalla, N. – Bapi, R.S. – Krishna, P.R. (2007): Unbalanced data classification using extreme outlier elimination and sampling techniques for fraud detection. 15th International Conference on Advanced Computing and Communications (ADCOM 2007), Guwahati, India, pp. 511–516. https://doi.org/10.1109/ADCOM.2007.74

Pathak, J. – Vidyarthi, N. – Summers, S.L. (2005): A fuzzy-based algorithm for auditors to detect elements of fraud in settled insurance claims. Managerial Auditing Journal, 20(6): 632–644. https://doi.org/10.1108/02686900510606119

Pérez, J.M. – Muguerza, J. – Arbelaitz, O. – Gurrutxaga, I. – Martín, J.I. (2005): Consolidated Tree Classifier Learning in a Car Insurance Fraud Detection Domain with Class Imbalance. In: Singh, S. – Singh, M. – Apte, C. – Perner, P. (szerk.): Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_41

Phua, C. – Alahakoon, D. – Lee, V. (2004): Minority report in fraud detection: classification of skewed data. ACM Sigkdd Explorations Newsletter, 6(1): 50–59. https://doi.org/10.1145/1007730.1007738

Shaeiri, Z. – Kazemitabar, S. J. (2020): Fast unsupervised automobile insurance fraud detection based on spectral ranking of anomalies. International Journal of Engineering, 33(7): 1240–1248. https://doi.org/10.5829/ije.2020.33.07a.10

Šubelj, L. – Furlan, Š. – Bajec, M. (2011): An expert system for detecting automobile insurance fraud using social network analysis. Expert Systems with Applications, 38(1): 1039–1052. https://doi.org/10.1016/j.eswa.2010.07.143

Subudhi, S. – Panigrahi, S. (2017): Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection. Journal of King Saud University – Computer and Information Sciences, 32(5): 568–575. https://doi.org/10.1016/j.jksuci.2017.09.010

Sundarkumar, G.G. – Ravi, V. (2015): A novel hybrid undersampling method for mining unbalanced datasets in banking and insurance. Engineering Applications of Artificial Intelligence, 37(January): 368–377. https://doi.org/10.1016/j.engappai.2014.09.019

Sundarkumar, G.G. – Ravi, V. – Siddeshwar, V. (2015): One-class support vector machine based undersampling: Application to churn prediction and insurance fraud detection. IEEE International Conference on Computational Intelligence and Research (ICCIC), Madurai, India, pp. 1–7. https://doi.org/10.1109/ICCIC.2015.7435726

Tao, H. – Zhixin, L. – Xiaodong, S. (2012): Insurance fraud identification research based on fuzzy support vector machine with dual membership. International Conference on Information Management, Innovation Management and Industrial Engineering, Sanya, pp. 457–460. https://doi.org/10.1109/ICIII.2012.6340016

Viaene, S. – Ayuso, M. – Guillen, M. – Van Gheel, D. – Dedene, G. (2007): Strategies for detecting fraudulent claims in the automobile insurance industry. European Journal of Operational Research, 176(1): 565–583. https://doi.org/10.1016/j.ejor.2005.08.005

Viaene, S. – Derrig, R. A. – Baesens, B. – Dedene, G. (2002): A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection. Journal of Risk and Insurance, 69(3): 373–421. https://doi.org/10.1111/1539-6975.00023

Virág Miklós – Kristóf Tamás (2005): Az első hazai csődmodell újraszámítása neurális hálók segítségével. Közgazdasági Szemle, 52(2) 144–162.

Virág Miklós – Nyitrai Tamás (2013): Application of support vector machines on the basis of the first Hungarian bankruptcy model. Society and Economy, 35(2): 227–248. https://doi.org/10.1556/SocEc.35.2013.2.6

Virág Miklós – Nyitrai Tamás (2014): Metamódszerek alkalmazása a csődelőrejelzésben. Hitelintézeti Szemle, 13(4): 180–195. https://hitelintezetiszemle.mnb.hu/letoltes/8-virag-nyitrai-2.pdf

Wang, Y. – Xu, W. (2018): Leveraging deep learning with LDA-based text analytics to detect automobile insurance fraud. Decision Support Systems, 105(January): 87–95. https://doi.org/10.1016/j.dss.2017.11.001

Weisberg, H. – Derrig, R. (1991): Fraud and Automobile Insurance: A Report on Bodily Injury Liability Claims in Massachusetts. Journal of Insurance Regulation, 9(4): 497–541.

Weisberg, H. – Derrig, R. (1998): Quantitative methods for detecting fraudulent automobile bodily injury claims. Risques, 35: 75–99.

Wilson, J.H. (2009): An analytical approach to detecting insurance fraud using logistic regression. Journal of Finance and Accountancy, 85(150): 1–15.

Xu, W. – Wang, S. – Zhang, D. – Yang, B. (2011): Random rough subspace based neural network ensemble for insurance fraud detection. Fourth International Joint Conference on Computational Sciences and Optimization, Kunming and Lijiang City, China, pp. 1276–1280. https://doi.org/10.1109/CSO.2011.213

Yan, C. – Li, Y. (2015): The Identification Algorithm and Model Construction of Automobile Insurance Fraud Based on Data Mining. Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), Qinhuangdao, China, pp. 1922–1928. https://doi.org/10.1109/IMCCC.2015.408

Zelenkov, Y. (2019): Example-dependent cost-sensitive adaptive boosting. Expert Systems with Applications, 135: 71–82. https://doi.org/10.1016/j.eswa.2019.06.009