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Risk factor selection in automobile insurance policies: a way to improve the bottom line of insurance companies

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2015
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Fundação Escola de Comércio Alvares Penteado
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The objective of this paper is to test the validity of using 'bonus-malus' (BM) levels to classify policyholders satisfactorily. In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. The empirical evidence shows that common risk factors employed by insurance companies are good explanatory variables for classifying car policyholders' policies. In addition, the BM level variable slightly increases the explanatory power of the a priori risks factors.
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Åberg, L., & Rimmö, P. A. (1998). Dimensions of aberrant driver behavior. Ergonomics, 41(1),39-56. Ahn, B. S., Cho, S. S., & Kim, C. Y. (2000). The integrated methodology rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 18(2), 65-74. Arvidsson, S. (2010). Does private information affect the insurance risk? Working paper, The Geneva Association, 396, 2010. Retrieved from http://www.transportportal.se/SWoPEc/Essay_1_Arvidsson_Does_private_information.pdf Bazan, J., Nguyen, H. S., Nguyen, S. H., Synak, P., & Wróblewski, J. (2000). Rough set algorithms in classification problem. In L. Polkowski, S. Tsumoto, & T. Y. Lin (Eds.), Rough set methods and applications (pp. 49-88). New York: Physica-Verlag. Beynon, M. J., & Peel, M. J. (2001). Variable precision rough set theory and data discrimination: An application to corporate failure prediction. OMEGA: The International Journal of Management Science, 29(6), 561-576. Bousoño, C., Heras, A., & Tolmos, P. (2008). Factores de riesgo y cálculo de primas mediante técnicas de aprendizaje. Madrid, España: Ed. MAPFRE. Brockett P., Cooper, W., Golden, L., & Pitaktong, U. (1994). A neural network method for obtaining an early warning of insurer insolvency. The Journal of Risk and Insurance, 61(3), 402-424. Brockett, P., Golden, L., Jang, J., & Yang.C. (2006). A comparison of neural network, statistical methods, and variable choice for life insurers’ financial distress prediction. The Journal of Risk and Insurance, 73(3), 397-419. Costa, P. T., & McCrae, R. R. (1992). NEO PI-R professional manual. Odessa, FL: Psychological Assessment Resources. D’Arcy, S. (2005). Predictive modeling in automobile insurance: A preliminary analysis. [Working Paper, 302]. World Risk and Insurance Economics Congress, August, Salt Lake City. Retrieved from http://business.illinois.edu/ormir/Predictive%20Modeling%20in%20Automobile%20Insurance%207-1-05(PDF).pdf Denuit, M., Maréchal, X., Pitrebois, S., & Walhin, J. F. (2007). Index, in actuarial modeling of claim counts: Risk classification, credibility and bonus-malus systems. Chichester, UK: John Wiley & Sons. Díaz, Z., Segovia, M. J., Fernández, J., & Pozo, E. Machine learning and statistical techniques: An application to the prediction of insolvency in Spanish non-life insurance companies. (2005). The International Journal of Digital Accounting Research, 5(9), 1-45. Retrieved from http://www.uhu.es/ijdar/10.4192/1577-8517-v5_1.pdf Dimitras, A., Slowinski, R., Susmaga, R., & Zopounidis, C. (1999). Business failure prediction using Rough Sets. European Journal of Operational Research, 114(2), 263-280. Dionne, G., & Ghali, O. (2005). The bonusmalus system in Tunisia: An empirical Evaluation. Journal of Risk and Insurance, 72(4), 609-633. Ebanks, B., Karwowski, W., & Ostaszewski, K. (1992). Application of measures of fuzziness to risk classification in insurance. Paper presented at Forth International Conference on Computing and Information ICCI’92, Toronto. Forward, S. (2008). Driving violations: Investigating forms of irrational rationality. Uppsala: Universitetsbiblioteket. Retrieved from http://uu.diva-portal.org/smash/get/diva2:172720/FULLTEXT01 Glendon, A. I., Dorn, L., Davies, D. R., Matthews, G., & Taylor, R. G. (1996). Age and gender differences in perceived accident likelihood and driver competences. Risk Analysis, 16(6), 755-762. doi: 10.1111/j.1539-6924.1996.tb00826.x Goh, C., & Law, R. (2003). Incorporating the rough sets theory into travel demand analysis. Tourism Management, 24(5), 511-517. Greco, S., Matarazzo, B., & Slowinski, R. (1998). A new rough set approach to evaluation of bankruptcy risk. In C. Zopounidis (Ed.), New operational tools in the management of financial risks (pp. 121-136). Dordrecht: Kluwer Academic Publishers. Greco, S., Matarazzo, B., & Slowinski, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 129(1), 1-47. Gulian, E., Matthews, G., Glendon, A. I., Davies, D. R., & Debney, L. M. (1989). Dimensions of driver stress. Ergonomics, 32(6), 585-602. Gulliver, P., & Begg, D. (2007). Personality factors as predictors of persistent risky driving behavior and crash involvement among young adults. Injury Prevention, 13(6) 376-381. Heras, A., Vilar, J. L., & Gil, J. A. (2002). Asymptotic fairness of Bonus- Malus systems and Optimal scales premiums. The Geneva Papers on risk and Insurance Theory, 27(1), 61-82. Hey, J. (1985). No claim bonus? The Geneva Papers on risk and Insurance, 10(36), 209-228. Horgby, P.-J. (1998). Risk classification by fuzzy inference. The Geneva Papers on Risk and Insurance Theory, 23(1), 63-82. International Monetary Fund. (2011). World economic outlook database. Retrieved from www.imf.org Iversen, H. (2004). Risk-taking attitudes and risky driving behavior. Transportation Research Part F, 7(3), 135-150. Johnson, J. (2006). Can complexity help us better understand risk? Risk Management, 8(4), 227-267. Kramer, B. (1997). N.E.W.S.: A model for the evaluation of non-life insurance companies.European Journal of Operational Research, 98(2), 419-430. Laitinen, E. K. (1992). Prediction of failure of a newly founded firm. Journal of Business Venturing, 7(4), 323-340. Lemaire, J. (1988). A comparative analysis of most European and Japanese Bonus-malus Systems. Journal of Risk and Insurance, 55(4), 660-681. Lemaire, J. (1990). Fuzzy insurance. ASTIN Bulletin, 20(1), 33-55. Lemaire, J. (1995). Bonus-malus systems in automobile insurance. Boston: Kluwert Academic Publisher. Martinez de Lejarza Esparducer, I. (1996, September). Forecasting company failure: Neural approach versus discriminant analysis: An application to Spanish insurance companies of the 80´s. International Conference on Artificial Intelligence in Accounting, Finance and Tax, Punta Umbria (Huelva), Spain, 2. Matthews, G., Dorn, L., & Glendon, A. (1991). Personality correlates of driver stress. Personality and Individual Differences, 12(6), 535-549. McKee, T. (2000). Developing a bankruptcy prediction model via rough sets theory. International Journal of Intelligent Systems in Accounting, Finance and Management, 14(3), 159-173. Nordfjaern, T., Simsekoglu, O., & Rundmo, T. (2012). A comparison of road traffic culture, risk assessment and speeding predictors between Norway and Turkey. Risk Management, 14(3), 202-221. Nurmi, H., Kacprzyk, J., & Fedrizzi, M. (1996). Probabilistic, fuzzy and rough concepts in social choice. European Journal of Operational Research, 95(2), 264-277. Park, S., Lemaire, J., & Chua, C.T. (2009). Is the design of Bonus-Malus Systems influenced by insurance maturity or national culture? Evidence from Asia. The Geneva Papers, 35(S1), 7-27. Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Dordrecht: Kluwert Academic Publishers. Pawlak, Z., Grzymala-Busse, J., Slowinski, R., & Ziarko, W. (1995). Rough Sets. Communications of the ACM, 38(11), 89-97. Retrieved from http://dl.acm.org/ft_gateway.cfm?id=277421&ftid=17537&dwn=1&CFID=220789019&CFTOKEN=72446287 Pitrebois, S., Denuit, M., & Walhin, J.F. (2006). Multi-event Bonus-malus scales. The Journal of Risk and Insurance, 73(3) 517-528. PwC. (2012). The five keys to the industry. Retrieved from http://www.pwc.es/en/financieroseguros/claves-sector-seguros.jhtml Resende, P. C., Jr., & Guimãres, T. (2012). Inovação em serviços: O estado da arte e uma proposta de agenda de pesquisa. Revista Brasileira de Gestão de Negócios, 14(44), 293-313. Richadeau, D. (1999). Automobile insurance contracts and risk of accident: An empirical test using French individual data. The Geneva Papers on Risk and Insurance Theory, 24(1), 97-114. Salcedo Sanz, S., Fernández Villacañas, J. L., Segovia Vargas M. J., & Bousoño Calzón, C. (2005). Genetic programming for the prediction of insolvency in non-life insurance companies. Computers and Operations Research, 32(4), 749-765. Salcedo Sanz, S., Prado Cumplido, M., Segovia Vargas, M. J., Perez Cruz, F., & Bousoño Calzón, C. (2004). Feature selection methods involving Support Vector Machines for prediction of insolvency in non-life insurance companies. Intelligent Systems in Accounting, Finance and Management, 12(4), 261-281. Sanchís, A., Segovia, M. J., Gil, J. A., Heras, A., & Vilar, J. L. (2007). Rough Sets and the Role of Monetary Policy in Financial Stability (Macroeconomic Problem) and the Prediction of Insolvency in the Insurance Sector (Microeconomic Problem). European Journal of Operational Research, 181(3), 1554-1573. Schwebel, D. C., Severson, J., Ball, K.K., & Rizzo, M. (2006). Individual difference factors in risky driving: The roles of anger/hostility, conscientiousness, and sensation-seeking. Accident Analysis and Prevention, 38(4), 801-810. Segovia-González, M. M., Contreras, I., & Mar-Molinero, C. A. (2009). DEA analysis of risk, cost, and revenues in insurance. Journal of Operational Research Society, 60(11), 1483-1494. Segovia-Vargas M. J., Salcedo-Sanz, S., & Bousoño-Calzón, C. (2004). Prediction of Insolvency in non-life insurance companies using support vector machines and genetic algorithms. Fuzzy Economic Review, 9(1), 79-94. Slowinski, R., & Zopounidis, C. (1995). Application of the rough set approach to evaluation of bankruptcy risk. International Journal of Intelligent Systems in Accounting, Finance and Management, 4(1), 27-41. Shapiro, A. (2005). Fuzzy logic in insurance: the first 20 years. Actuarial Research Clearing House, 39(1), 1-32. Retrieved from https://www.soa.org/News-and-Publications/Publications/Proceedings/Arch/pub-arch-tableof-contents-2005-1.aspx Shen, Q., & Jensen, R. (2007). Rough sets, their extensions and applications. International Journal of Automation and Computing, 4(3), 217-228. Shyng, J.-Y., Wang, F.-K., Tzeng, G.-H., & Wu, K.-S. (2007). Rough Set Theory in analyzing the attributes of combination values for the insurance market. Expert Systems with Applications, 32(1), 56–64. Silva, J. C. B. (2004). A escolha da seguradora para o seguro fiança locatícia na óptica dos corretores de seguros. Revista Brasileira de Gestão de Negócios, 6(15),49-68. Skowron, A., & Rauszer, C. M. (1992). The discernibility matrices and functions in information systems. In R. W. Slowinski (Ed.), Intelligent decision support (Chap. 2, pp. 331-362). Dordrecht: Kluwer Academic Publishers. Spanish National Institute of Statistics (2015). Accidentes. Serie 2004-2012. Retrieved from http://www.ine.es/jaxi/menu.do?type=pcaxis&path=/t10/a109/a04/&file=pcaxis Turner, C., & McClure, R. (2003). Age and gender differences in risk-taking behavior as an explanation for high incidence of motor vehicle crashes as a driver in young males. Injury Control and Safety Promotion, 10(3), 123-130. Warsaw University. (2005). RSES 2.2 User’s Guide. Retrieved from http://logic.mimuw.edu.pl/~rses/RSES_doc_eng.pdf Wit, G. W. (1982). Underwriting and uncertainty. Insurance: Mathematics and Economics, 1(4), 277-285. Witlox, F., & Tindemans, H. (2004). The application of rough sets analysis in activity-based modeling, opportunities and constraints. Expert Systems with Application, 27(4), 585-592. Young, V. (1996). Insurance rate changing: A fuzzy logic approach. Journal of Risk and Insurance, 63(3), 461-484. Zuckerman, M., & Kuhlman, M. (2000). Personality and Risk-Taking: Common bisocial factors. Journal of Personality, 68(6), 999-1029.
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