Seleção dos fatores de risco nas políticas de seguro de automóveis: uma maneira de aprimorar os lucros das companhias de seguro

Objective – The objective of this paper is to test the validity of using ‘bonus-malus’ (BM) levels to classify policyholders satisfactorily. Design/methodology/approach – In order to achieve the proposed objective and to show empirical evidence, an artificial intelligence method, Rough Set theory, has been employed. Findings – 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. Practical implications – To increase the prediction capacity of BM level, psychological questionnaires could be used to measure policyholders’ hidden characteristics. Contributions – The main contribution is that the methodology used to carry out research, the Rough Set Theory, has not been applied to this problem.

[1]  Zuleyka Díaz Martínez,et al.  Machine learning and statistical techniques. An application to the prediction of insolvency in spanish non-life insurance companies , 2005 .

[2]  José Luis Vilar,et al.  Asymptotic Fairness of Bonus-Malus Systems and Optimal Scales of Premiums , 2002 .

[3]  Didier Richaudeau,et al.  Automobile Insurance Contracts and Risk of Accident: An Empirical Test Using French Individual Data , 1999 .

[4]  John D. Hey,et al.  No Claim Bonus? , 1985 .

[5]  Erkki K. Laitinen,et al.  Prediction of failure of a newly founded firm , 1992 .

[6]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[7]  Jaeho Jang,et al.  A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction , 2006 .

[8]  Ray G. Taylor,et al.  Age and gender differences in perceived accident likelihood and driver competences. , 1996, Risk analysis : an official publication of the Society for Risk Analysis.

[9]  Sancho Salcedo-Sanz,et al.  Feature selection methods involving support vector machines for prediction of insolvency in non-life insurance companies , 2004, Intell. Syst. Account. Finance Manag..

[10]  G. Dionne,et al.  The (1992) Bonus-Malus System in Tunisia: An Empirical Evaluation , 2003 .

[11]  Michel Denuit,et al.  Multi-event bonus-malus scales , 2006 .

[12]  G. Matthews,et al.  DIMENSIONS OF DRIVER STRESS , 1989 .

[13]  Bert Kramer,et al.  N.E.W.S.: a model for the evaluation of non-life insurance companies , 1997 .

[14]  Jeffrey Johnson,et al.  Can Complexity Help Us Better Understand Risk? , 2006 .

[15]  Rob Law,et al.  Incorporating the rough sets theory into travel demand analysis , 2003 .

[16]  Gwo-Hshiung Tzeng,et al.  Rough Set Theory in analyzing the attributes of combination values for the insurance market , 2007, Expert Syst. Appl..

[17]  Byeong Seok Ahn,et al.  The integrated methodology of rough set theory and artificial neural network for business failure prediction , 2000 .

[18]  Jean Lemaire,et al.  Bonus-malus systems in automobile insurance , 1995 .

[19]  T. A. Guimarães,et al.  Inovação em Serviços: o estado da arte e uma proposta de agenda de pesquisa , 2012 .

[20]  M. Beynon,et al.  Variable precision rough set theory and data discretisation: an application to corporate failure prediction , 2001 .

[21]  Sancho Salcedo-Sanz,et al.  Genetic programming for the prediction of insolvency in non-life insurance companies , 2005, Comput. Oper. Res..

[22]  Roman Słowiński,et al.  A New Rough Set Approach to Evaluation of Bankruptcy Risk , 1998 .

[23]  Waldemar Karwowski,et al.  Application of measures of fuzziness to risk classification in insurance , 1992, Proceedings ICCI `92: Fourth International Conference on Computing and Information.

[24]  P. Horgby.  Risk Classification by Fuzzy Inference , 1998 .

[25]  Torbjørn Rundmo,et al.  A comparison of road traffic culture, risk assessment and speeding predictors between Norway and Turkey , 2012 .

[26]  G. W. de Wit,et al.  Underwriting and uncertainty , 1982 .

[27]  Constantin Zopounidis,et al.  Application of the Rough Set Approach to Evaluation of Bankruptcy Risk , 1995 .

[28]  Dorothy Begg,et al.  Personality factors as predictors of persistent risky driving behavior and crash involvement among young adults , 2007, Injury Prevention.

[29]  A Comparative Analysis of Most European and Japanese Bonus-malus Systems , 1988 .

[30]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[31]  R. McClure,et al.  Age and gender differences in risk-taking behaviour as an explanation for high incidence of motor vehicle crashes as a driver in young males , 2003, Injury control and safety promotion.

[32]  Virginia R. Young,et al.  Insurance Rate Changing: A Fuzzy Logic Approach , 1996 .

[33]  M. Zuckerman,et al.  Personality and risk-taking: common biosocial factors. , 2000, Journal of personality.

[34]  Frank Witlox,et al.  The application of rough sets analysis in activity-based modelling. Opportunities and constraints , 2004, Expert Syst. Appl..

[35]  Thomas E. McKee.  Developing a bankruptcy prediction model via rough sets theory , 2000 .

[36]  Salvatore Greco,et al.  Rough sets theory for multicriteria decision analysis , 2001, Eur. J. Oper. Res..

[37]  J. Kacprzyk,et al.  Probabilistic, fuzzy and rough concepts in social choice , 1996 .

[38]  Michel Denuit,et al.  Actuarial Modelling of Claim Counts: Risk Classification, Credibility and Bonus-Malus Systems , 2007 .

[39]  Constantin Zopounidis,et al.  Business failure prediction using rough sets , 1999, Eur. J. Oper. Res..

[40]  Jan G. Bazan,et al.  Rough set algorithms in classification problem , 2000 .

[41]  Sonja Forward,et al.  Driving Violations: Investigating Forms of Irrational Rationality , 2012 .

[42]  G. Matthews,et al.  Personality correlates of driver stress , 1991 .

[43]  W. Cooper,et al.  You have printed the following article : A Neural Network Method for Obtaining an Early Warning of Insurer Insolvency , 2007 .

[44]  Andrzej Skowron,et al.  The Discernibility Matrices and Functions in Information Systems , 1992, Intelligent Decision Support.

[45]  M. Rizzo,et al.  Individual difference factors in risky driving: the roles of anger/hostility, conscientiousness, and sensation-seeking. , 2006, Accident; analysis and prevention.

[46]  Cecilio Mar-Molinero,et al.  A DEA analysis of risk, cost, and revenues in insurance , 2009, J. Oper. Res. Soc..

[47]  Qiang Shen,et al.  Rough sets, their extensions and applications , 2007, Int. J. Autom. Comput..

[48]  A. Sanchis,et al.  Rough Sets and the role of the monetary policy in financial stability (macroeconomic problem) and the prediction of insolvency in insurance sector (microeconomic problem) , 2007, Eur. J. Oper. Res..

[49]  Sancho Salcedo Sanz,et al.  Prediction of insolvency in non-life insurance companies using support vector machines and genetic algorithms , 2003 .