A Data Mining Approach for Risk Assessment in Car Insurance: Evidence from Montenegro

This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such is the one in Montenegro.

[1]  Zoran Obradovic,et al.  Component-based decision trees for classification , 2011, Intell. Data Anal..

[2]  Kate Smith-Miles,et al.  Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry , 2001, Intell. Syst. Account. Finance Manag..

[3]  Weiru Chen,et al.  Sequential Patterns Postprocessing for Structural Relation Patterns Mining , 2010, Strategic Advancements in Utilizing Data Mining and Warehousing Technologies.

[4]  R. Artes,et al.  Estimating total claim size in the auto insurance industry: a comparison between tweedie and zero-adjusted inverse gaussian distribution , 2011 .

[5]  Jianbing Xiahou,et al.  Notice of RetractionAnalysis on application of data mining upon rate making and risk analysis in insurance , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[6]  Deanne Larson,et al.  Agile Methodologies for Business Intelligence , 2012 .

[7]  Min Song,et al.  Handbook of Research on Text and Web Mining Technologies , 2008 .

[8]  Danny Samson,et al.  Linear models as aids in insurance decision making: The estimation of automobile insurance claims , 1987 .

[9]  Kate Smith-Miles,et al.  An analysis of customer retention and insurance claim patterns using data mining: a case study , 2000, J. Oper. Res. Soc..

[10]  M. Merz,et al.  Stochastic Claims Reserving Methods in Insurance , 2008 .

[11]  Bent Jørgensen,et al.  Fitting Tweedie's Compound Poisson Model to Insurance Claims Data: Dispersion Modelling , 2002, ASTIN Bulletin.

[12]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[13]  G. V. Kass.  An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .

[14]  Ramón López de Mántaras,et al.  A distance-based attribute selection measure for decision tree induction , 1991, Machine Learning.

[15]  D. Brillinger.  A Generalized Linear Model With “Gaussian” Regressor Variables , 2012 .

[16]  William P. Wagner,et al.  The Current State of Analytics in the Corporation: The View from Industry Leaders , 2010, Int. J. Bus. Intell. Res..

[17]  Andreas Christmann,et al.  An approach to model complex high–dimensional insurance data , 2004 .

[18]  Jatinder N. D. Gupta,et al.  Intelligent Enterprises of the 21st Century , 2003 .

[19]  K Aparna,et al.  Comprehensive Study and Analysis of Partitional Data Clustering Techniques , 2015 .

[20]  Leo Guelman,et al.  Gradient boosting trees for auto insurance loss cost modeling and prediction , 2012, Expert Syst. Appl..

[21]  W. Loh,et al.  SPLIT SELECTION METHODS FOR CLASSIFICATION TREES , 1997 .

[22]  M. Raisinghani.  E-Pricing for Intelligent Enterprises: A Strategic Perspective , 2004 .

[23]  James Guszcza,et al.  A Bayesian non‐linear model for forecasting insurance loss payments , 2012 .

[24]  Gillian Z. Heller,et al.  Generalized Linear Models for Insurance Data , 2008 .

[25]  G. Heller,et al.  The Zero-adjusted Inverse Gaussian distribution as a model for insurance claims. , 2006 .

[26]  Stanley R. M. Oliveira,et al.  Privacy-Preserving Data Mining on the Web: Foundations and Techniques , 2006 .

[27]  S. Sathiya Keerthi,et al.  A Fast Dual Algorithm for Kernel Logistic Regression , 2002, 2007 International Joint Conference on Neural Networks.

[28]  Richard Verrall,et al.  A generalized linear model with smoothing effects for claims reserving , 2011 .

[29]  Juan Antonio Pastor Collado,et al.  Business Intelligence and Agile Methodologies for Knowledge-Based Organizations: Cross-Disciplinary Applications , 2011 .

[30]  A. E. Renshaw,et al.  Modelling the Claims Process in the Presence of Covariates , 1994 .

[31]  Graham J. Williams,et al.  Mining the Knowledge Mine: The Hot Spots Methodology for Mining Large Real World Databases , 1997, Australian Joint Conference on Artificial Intelligence.

[32]  Tao Wang,et al.  Data Mining Techniques Applied in the Financial Industry , 2013 .