Insurance Claim Classification: A new Genetic Programming Approach

Subject Areas : Risk Management

Alireza Bahiraie ¹ , Farbod Khanizadeh ² , Farzan Khamesian ³

1 - Faculty of Mathematics, Statistics & Computer Science, Semnan University 35131-19111, Semnan, Iran
2 - Insurance Research Centre (IRC), Tehran 1998758513, Iran
3 - Insurance Research Centre (IRC), Tehran 1998758513, Iran

Received: 2021-04-03 Accepted : 2021-09-07 Published : 2022-04-01

Keywords: Genetic Programming, Insurance Claim, Classification, supervised Learning,

Abstract :

In this study we provide insurance companies with a tool to classify the risk level and predict the possibility of future claims. The support vector machine (SVM) and genetic programming (GP) are two approaches used for the analysis. Basically, in Iran insurance industry there is no systematic strategy to evaluate the car body insurance policy. Companies refer mainly to the world experience and employ it to rate the premium. An insurance claim dataset provided by an Iranian insurance company with a sample size of 37904 is considered for programming and analysis. According to the structure of the dataset, a supervised learning algorithm was used to describe the underlying relationships between variables.

References:

[1] Acharya, V., Pedersen, L., Asset pricing with liquidity risk, Journal of Financial Economics,
2005, 77, P. 375-410, Doi:10.1016/j.jfineco.2004.06.007

 

[2] Bahiraie, A. and Alipour, M., Jump OpVaR with operational risk, International Journal of Computing Science and Mathematics, 2020, 12(3), P.213-225, Doi:10.1504/IJCSM.2019.10025236

 

[3] Bahiraie, A., Azhar, A.K.M. and Ibrahim, A. A new dynamic geometric approach for empirical analysis of financial ratios and bankruptcy, Journal of Industrial and Management Optimization, 2011, 7(4), P.947–965, Doi:10.3934/jimo.2011.7.947

 

[4] Bahiraie, A., Abbasi, B., Omidi, F., Hamzah, N.A. and Yaakub, A.H., Continuous time portfolio optimization, International Journal of Nonlinear Analysis and Applications, 2015, 6(2), P. 103–112.

Doi:10.22075/IJNAA.2015.258

 

[5] Bahiraie, A., and Alipour, M., Option Pricing Accumulated with Operational Risk, Advances in Mathematical Finance and Applications, 2020, 4, P. 437-448, Doi:10.22034/amfa.2020.1897687.1409

 

[6] Black, F., Scholes, M., The Pricing of Options and Corporate Liabilities, Journal of Political
Economy, 1973, 81, P. 637-654, Doi:org/10.1086/260062

 

[7] Chavez-Demoulin, V., Embrechts, P., Neslehova, J., Quantitative models for operational
risk: Extremes, dependence and aggregation, Journal of Banking and Finance, 2006, 30(206), P.2635-2658.

 Doi: 10.1016/j.jbankfin.2005.11.008

 

[8] Chorafas, D., Operational Risk Control with Basel II: Basic Principles and Capital Requirements,
Butterworth-Heinemann, Oxford, 2004, Doi:10.1007/978-3-642-15923-7

 

[9] Cont, R., Tankov, P., Financial Modelling with Jump Processes, second printing. Chapman and
Hall/CRC Press, London, 2004, Doi.org/10.1201/9780203485217

 

[10] Eshaghi, M. and Askari, G., Hyper-Rational Choice and Economic Behaviour, Advances in Mathematical Finance and Applications, 2018, 3(3), P. 69-76, Doi:10.22034/AMFA.2018.544950

 

[11] Elandt, R., The folded normal distribution: two methods of estimating parameters from moments, Journal of Technimetrics, 1961, 3, P.551-562, Doi:10.2307/ 1266561

 

[12] Hanson, F. B., Westman, J. J, Jump-Diffusion Stock Return Models in Finance: Stochastic
Process Density with Uniform-Jump Amplitude, working paper, University of Illinois at Chicago,
2002, Doi:10.1016/j.finmar.2013.05.002

 

[13] Hull, J. and White, A., The impact of default risk on the prices of options and other derivative
securities, Journal of Banking &Finance, 1995, 19, P. 299-322, Doi: 10.1016/0378-4266(94)00050

 

[14] Izadikhah, M., Improving the Banks Shareholder Long Term Values by Using Data Envelopment Analysis Model, Advances in Mathematical Finance and Applications, 2018, 3, P. 27-41, Doi:10.22034/amfa.2018.540829

 

[15] Jackson, P., Maude, D. J., Perraudin, W., Bank capital and value at risk, Journal of Derivatives, 1997,
4, P.73-90, Doi: 10.3905/jod.1997.407972

 

[16] Khanizadeh, F., Khamesian, F., Bahiraie, A., Customer Segmentation for Life Insurance in Iran Using K-means Clustering, International Journal of Nonlinear Analysis and Applications, 2021, 1(12), P.633-642. Doi:10.22075/IJNAA.2021.22324.2350.

 

[17] Klein. P., Pricing Black-Scholes options with correlated credit risk, Journal of Banking and Finance, 1996, 20, P.1211-1129, Doi:10.1016/0378-4266(95)00052

 

[18] Merton, R. C., Option pricing when underlying stock returns are discontinuous, Journal of Financial Economics. 1976, 3, P.125-144, Doi:10.1016/0304-405 (76)90022-2

 

[19] Mitra, S., Operational risk of option hedging, Journal of Economic Modelling, 2013, 33, P.194-203, Doi.org/10.1016/j.econmod.2013.04.031

 

[20] Sette S, Boullart L. Genetic programming: principles and applications. Engineering applications of artificial intelligence, 2001, 14, P.727-736. Doi:10.1016/s0952-1976(02)00013-1

 

[21] Su, X., Wang, W., Pricing options with credit risk in a reduced form model, Journal of the
Korean Statistical Society, 2012, 41, P.437-444, Doi:10.1016/j.jkss.2012.01.006

 

[22] Yeo AC, Smith KA, Willis RJ, Brooks M., Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry, Intelligent Systems in Accounting, Finance and Management. 2001,10, P. 39-50. Doi:10.1002/isaf.196