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Information Journal Paper

Title

Insurance Claim Classification: Genetic Programming Approach (Applied-Research Paper)

Pages

  437-446

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. The model accuracy is over 90% and the outcomes indicate that car type, car plate, car color and car age were the main four factors contributing in prediction of claims.

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    APA: Copy

    BAHIRAIE, ALIREZA, Khanizadeh, Farbod, & KHAMESIAN, FARZAN. (2022). Insurance Claim Classification: Genetic Programming Approach (Applied-Research Paper). ADVANCES IN MATHEMATICAL FINANCE AND APPLICATIONS, 7(2), 437-446. SID. https://sid.ir/paper/1032273/en

    Vancouver: Copy

    BAHIRAIE ALIREZA, Khanizadeh Farbod, KHAMESIAN FARZAN. Insurance Claim Classification: Genetic Programming Approach (Applied-Research Paper). ADVANCES IN MATHEMATICAL FINANCE AND APPLICATIONS[Internet]. 2022;7(2):437-446. Available from: https://sid.ir/paper/1032273/en

    IEEE: Copy

    ALIREZA BAHIRAIE, Farbod Khanizadeh, and FARZAN KHAMESIAN, “Insurance Claim Classification: Genetic Programming Approach (Applied-Research Paper),” ADVANCES IN MATHEMATICAL FINANCE AND APPLICATIONS, vol. 7, no. 2, pp. 437–446, 2022, [Online]. Available: https://sid.ir/paper/1032273/en

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