Application of Data Mining through Machine Learning Algorithms to Study Effect of Car Features in Predicting Financial Claim of Motor Third Party Liability Insurance

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Asghari Oskoei Mohammadreza; Khanizadeh Farbod; Bahador Azadeh

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Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH) Year:2020 | Volume:34 | Issue:4 Page(s): 33-66

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Information Journal Paper

Title

Application of Data Mining through Machine Learning Algorithms to Study Effect of Car Features in Predicting Financial Claim of Motor Third Party Liability Insurance

Author(s)

Asghari Oskoei Mohammadreza | Khanizadeh Farbod | Bahador Azadeh | Issue Writer Certificate

Keywords

Insurance Customer Classification

Decision Tree

Support Vector Machine

Naïve Bayes

Neural Networks

Abstract

Objective: Risk classification of insurance customers, based on the observable characteristics, can significantly help insurers mitigate losses, classify their customers and prevent adverse selection. This paper aims to study losses occurred in motor Third Party Liability (TPL) insurance and predict customers’ risk of loss. Methodology: With the help of four supervised algorithms namely; Decision Tree, SVM, naï ve Bayes and neural network hidden pattern of data is discovered to classify customers of TPL insurance. Furthermore, the imbalanced dataset was the main challenge for implementing machine learning and data mining techniques which will be discussed throughout the article. Findings: The dataset contains more than 400, 000 observations for five years from an Iranian insurance company. It also has five variables of which four are independent: car type, car group, plate type, car age; and one binary dependent variable: financial loss. Comparing the model performances, Decision Tree is the most efficient (F1=0. 72± 1). Conclusions: The model provides prioritization of independent features as follows: car type, plate type, car age, car group. Findings also suggest that to obtain more accurate prediction on claims and high-risk customers, more features concerning drivers’ traits are required.

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APA: Copy

Asghari Oskoei, Mohammadreza, Khanizadeh, Farbod, & Bahador, Azadeh. (2020). Application of Data Mining through Machine Learning Algorithms to Study Effect of Car Features in Predicting Financial Claim of Motor Third Party Liability Insurance. IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH), 34(4 ), 33-66. SID. https://sid.ir/paper/656190/en

Vancouver: Copy

Asghari Oskoei Mohammadreza, Khanizadeh Farbod, Bahador Azadeh. Application of Data Mining through Machine Learning Algorithms to Study Effect of Car Features in Predicting Financial Claim of Motor Third Party Liability Insurance. IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH)[Internet]. 2020;34(4 ):33-66. Available from: https://sid.ir/paper/656190/en

IEEE: Copy

Mohammadreza Asghari Oskoei, Farbod Khanizadeh, and Azadeh Bahador, “Application of Data Mining through Machine Learning Algorithms to Study Effect of Car Features in Predicting Financial Claim of Motor Third Party Liability Insurance,” IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH), vol. 34, no. 4 , pp. 33–66, 2020, [Online]. Available: https://sid.ir/paper/656190/en

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