Model estimation of claim risk and premium for motor vehicle insurance by using Bayesian method

Riaman, Riaman; Lesmana, Eman; Wulandari, Rizky Devy; Napitupulu, Herlina; Supian, Sudradjat

doi:10.1088/1757-899x/300/1/012027

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…Bonat [10] menyatakan bahwa perluasan berbentuk distribusi Poisson-Tweedie dapat membantu mengatasi dispersi pada data terhitung, dimana dispersi terjadi dikarenakan banyaknya angka nol yang muncul pada data. Sukono [11] memberikan gambaran penggunaan metode bayes untuk menghitung besarnya premi murni dengan menggunakan harapan frekuensi klaim dan nilai harapan dari besarnya klaim, dengan distribusi yang digunakan adalah distribusi Poisson untuk frekuensi klaim dan distribusi gamma untuk besarnya klaim. Debrabant [12] melakukan identifikasi terhadap titik hitam atau titiktitik dimana terjadi kecelakaan yang tidak biasa pada suatu daerah dengan menggunaan model Poisson-tweedie, yang dapat digunakan untuk mengambil keputusan dalam pencegahan kecelakaan di masa depan.…”

Section: Pendahuluanunclassified

Penghitungan Premi Asuransi Kendaraan Bermotor Menggunakan Generalized Linear Models dengan Distribusi Tweedie

Putra¹,

Lesmana²,

Purnaba³

2021

Jambura J. Math

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ABSTRAKSeorang aktuaris mempunyai tugas penting dalam menentukan harga premi yang sesuai untuk setiap nasabah dengan risiko dan karakteristik yang berbeda. Banyak variabel yang dapat mempengaruhi harga premi. Oleh karena itu, aktuaris harus mengetahui variabel-variabel yang berpengaruh signifikan terhadap premi. Tujuan dari penelitian ini adalah untuk menentukan variabel yang dapat mempengaruhi besaran premi murni menggunakan distribusi campuran dalam menentukan besarnya premi melalui Generalized Linear Models (GLM) serta menentukan model harga premi yang sesuai berdasarkan variabel-variabel yang mempengaruhinya. Salah satu analisis statistik yang dapat digunakan untuk memodelkan premi asuransi adalah Generalized Linear Models. GLM merupakan perluasan dari model regresi klasik yang dapat mengakomodasi fleksibilitas untuk menggunakan beberapa distribusi data tetapi terbatas pada distribusi keluarga eksponensial. Dalam model GLM, premi diperoleh dengan mengalikan nilai ekspektasi bersyarat dari frekuensi klaim dan biaya klaim. Berdasarkan penelitian yang telah dilakukan diketahui bahwa frekuensi klaim dan besarnya klaim mengikuti distribusi Tweedie. Dari kedua model tersebut diketahui bahwa variabel yang mempengaruhi premi murni adalah jumlah anak, pendapatan per bulan, status pernikahan, pendidikan, pekerjaan, penggunaan kendaraan, besarnya bluebook yang dibayarkan, dan jenis kendaraan nasabah. Hal ini menunjukkan bahwa model GLM merupakan model yang representatif dan berguna bagi perusahaan asuransi. ABSTRACTIt is an important task for an actuary in determining the appropriate premium price for each customer with different risks and characteristics. Many variables can affect the premium price. Therefore, actuaries must determine the variables that significantly affect the premium. The purpose of this study is to determine the variables that can affect the amount of pure premium using a mixed distribution in determining the amount of premium through Generalized Linear Models (GLM) and determine the appropriate premium price model based on the variables that influence it. One of the statistical analyzes that can be used to model insurance premiums is the Generalized Linear Models. GLM is an extension of the classic regression model that can accommodate the flexibility of its users to use multiple data distributions but is limited to the exponential family distribution. In the GLM model, the premium is obtained by multiplying the conditional expected value of the frequency of claims and the cost of claims. Based on the research that has been done, it is known that the frequency of claims and the size of claims follow the Tweedie distribution. From the two models, it is known that the variables affecting the pure premium are the number of children, monthly income, marital status, education, occupation, vehicle use, the number of bluebooks paid, and the type of vehicle from the customer. This shows that the GLM model is a representative and useful model for the insurance company business.

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Section: Pendahuluanunclassified

Penghitungan Premi Asuransi Kendaraan Bermotor Menggunakan Generalized Linear Models dengan Distribusi Tweedie

Putra¹,

Lesmana²,

Purnaba³

2021

Jambura J. Math

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show abstract

“…Other authors assert that earned premiums have a major impact on performance of insurance companies (see Kumaga,Kuttu & Asuming [19]). Sukono et al [20] maintain that performance can be enhanced through application of claims risk and premium predictive models. Their work is consistent with Segovia-Vargas et al [21] who posit that risk classification models can be used to manage performance.…”

Section: The Research Problem and Justification Of Studymentioning

confidence: 99%

Performance Evaluation of Motor Insurance Companies: Panel Data Evidence from Nigeria

Ugwuanyim¹,

Onwuegbuchunam²,

Bartholomew³

et al. 2021

JTTs

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This paper evaluated performance of motor insurance companies in Nigeria. The objectives were to determine the following: 1) significant effects of claims settlements on motor insurance firms' earned premium; 2) differences in managerial/technological capabilities among the companies and 3) effects of policy (or time effect) on insurance firms' output within the study period. Panel data obtained for this study comprised operational data on premium earned and direct claims settlement by these companies over a period of six (6) years. Using panel data statistical models, we found that direct claims settlement negatively affected insurance companies' earned premium. Also, significant differences in technological and managerial capabilities were found to exist among the companies, though only one company exhibited this heterogeneity. Besides, there were no policy impacts (or time effect) on vehicle insurance firms' output in the study period. Policy implications of the results were discussed.

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“…Sukono [9] explored the determination of risk model claims and vehicle insurance premiums using Bayesian method technique. It is axiomatised that a Poisson distribution governs the frequency of claims, while some claims are governed by a Gamma distribution.…”

Section: Pre-processingmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Processing of Health Insurance Claim

Saxena¹,

Seth²,

Chawla³

et al. 2020

IJAPIE

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The health insurance industry protects against financial losses resulting from various health conditions. Since a long, it has relied on statistics and data to calculate risks and thereby, centre attention more profoundly on a particular target audience for increasing the operational efficiency of the industry. Technologies like Machine Learning and Artificial Intelligence prove to be an efficient tool for enabling insurance companies to predict the Customer Lifetime Value (CLV). This can be done using customer lifestyle behaviour data allowing to assess the customer's potential profitability for insurance companies. This creates a more personalised marketing offer within the audience. The insurance industry and its components constitute a dynamic and competitive sector representing approximately 2.7 percent of the US Gross Domestic Product (GDP). As customers have become progressively scrupulous about narrowing down their specific requirements, insurers and insurance companies are scrutinizing techniques for improving business operations and consumer satisfaction. An attempt in this regard has been made to analyse the “sample insurance claim prediction dataset" using various machine learning models including Decision tree, Random Forest algorithms, Naïve Bayes, K-nearest neighbour algorithm, Supper Vector machines and Neural Networks. A comparative analysis is performed to generate reports.

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Model estimation of claim risk and premium for motor vehicle insurance by using Bayesian method

Cited by 11 publications

References 7 publications

Penghitungan Premi Asuransi Kendaraan Bermotor Menggunakan Generalized Linear Models dengan Distribusi Tweedie

Penghitungan Premi Asuransi Kendaraan Bermotor Menggunakan Generalized Linear Models dengan Distribusi Tweedie

Performance Evaluation of Motor Insurance Companies: Panel Data Evidence from Nigeria

Comparison of Machine Learning Algorithms for Processing of Health Insurance Claim

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