Predicting the claim amount from car insurance using multiple linear regression: a case study of Iran insurance
The escalating annual insurance costs nationwide have sparked a growing interest among insurance industry managers and policymakers in analyzing insurance data to forecast future costs. Accurately predicting the number of claims and implementing appropriate policies can help mitigate potential losse...
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| Format: | Article |
| Language: | English |
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REA Press
2023-09-01
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| Series: | Big Data and Computing Visions |
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| Online Access: | https://www.bidacv.com/article_190318_62c1972ed672cd883789169b4167f352.pdf |
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| author | Reza Rasinojehdehi Soheil Azizi |
| author_facet | Reza Rasinojehdehi Soheil Azizi |
| author_sort | Reza Rasinojehdehi |
| collection | DOAJ |
| description | The escalating annual insurance costs nationwide have sparked a growing interest among insurance industry managers and policymakers in analyzing insurance data to forecast future costs. Accurately predicting the number of claims and implementing appropriate policies can help mitigate potential losses for insurance companies and customers. This study focuses on predicting the amount of customer claims and utilizes data from 128 individuals insured by Iran insurance company. The dataset includes various attributes such as the age of the vehicle owner, type of car, age of the car itself, number of claims, and the corresponding claim amounts (measured in 10,000 Tomans) recorded in the year 1400. All features, except the claim amount (the target variable), were discretized into ordinal variables to ensure accurate analysis and address any outliers or data inconsistencies. Multiple linear regression was employed to predict the target variable, enabling an investigation into the influence of each feature on estimating the claim amount. The data analysis was conducted using IBM SPSS MODELER software, allowing for a comprehensive examination of the assumptions associated with the regression model. By leveraging this approach, insurance industry stakeholders can gain valuable insights into predicting claim amounts and make informed decisions to optimize their operations and minimize potential financial risks. |
| format | Article |
| id | doaj-art-62dbaed6f5f743d5a889f4d26582708d |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2023-09-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-62dbaed6f5f743d5a889f4d26582708d2025-01-30T12:23:02ZengREA PressBig Data and Computing Visions2783-49562821-014X2023-09-013312513610.22105/bdcv.2024.424709.1169190318Predicting the claim amount from car insurance using multiple linear regression: a case study of Iran insuranceReza Rasinojehdehi0Soheil Azizi1Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.Department of Accounting and Management, Allameh Tabatabai University, Tehran, Iran.The escalating annual insurance costs nationwide have sparked a growing interest among insurance industry managers and policymakers in analyzing insurance data to forecast future costs. Accurately predicting the number of claims and implementing appropriate policies can help mitigate potential losses for insurance companies and customers. This study focuses on predicting the amount of customer claims and utilizes data from 128 individuals insured by Iran insurance company. The dataset includes various attributes such as the age of the vehicle owner, type of car, age of the car itself, number of claims, and the corresponding claim amounts (measured in 10,000 Tomans) recorded in the year 1400. All features, except the claim amount (the target variable), were discretized into ordinal variables to ensure accurate analysis and address any outliers or data inconsistencies. Multiple linear regression was employed to predict the target variable, enabling an investigation into the influence of each feature on estimating the claim amount. The data analysis was conducted using IBM SPSS MODELER software, allowing for a comprehensive examination of the assumptions associated with the regression model. By leveraging this approach, insurance industry stakeholders can gain valuable insights into predicting claim amounts and make informed decisions to optimize their operations and minimize potential financial risks.https://www.bidacv.com/article_190318_62c1972ed672cd883789169b4167f352.pdfdata analysispredictionregressioninsurance claim amount |
| spellingShingle | Reza Rasinojehdehi Soheil Azizi Predicting the claim amount from car insurance using multiple linear regression: a case study of Iran insurance Big Data and Computing Visions data analysis prediction regression insurance claim amount |
| title | Predicting the claim amount from car insurance using multiple linear regression: a case study of Iran insurance |
| title_full | Predicting the claim amount from car insurance using multiple linear regression: a case study of Iran insurance |
| title_fullStr | Predicting the claim amount from car insurance using multiple linear regression: a case study of Iran insurance |
| title_full_unstemmed | Predicting the claim amount from car insurance using multiple linear regression: a case study of Iran insurance |
| title_short | Predicting the claim amount from car insurance using multiple linear regression: a case study of Iran insurance |
| title_sort | predicting the claim amount from car insurance using multiple linear regression a case study of iran insurance |
| topic | data analysis prediction regression insurance claim amount |
| url | https://www.bidacv.com/article_190318_62c1972ed672cd883789169b4167f352.pdf |
| work_keys_str_mv | AT rezarasinojehdehi predictingtheclaimamountfromcarinsuranceusingmultiplelinearregressionacasestudyofiraninsurance AT soheilazizi predictingtheclaimamountfromcarinsuranceusingmultiplelinearregressionacasestudyofiraninsurance |