Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company)
Since vehicle collision coverage, unlike what it seems, is not very profitable for insurance companies and is moving towards making losses, this paper considered the adequacy of measures and rates used by insurance companies, and intended to optimize the methods by employing more scientific approach...
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| Format: | Article |
| Language: | English |
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University of Tehran
2016-12-01
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| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_59948_ea8c30120134e201511ba102389c314c.pdf |
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| _version_ | 1850260141815889920 |
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| author | Mohammad Saleh Torkestani Arman Dehpanah Mohammad Taghi Taghavifard Shahram Shafiee |
| author_facet | Mohammad Saleh Torkestani Arman Dehpanah Mohammad Taghi Taghavifard Shahram Shafiee |
| author_sort | Mohammad Saleh Torkestani |
| collection | DOAJ |
| description | Since vehicle collision coverage, unlike what it seems, is not very profitable for insurance companies and is moving towards making losses, this paper considered the adequacy of measures and rates used by insurance companies, and intended to optimize the methods by employing more scientific approaches. In order to do so, first, the factors affecting the risk of policy holders were identified and after comparing these factors with existing data in the database of surveyed company, the final factors were selected. Then, after preprocessing these data, prediction of the damage class and the quantity of policyholders’ potential damages were accomplished using neural networks model. So that, with using these results and considering their damage ratio, insurance companies could define optimized premium rates for their policies. The results showed that the offered model was able to predict the damage class and potential damages of policy holders respectively with 91 and 87 percent accuracy. |
| format | Article |
| id | doaj-art-d018f0cafb554ac59ae5d5576773ca82 |
| institution | OA Journals |
| issn | 2008-5893 2423-5059 |
| language | English |
| publishDate | 2016-12-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj-art-d018f0cafb554ac59ae5d5576773ca822025-08-20T01:55:42ZengUniversity of TehranJournal of Information Technology Management2008-58932423-50592016-12-018471173210.22059/jitm.2016.5994859948Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company)Mohammad Saleh Torkestani0Arman Dehpanah1Mohammad Taghi Taghavifard2Shahram Shafiee3Assistant Prof. in Business Administration, Allameh Tabatabaei University, Tehran, IranPh.D. Candidate in Business Administration, Islamic Azad University, Babol, IranAssociate Prof. in Industrial Management, Allameh Tabatabaei University, Tehran, IranAssistant Prof., Faculty of Physical Education and Sports Sciences, University of Guilan, Rasht, IranSince vehicle collision coverage, unlike what it seems, is not very profitable for insurance companies and is moving towards making losses, this paper considered the adequacy of measures and rates used by insurance companies, and intended to optimize the methods by employing more scientific approaches. In order to do so, first, the factors affecting the risk of policy holders were identified and after comparing these factors with existing data in the database of surveyed company, the final factors were selected. Then, after preprocessing these data, prediction of the damage class and the quantity of policyholders’ potential damages were accomplished using neural networks model. So that, with using these results and considering their damage ratio, insurance companies could define optimized premium rates for their policies. The results showed that the offered model was able to predict the damage class and potential damages of policy holders respectively with 91 and 87 percent accuracy.https://jitm.ut.ac.ir/article_59948_ea8c30120134e201511ba102389c314c.pdfcar collision coverageData Miningneural networks modelPrediction |
| spellingShingle | Mohammad Saleh Torkestani Arman Dehpanah Mohammad Taghi Taghavifard Shahram Shafiee Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company) Journal of Information Technology Management car collision coverage Data Mining neural networks model Prediction |
| title | Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company) |
| title_full | Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company) |
| title_fullStr | Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company) |
| title_full_unstemmed | Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company) |
| title_short | Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company) |
| title_sort | providing a framework for reforming premium rates of vehicle collision coverage with using neural networks model a case study of asia insurance company |
| topic | car collision coverage Data Mining neural networks model Prediction |
| url | https://jitm.ut.ac.ir/article_59948_ea8c30120134e201511ba102389c314c.pdf |
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