Torkestani, Mohammad Saleh, Dehpanah, Arman, Taghavifard, Mohammad Taghi, Shafiee, Shahram. (1395). Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company). , 8(4), 711-732. doi: 10.22059/jitm.2016.59948
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)". , 8, 4, 1395, 711-732. doi: 10.22059/jitm.2016.59948
Torkestani, Mohammad Saleh, Dehpanah, Arman, Taghavifard, Mohammad Taghi, Shafiee, Shahram. (1395). 'Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company)', , 8(4), pp. 711-732. doi: 10.22059/jitm.2016.59948
Torkestani, Mohammad Saleh, Dehpanah, Arman, Taghavifard, Mohammad Taghi, Shafiee, Shahram. Providing a Framework for Reforming Premium Rates of Vehicle Collision Coverage with Using Neural Networks Model (A Case Study of Asia Insurance Company). , 1395; 8(4): 711-732. doi: 10.22059/jitm.2016.59948

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
مقاله 4، دوره 8، شماره 4، 2016، صفحه 711-732    اصل مقاله (808.4 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jitm.2016.59948
نویسندگان
Mohammad Saleh Torkestani1؛ Arman Dehpanah* 2؛ Mohammad Taghi Taghavifard3؛ Shahram Shafiee4
1Assistant Prof. in Business Administration, Allameh Tabatabaei University, Tehran, Iran
2Ph.D. Candidate in Business Administration, Islamic Azad University, Babol, Iran
3Associate Prof. in Industrial Management, Allameh Tabatabaei University, Tehran, Iran
4Assistant Prof., Faculty of Physical Education and Sports Sciences, University of Guilan, Rasht, Iran
چکیده
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.
کلیدواژه‌ها
car collision coverage؛ Data Mining؛ neural networks model؛ prediction
مراجع
آمار
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