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Identifying the Effective Factors in the Profit and Loss of Vehicle Third Party Insurance for Insurance Companies via Data Mining Classification Algorithms

机译:通过数据挖掘分类算法识别保险公司车辆第三方保险损益的影响因素

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Background: Insurance companies for surviving and keeping the market, always emphasize on profitability and reduction of their losses. Methods: Investigation of the Car Insurance Information shows that main factors which effect on profit or loss of insurance companies include: type of vehicle usage, license, type of license and compliance or lack of compliance with the vehicle, the amount of the premium, rate of the commitment, the car quality of the car companies, the age of the driver, driver education, the mismatch premiums with the insurance case, the delay in the renewal of insurance policies. Findings: In this paper, using data mining information of 2011year of third party insurance in Iran insurance companies in Kohgiluyeh and Boyer Ahmad province are studied. The results showed that using the classification algorithm with over 91% accuracy and decision trees with over 96% accuracy able to provide a model to identify effecting factors and determine their impact on the profit and loss of vehicle third party insurance. Applications/Improvements: Comparing results indicated that, the decision tree Wj48 with very high accuracy will able to detect and predict the occurrence of the damage of an insurance, properly. After that two other algorithms as well as with high accuracy and the same have this ability.
机译:背景:保险公司为了生存和保持市场,始终强调盈利能力和减少损失。方法:对汽车保险信息的调查显示,影响保险公司损益的主要因素包括:车辆的使用类型,牌照,牌照的类型以及车辆的合规性或不合规性,保费金额,费率承诺,汽车公司的汽车质量,驾驶员的年龄,驾驶员的教育程度,与保险案不符的保费,延误保险单的更新。调查结果:本文使用数据挖掘的信息,对Kohgiluyeh和Boyer Ahmad省的伊朗保险公司的2011年第三方保险进行了研究。结果表明,使用分类算法的准确率超过91%,决策树的准确率超过96%,可以提供模型来识别影响因素并确定其对车辆第三方保险损益的影响。应用/改进:比较结果表明,决策树Wj48的准确性非常高,能够正确检测和预测保险损害的发生。此后,另外两个算法以及具有相同精度的算法都具有此功能。

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