期刊名称：International Journal of Mechatronics, Electrical and Computer Technology
印刷版ISSN：2305-0543
出版年度：2018
卷号：8
期号：27
页码：3764-3771
出版社：Austrian E-Journals of Universal Scientific Organization
摘要：Insurance industry is one of the most important issues in both economy and human being life in modern societies which awards peace and safety to the people by compensating the financial risk of detriments and losses. This industry, like others, requires to choose some strategies to obtain desired ranking and remain in competitive market. One of efficient factors which affects enormous decision makings in insurance is paying attention to important information of customers and bazar that each insurance company stores it in its own database. But with daily increasing data in databases, although hidden knowledge and pattern discovery using usual statistical methods is not impossible, it is so complicated and time-consuming. In this paper we employ data mining as a powerful approach for extracting hidden knowledge and patterns on massive data to guide insurance industry. For example, one of the greatest deleterious challenges here is interacting between insurance companies and policyholders which creates a feasible situation for fraudulent claims. Due to importance of this issue, after investigating different ways of fraudulent crimes in insurance, we use K-Means clustering technique to find fraud patterns in automobile insurance include body and third-party. Our experimental results indicate a high accuracy when have been compared with statistical information extracted from data sets. Outcomes show significant relations among efficient factors in similar fraud cases.
关键词：automobile insurance; data mining; clustering; k-means.
