Data Mining Pattern on Ratemaking of Insurance Company-Automobile Damage Insurance

• Main Authors: Sheng-Jen Hsu, 許勝仁
• Other Authors: Chao-Hsin Lin
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/64083669552914442917

碩士 === 國立高雄第一科技大學 === 風險管理與保險所 === 91 === Companies have been collecting data for decades, building massive data warehouses in which to store it. Even though this data is available, very few companies have been able to realize the actual value stored in it. The question these companies are asking is how to extract this value. The answer is Data mining. Clustering analysis is the common way to analyze the data when making data mining. There are many technologies available to data mining practitioners, including Artificial Neural Networks, Regression, and Decision Trees. This study using Self-Organization Map of artificial neural network for data mining. In this study, based on the techniques of data mining, we propose a pattern that utilizes the Two-level self-Organization Map (SOM) of artificial neural network to cluster the data of Automobile Damage Insurance. This study tries to find the features and the characteristics of underwrite and claims data by the analysis of each cluster and to discover the potentially useful information, by the construction of a ratemaking pattern. Through this pattern, insurance companies can adjustment their premium rate pricing strategies in the deregulation of insurance market.