dc.description.abstract | Technological evolution drives the development of industry. When technology and the financial industry are brought together, it inspires creative ideas and new competition in financial innovation. For insurance companies, the introduction of FinTech may help improve customer service, strengthen risk management, elevate management efficiency, or even push the company into the list of top insurers. With the convenient availability of information and increased public awareness of insurance concepts in Taiwan, knowledge about insurance products can be easily and quickly acquired. Customers are becoming more price-sensitive when purchasing related products, and less likely to be convinced by insurance agents. On the other hand, the prevalence of personal mobile devices makes it easier for insurance companies to acquire a large amount of differentiated transaction information from individual customers when they purchase insurance policies. Such changes have impacted the traditional marketing strategies and techniques of the industry. Insurance companies nowadays have to give up the conventional methods of soliciting business, where mass marketing and product differentiation are encouraged to help insurance agents identify potential “best sellers.” Instead, insurers tend to concentrate their efforts on target marketing, trying to focus on selected customer segments. Through target marketing, insurance companies can also arrange different distribution channels for different products based on customer demand. Variant product combinations or price packages are offered to different sales channels so as to better accommodate the needs of the target market. Data collection and subsequent data mining, no doubt, are important steps to achieve these purposes.
This study is targets the underwriting information of an insurance company. Using data mining techniques, the research endeavors to explore the relationship between customer characteristics and the insurance products which have been sold successfully. The cluster algorithms adopted herein are technique frequently used to analyze market segmentation, where consumers with similar characteristics are grouped together. For this research, Cascade K-means algorithm is used to classify the customers, taking into consideration product attributes. In other words, customers are first grouped based on their characteristics. The relationship among the clusters are then analyzed using the HotSpot Algorithm, so as to probe the underlying significance of this data. These approaches are taken to explore the relation of customer characteristics for those insurance products with transaction data, and in the meantime identifying market segmentation for different products. The ultimate objective of this research is to help the insurance company analyze and understand the unique features of each customer group, provide tailor-made marketing strategies and insurance products, or develop professionals that are able to fulfill related strategies based on these attributes. | en_US |