保險業的資料探勘應用—壽險高潛力客戶

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題名:	保險業的資料探勘應用—壽險高潛力客戶
作者:	林志鎧;JR-KAI, LIN
貢獻者:	資訊管理學系在職專班
關鍵詞:	資料探勘;人壽保險;Cascade K-means;HotSpot;Data Mining;Life Insurance;Cascade K-means;HotSpot
日期:	2018-07-23
上傳時間:	2018-08-31 14:50:26 (UTC+8)
出版者:	國立中央大學
摘要:	科技的進步促使產業往前發展，而金融（Finance）與科技（Technology）的碰撞，讓金融創新展開了ㄧ個新的競爭起點與激盪出新的思維。保險公司若可藉此改善客戶之服務、著重風險之管理、並提升經營之效率，將可能藉此邁入頂尖保險公司之列。在資訊的普及及國人保險意識的抬頭下，保險商品的資訊取得已較以往迅速及便利，客戶在購買保險商品上也更精打細算，不易受到保險業務員的話術影響購買保險商品時的意願。另一方面，在個人化的行動裝置普及下，在客戶進行購買行為時，保險公司也更容易收集到大量個人化差異的交易資訊。因此傳統的保險行銷策略及技術均受到衝擊，現代化的保險公司皆已捨去任由業務員大量行銷及產品差異化行銷，輔助業務員尋找最可能銷售成功之保險商品，轉向為鎖定客戶市場區分進行目標行銷。藉由目標行銷保險公司也可以根據客戶需求，將商品區分不同的通路區隔，在不同的銷售通路提供不同的組合商品或是專案價格，以求更接近目標市場。而藉由收集交易資料集進行資料採擷便是達成此目的的重要環節之一。本研究以某保險公司承保資料為研究對象，利用資料探勘之技術找出成功銷售保險商品時客戶特徵之關聯性。資料探勘中的群集分析演算法常用於分析市場區隔，將有類似特徵的消費者區分為若干群組。本研究探討Cascade K-means演算法及商品特性區分客戶族群之差異，亦即進行顧客特徵分類，再利用HotSpot關聯演算法擷取每一群集之特徵關聯性，期能得知資料潛藏之意義。目標為找到交易資料集中客戶購買保險商品的特徵關聯性，並且發掘保險商品的市場區隔。研究的最終目標為讓保險公司可對每一客戶族群之特性加以分析與了解，依其屬性訂定專屬之行銷策略、保險商品、或培訓相關策略之專業人才。;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.
顯示於類別:	[資訊管理學系碩士在職專班 ] 博碩士論文

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