建構財富管理業務之商品適合度分析模型

Building a Fitting Model of Financial Products for the Wealth Management

李美瑩(Mei-Ying Lee)

指導教授 ： 皮世明

中原大學/商學院/資訊管理學系/碩士(2006年)

https://doi.org/10.6840/cycu200600503

摘要

根據「2004年亞洲財富大調查」發現目前財富管理業務已成為亞洲金融機極欲開拓的市場。而在國內也因為定存微利時代的來臨，客戶產生更多的理財規劃需求，加上金控公司為強化競爭力與追求更高利潤，已紛紛推出貴賓理財服務。而理財業務人員在專業的理財服務上應站在消費者的角度，為消費者提供最佳的資產負債配置建議，滿足顧客對於投資理財的需求，另一方面理財業務人員透過對客戶理財事務之規劃諮詢，獲取相關諮詢管理之費用收入，創造銀行長期利潤。然而此二者之間的關係，可能造成不協調之原因，在於商品提供部門，有可能為增進業績，積極促銷其商品，而未依客戶財富狀況、風險偏好、投資期間長短與目標，提供最適商品予客戶。

因而，金管會於2005年發佈銀行辦理財富管理業務應注意事項；要求各銀行在辦理財富管理業務，應建立一套商品適合度政策，其內容包括：(1)客戶風險等級、產品風險等級之分類，俾依據客戶風險之承受度提供客戶適當之商品；(2)建立監控分析機制，以避免理財業務人員不當銷售或推介之行為…等規範制度。

因此，本研究透過資料倉儲、商業智慧系統等整合性分析工具的輔助，將分散的產品、行銷、客戶等相關資訊加以蒐集並彙整，提供給相關管理決策人員分析應用。透過這些分析可以找到資料背後潛在的知識，並將這些知識轉為有助於企業決策的輔助資訊。整個的研究架構以學者(Arun. S. and Atish. P. S.)在“A Comparison of Data Warehousing Methodologies” 所提到的資料倉儲方法論為主軸，做為商業需求分析、資料模型與資料倉儲架構設計之主要參考依據，並透過個案研究方法，實際針對單一個案進行訪談及資料蒐集。

本研究以銀行業為對象定義銀行業者在財富管理業務之商品適合度為範疇，針對銀行業在財富管理的商品適合度所涉及之產品、市場與客戶投資組合等業務方面，分析和設計相關資訊。目的在建構出本研究個案環境中對於「財富管理業務之商品適合度分析」之分析模型架構。最後，本研究將商品適合度之特性分析、歸納如下：

1、客戶特性：主要在瞭解其客戶圖像組成、投資行為及投資狀況分析。
2、產品特性：因為產品的屬性不同，其資產類別可被歸類成債券、股票、結構型保本、結構型不保本，其分析的面相可分成「部位分佈」、「風險等級」、「營運績效」、「流動性」和「市場報酬」…等分析屬性。

關鍵字

多維度模型 ； 財富管理 ； 商業智慧 ； 資料倉儲 ； 商品適合度

並列摘要

According to the 2004 Asian Wealth Survey, Wealth Management Service is a market sector that Asian financial service companies are eager to enter. The advent of micro-interest-rate has boosted the demand of personal financial management service, and holding companies are offering concierge financial service in order to strengthen their competitiveness and to maximize profit. In this context, financial advisers are expected to stand in client’s interest and provide them with best financial advice, while banks receive service fees and look to create long-term profit through.

However, as banks becomes more occupied be with promoting service products, client’s interest could be compromised as financial advice they receive may not be made based consideration about their financial status, risk preference, long-term and short-term investment goals.

Taiwan’s Financial Supervisory Commission in 2005 published Notices for Wealth Management Service, which requires banks, when undertaking Wealth Management Services, to establish a product suitability policy that addresses the followings: (1) classification of client’s risk grade and product risk grade, which should serve as a foundation to match clients with suitable products (2) the establishment of a regulatory mechanism that prevents improper reference or sales of financial service products.

Through integrated analysis tools, such as Data Warehouse and Business Intelligence, this research looks to collect and digest diverse information concerning product, marketing and client for the analysis. It is hoped that through analysis, the knowledge underlying the data can be identified and turned into useful information for enterprise decision makers. Data Warehouse, a research methodology that Arun. S. and Atish. P. S. proposed in the“A Comparison of Data Warehousing Methodologies”is employed in this research as the main approach to conduct demand analysis, data model and data warehouse designing. Case study is also conducted for each case for interviews and data collection.

The scope of this research is fitting of financial products for the wealth management offered by the banking. In the aspect of market and portfolio of customer investment that concerns wealth management service, this research analyzes and design relevant information, with an aim to establish an analysis model that can be used in the case study of fitting of financial products for the wealth management. This research concludes characteristics analysis as follows:

1、Customer characteristics: it’s mainly used to look into customer profile, investment behavior and to analyze investment status.
2、Product characteristics: products are mainly categorized into fund-type and structure-type products; assets of products are categorized into bond, equity, principal protected structured product and non-principal protected structured; analysis dimensions are position distribution, grade of risk, performance, liquidity, market returns, etc.

並列關鍵字

Data Warehouse ； Fitting of Financial Products ； Wealth Management ； Business Intelligence ； Dimesional Data Model

參考文獻

[26]朱慧蘭，「商業智慧系統之設計與應用」，中原大學資訊管理研究所未出版碩士論文，民92。

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[2]Arun. S., and Atish. P. S., “A Comparison of Data Warehousing Methodologies,” Communications of the ACM, 48(3), 2005.

[7]Cunningham, C., Song, I. Y., and Chen, P. P., “Data Warehouse Design to Support Customer Relationship Management Analyses,” ACM, 2004.

[8]Hammergren, T., “Data Warehousing - Building the Corporate Knowledge Base,” International Thomas Publishing Company, 1996.