Financial organizations and wealth management firms today deal with a wide variety of clients with different capacity to invest. As per the Securities and Exchange Board of India (SEBI) guidelines released in 2013, services offered by the wealth management division are categorized as Advisory Services and Execution Services which are independent. A client may avail only advisory services or only execution services or both from the same wealth management firm. An advisory client’s preferences for various asset classes remain almost constant over a short span of time as their Capacity to Risk (CTR) and the Attitude to Risk (ATR) remain almost same over a short span. So, the expected revenue from the existing advisory clients remain almost constant over a few years. The only revenue source for the wealth management division of the firm is the advisory fee from the advisory client and the execution fee which it charges from clients who avail the execution facilities. In order to survive in this rapidly changing business scenario, advisors need to tap the information about prospective customers from the existing clients. A prospect can be a family member, or a friend of a remote business associate of the existing client. This paper proposes a knowledge management framework to capture the tacit knowledge about a prospect, who can be a future customer of the wealth management firm’s other services/products, from the existing client during an interaction which an advisor has with the client. This knowledge captured is stored in a suitable information framework to create and extract value for the organization.

Knowledge is an important resource for creating core competences and performing innovations for both individuals and firms now and in the future. Managing this knowledge has become an important issue in the past few years. Firms and individuals have developed a variety of technologies and applications to capture this knowledge for academic research and practical use. Knowledge Management (KM) research has focused on its nature, concepts, frameworks, tools, functions and methodologies for real applications of KM technologies. Liao (2003) reviews KM technologies and applications from 1995 to 2002 on the basis of 234 articles. He classifies KM technologies into seven categories: KM framework, knowledge-based systems, data mining, information and communication technology, artificial intelligence/expert systems, database technology, and modeling. As such, organizations are targeting KM to enhance their efficiency and performance. In a highly dynamic environment, KM supports processes to create, capture and act on information which allows the organizations to adapt rapidly to changes around them (Lim and Klobas, 2000). The firms focus on KM because it helps to bring the maximum possible return to an organization by capitalizing on the expertise of its employees. The proliferation of information technology not only plays an important role in electronic commerce, but also in KM. Advances in information technology (e.g., Internet, browsers, data warehouses, data mining techniques, etc.) can be used to systematize large-scale intra and interfirm knowledge. Though training employees through development programs, organizational policies and procedures, and maintaining manuals is not new (Alavi and Leidner, 1999), the firms today are more knowledge-focused.

Knowledge Management Journal, Capacity to Risk (CTR), Attitude to Risk (ATR), Supporting Investment, Advisors, Knowledge Management Framework, Knowledge Management (KM), Client, Securities and Exchange Board of India (SEBI), Prospect Intelligence.