Era of the Smarter Advisor delivered with Cognitive Computing
Co-Authored by Brian Walter, Watson Transformation Partner, Banking & Financial Markets, IBM Global Business Services
As investors recover from the recent financial crisis, the individualized need for wealth management has never been more acute. Gone are the days when pension plans or social security provided assurance for retirement. Today, the onus is on the individual to manage their lifecycle of savings and retirement, and be the ultimate keeper of their financial health. Leading wealth managers have responded by reorienting toward goals-based investing and away from a simple asset allocation and performance focus. Together with the rapid growth of data and challenges of meeting evolving client expectations, a fundamental shift in thinking and re-tooling is needed.
Firms need to shift their mindset from developing applications with complicated dashboards, elaborate alerts, and inventories of reports to what’s really important: how the primary user, the Advisor thinks and performs their daily work. Three key user objectives need to be at the center of this shift: (1) Smarter Relationships: understand the client, (2) Smarter Teams: form more effective teams, and (3) Smarter Conversations: more relevant and higher value conversations. Cognitive computing represents a new set of technologies capable of human-like interactions, reasoning, context-based deduction and self-learning. This is wellsuited for the complex wealth management business. IBM's Watson is one of the first cognitive computing platforms to be built and it is designed to provide insight into dynamic, information-rich situations where data sources are numerous, data creation is unprecedented and is often conflicting—an environment that aptly characterizes financial advisory.
Today’s client profiles typically contain limited static information, and as result advisors spend a lot of their time “filling in the blanks” for other members of the solution team. Exponential growth in the availability of sources of data in today’s digital age, including social media, call transcripts and the public personas common to high net worth clients, have significantly increased the volume, velocity and complexity of the challenge. Robust 360 Profiles can help financial advisors simultaneously explore and derive hidden gems and insights from traditionally "siloed" enterprise data. Sentiment Analysis using text analytics and natural language processing can distinguish between positive and negative commentary and filter important items from background noise. By analyzing client email interactions, service center call logs, and social media feeds, a virtual currency can be created for firms seeking real-time opinion snapshots and gauges on financial wellness and client service satisfaction to support retention efforts. Furthermore, Personality Insights leverages user modeling and linguistic analytics to infer personality characteristics, intrinsic needs and values of individuals. Businesses can improve new client acquisition, retention, and engagement, and strengthen existing relationships by building multidimensional portraits.
Financial advisors serve as the client facing quarterback for the firm’s broader network of experts and specialists. However, expertise is often difficult to locate across verticals and is sub-optimally utilized to reduce costs or maximize revenue opportunities. By connecting experts based on actual experience and not just the directory professional profile, Expertise Finder will help organizations build engaged, social and collaborative teams that drive deeper client relationships and deliver better business outcomes. Smarter Process can elevate everyday routines such as opening accounts, determining eligibility, and managing inquiries into competitive advantages. By delivering an integrated enterprise with full visibility, control and management of business processes, Smarter Process can make real-time adjustments to operations while ensuring strict compliance to business rules through decision management and automation capabilities. Finally, to address emerging business demands, Workforce Analytics can combine industry skills frameworks, role-based competency testing, project management tools and consulting services for greater precision in role and team design. Machine learning can continuously compile and analyze high-volume workforce data—from exit interviews to real-time SaaS-based employee analysis—to understand risk factors and reduce attrition.
Clients expect wealth advisors to know them and engage with them in innovative ways at the right time, in the right context. These deepened personalized experiences lead to new clients, new partnerships and long term, high value relationships. However, current tools, which bombard wealth managers as big data generates alerts, can lead to big headaches. Wealth managers need smarter productivity tools that help bubble up the most important and relevant talking points or action items that deliver high client impact. Conversation Starter presents to-do’s or discussion items in a prioritized manner with the underlying evidence and detail to accelerate the advisor’s ability to service client needs. The time it takes to prep for client meetings is decreased while a high level of relevancy and client service is maintained. Recommendation Engine will improve upon today’s static and rules-based systems limited to narrow, scripted processes. Cognitive capabilities will present recommendations based on real-time interactions and guide users to consider the best activity to build the relationship, based on their specific responses, profiles or habits. Lastly, leading wealth management firms are experimenting with Deep Q&A technologies that allow for natural language inquires on complex financial data streams. For example, firms are exploring the possibility of answering natural language questions such as “what are the most important calls I need to make this morning” or “what are the top financial concerns of clients like mine.” While the nuances of portfolio analysis, risk return assessment and trade off analytics continue to reside in proprietary systems built by advanced financial experts, cognitive applications can help make these systems more robust and accessible.
The road ahead
A promising journey awaits for organizations ready to take an analytics and cognitive-enabled approach to managing their business and enhancing client relationships, teams and conversations. Leading firms will move from paper to mobile, from spreadsheet to visualization, from reactive to predictive, from a limited view to 360 degree client relationship data, and from static to learning cognitive systems. Smarter enterprises that create a single "system of satisfaction" and infuse intelligence throughout their organizations will lead their industries and benefit from larger wallet share, greater assets under management, enhanced advisor productivity and most importantly, loyal Roger Hu and happy clients.