Cognitive wealth management: Human x digital
The wealth management industry has always been steeped in data. However, while traditional models have calculated risk and return based on limited data sources, such as demographics, assets, liabilities, and goals, AI-enabled tools draw on deeper and broader data sets to perform such tasks. With a cognitive wealth management model, it is possible to forgo the standard customer segments and instead establish a truly unique 360-degree view of the individual – one that calculates individual risk in a much more accurate way and takes into account everything from projected healthcare costs to real estate holdings.
A cognitive model also allows financial institutions to increase the depth of data. Whereas traditional advisors look at investor data from the years that they managed a client’s portfolio, an AI-enabled cognitive approach can look back as far as records are available for the individual and the market, thus identifying trends and patterns that are impractical for human advisors to produce.
Further, as social media and other online platforms continue to play a growing role in customers’ lives, it is important to consider what these channels may tell us about the client – particularly with respect to relative unknown areas, such as spending patterns, hobbies, and other lifestyle habits. As digital engagement continues to grow – especially among younger consumers – it is important for wealth management firms to incorporate these data sources into their assessments.
In many cases, firms rely on self-reporting tools to gather the data to calculate risk that is subject to the investor’s irrational thinking and biases, as well as their selective memory. A cognitive model, on the other hand, takes client-provided data and then cross references with other sources. In providing these checks and balances, this method improves objectivity and reduces human bias. Further, in a traditional model, the fund selection can be susceptible to the advisor’s conflicts of interest. The cognitive approach diminishes this risk by acting as a fiduciary, automatically allocating the lowest cost funds.
Finally, the cognitive approach calculates risk in a dynamic way, meaning that it uses the latest market data, coupled with current investor needs, in real-time to determine the right advice. Unlike the traditional models, rebalancing is a proactive task that can take place as frequently as needed.
In a traditional model, wealth management teams are siloed, which means that the investor might get advice on taxes, success planning and investments from disparate sources within the same organization. What’s more, communication between these business units is often infrequent and ineffective. These groups can be unaware that they are engaging the same individual, and may be providing conflicting information about asset allocation and long-term planning. A cognitive approach can help raise awareness and share information within the organization. While addressing the silos is going to take time, this model can provide a bridge in the short-term.
With existing self-service models, robo advisors are limited to portfolio building and rebalancing. But with a cognitive model, it is easier to advise on a broader range of needs, including tax planning, goal-based advice and personal insurance. What’s more, this approach can also understand the right context for that advice. For example, if an investor searches for “living will”, this model can trigger appropriate advice on life insurance, and likely find a receptive audience.
Regulatory compliance has always been a major concern for financial institutions and in today’s digital age, the landscape is only becoming increasing complex. For U.S. financial institutions there are over 200 rules from 14 regulatory bodies that became applicable since 2010. Regulatory documents are expected to exceed 300 million pages by 2020. Rules cost the six largest US banks $70B with over 10 percent of their workforce dedicated to this area.
Existing wealth management models do not adequately protect the organization from these risks. In fact, since 2008 banks paid $321B in fines globally, thus underscoring the need for more effective tools to monitor and assess regulatory issues. While still evolving, a cognitive approach can drive immense efficiency in this area by using AI-enabled technology to ingest every rule and provide decision-making support. These platforms can also proactively alert the proper department when a potential issue arises or if a new rule applies to its business, help anticipate cyber-security risks, and build compliance awareness across company silos.