Insight

Given these findings, it’s perhaps unsurprising that many financial services organizations have reaffirmed the need for human oversight, even as the world continues to reach new levels of digitization. In fact, just one year after issuing its $8T forecast, BI Intelligence released a follow up report, revising the forecasted figure for assets under management by roboadvisors to just $1T by 2020. So too are they realizing that the hybrid model—a wealth management approach that combines digital technology and human touch—is both outdated and inadequate. Instead, there is a need for financial institutions to refine this model and look to the opportunity that deeper, more advanced application of AI presents.  

A recent report published by ESI Thoughtlab confirms that the use of AI is still in its infancy among wealth and asset management firms. At present, most organizations have only adopted “unintelligent” robotic process automation (RPA), which is largely used to address administrative tasks and improve the productivity of advisors.  

However, the power of advanced AI—which includes intelligent process automation, machine learning and deep learning—can go beyond basic automation and perform much more complex tasks, such as accurately tracking market opportunities, anticipating and addressing compliance issues and even identifying new clients.

We call this shift to a deeper, more intelligent use of AI “cognitive wealth management.” Like traditional hybrid models, cognitive wealth management uses AI to provide client-facing tools, such as self-service onboarding, portals and interactive dashboards. However, it also goes deeper, targeting both the operations of the firm and the entire wealth management value chain. By targeting both front- and back-office functions, and also infusing AI within the wealth management process as a whole, this model unlocks new levels of client-centricity, organizational efficiency and cost savings.

Where have all the humans gone?

In addition to managing increasing pressure to reduce costs and grappling with mounting regulatory concerns, the wealth management industry is also facing a chronic workforce shortage. With the average age of a financial advisor at 60 and only 11 percent under 35, it is of critical importance to use digital technology to automate administrative tasks, thus freeing advisors to spend more time on high-value, client-facing services.

Cognitive wealth management:
human x digital

The Cognitive Wealth Management Model

Cognitive Wealth Management is an evolution of the traditional investing business model. Here we examine the underpinnings of this approach and demonstrate how it offers organizations an opportunity to increase growth, drive efficiency and reduce risk.

Data

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 client—one that calculates individual risk in a much more accurate way and takes into account everything from projected healthcare costs to home upsizing.  

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 than human advisors would struggle 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 relatively unfamiliar areas, such as spending patterns, hobbies and lifestyle. As digital engagement continues to grow—especially among younger consumers—it is important for wealth management firms to also incorporate these data sources into their assessments.

By increasing the breadth and depth of data risk profiles will become more accurate and adaptive. This individualized approach will also help generate stronger client outcomes, thus making wealth management harder to commoditize and also overcome skepticism about advisor fees.

Risk Calculation

Despite being based on client data, portfolio risk calculation is anything but a straightforward process. In many cases, firms rely on self-reporting tools to gather the data to calculate risk. These self-assessments are 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.

Return

In a traditional model, wealth management teams are siloed, which means that the investor might get advice on taxes, estate planning and investments from disparate sources within the same organization. What’s more, communication between these business units is often infrequent and ineffectual. These groups are not only unaware that they’re engaging the same individual, but also 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. And while addressing the silos is going to take time, this model can provide a bridge for the short-term. With existing hybrid models, roboadvisors 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.

Compliance

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 2020.¹ Managing compliance with regulatory rules costs the six largest US banks $70B with over 10 percent of their workforce dedicated to this area.²

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.³ 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.

In evaluating their wealth management strategy, financial institutions must consider the landscape: regulatory complexity continues to increase, the industry is experiencing a chronic shortage of financial advisors and those that remain are under constant pressure to provide high-touch service to clients. The organizations that succeed will be the ones that create a robust, 360-degree view of the customer and use the latest in technology to provide an individualized investment plan. A cognitive approach—one that unites digital and human for exponential cost reduction and growth across the whole value chain—is the next generation model that institutions should consider to ensure growth and long-term viability.

Sources

1.  JWG Letter to FCA, UK, 2016.
2.  Federal Financial Analytics. The Regulatory Price-Tag: Cost Implications of Post-Crisis Regulatory Reform.
3.  BCG. Global Risk 2017: Staying the Course in Banking.