Artificial intelligence (AI) has the potential to transform entire industries and change how value is created. In fact, some experts put AI’s contribution to the global economy to reach $15.7 trillion by 2030.

While AI’s applications in industries such as healthcare, manufacturing, and law enforcement have been widely documented and acknowledged, the discussion around its use cases in wealth management have mostly remained limited to niche circles.

Yet, wealth management as an industry is undergoing a remarkable evolution and digitalization journey. As providers of financial advice and planning, wealth managers are continually being asked to do more with less, as demand for their services continues to increase. Meanwhile, 40 percent of advisors plan to retire in the next 10 years and the headcount in the financial advice industry is expected to decline by 5.8 percent between 2017 and 2022—a problem compounded by a shortage of new talent entering the field.

AI offers an opportunity for advisors to deepen client relationships, realize new efficiencies, identify and hire the best candidates, and, ultimately, grow and scale their business. Generally speaking, wealth managers look at AI through five main lenses:

1. 1. Client Management,
   2. Practice Management,
   3. Talent Management,
   4. Competitive Intelligence/Benchmarking, and
   5. Operations and Compliance.

And while the uses of AI across the wealth management business continue to grow, in this blog, I will explore a sample list of use cases with the potential to make an immediate impact on the industry.

Deepening Client Relationships

Increasingly, advisors are being asked to not just provide financial plans and holistic advice, but also act as life coaches. Planning discussions are going a lot deeper than ever before, and the role of the advisor is changing. This shift is occurring at a time when more and more prospect and client interactions are completely virtual.

Leveraging machine learning to analyze psychometrics can help advisors assess clients’ psychology around financial matters, and develop customized engagement plans to work with each client in the way that’s most effective and applicable to their style. Further, digital assessment techniques make this possible in cases where the advisor may never physically meet the client.

Prioritizing Tasks, Scaling Business, Managing Workload

Recent volatility in the markets further underscored the importance of a financial advisor and a steady hand at the wheel when things get rough. Nothing can replace the stabilizing effect of an advisor on an anxious client in times of uncertainty. Yet, it is neither feasible—nor is it productive—for advisors to treat all clients the same way or with the same sense of urgency. It is also quite possible some clients may not even be aware of the turbulent markets, and proactive outreach could in fact have a detrimental effect.

Artificial intelligence can help advisors identify clients that are most prone to making an impulsive decision in times of market volatility. Leveraging previous assessment tests and historical behavioral patterns, AI can segment clients based on their risk tolerance, volatility composure, and investing confidence, helping advisors effectively and efficiently prioritize client outreach.

In addition, using robotic process automation and optical character recognition, advisors can automate the mundane and repetitive processes, such as account opening, transfer of securities from one account to the other, freeing valuable staff time to focus on building and maintaining client relationships.

Finally, looking at historical output and work trends, AI can predict which team members are likely to be over-stretched, which are likely to have extra bandwidth, allowing lead advisors and managers to allocate work more optimally, helping reduce the time it takes to complete tasks.

Identifying and Hiring the Best Candidates

A large number of advisors that enter the profession don’t last very long, and those that are successful are few and far between. Identifying talent that offers the best potential for growth and success is a critical issue for future business growth.

Data analytics can then take candidate assessments (on a number of behavioral and professional elements) and match them to long-term performance and develop a data driven solution that identifies characteristics that have the strongest correlation with future success, helping alleviate much of the uncertainty and individual decision making in the hiring process.

Growing Business

One of the most critical challenges facing advisors is driving organic growth. For a decade, many advisory firms relied on market performance as a driver of AUM growth. The pandemic and the ensuing market volatility reminded everyone the perils of that reliance. Further, after a year of remote work and social distancing, advisors have recognized that the old ways of networking and business development—wine tastings, golf outings, etc.—are not coming back in full swing for some time. All this requires advisors to think of business growth in more rigorous and creative ways.

For example, through the use of machine learning, AI can make predictions about key life events that have occurred or are about to occur. By being alerted to life events, such as a child birth, change of jobs, purchase of a new house, or a relocation, etc., advisors can start more meaningful conversations with clients, deliver better and more targeted advice, and offer solutions that are suitable for the life phase clients are entering. This all helps build deeper engagement with clients and open up new opportunities for organic growth.

AI is also increasingly being integrated with social and digital prospecting tools, which is helping advisors better understand client interests and pain points and deliver more targeted content to clients via social channels.

These are only a few examples of how AI can apply to wealth management, but as this list grows and the computing power of AI becomes more powerful, it is clear that our industry can no longer ignore AI. Particularly, as competition from new entrants and fintech’s increase and the talent shortage becomes more acute, the wealth management industry has much to gain from deploying AI across business functions and processes to drive growth and scale at this critical juncture.

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