Next-Gen Operating Models for Bankers and Advisors

By Lily Li, Senior Vice President of Global Wealth, Citi

Since I wrote a paper entitled “Contextual behaviour-driven strategy: Digital banking beyond internet and mobile applications in 2020-2021,” we have witnessed a fundamental shift in people’s behaviours. If you have been amazed at the speed of video-conferencing adoption, you will soon be surprised by interactions in which the conversation format is not limited to the two-dimensional. In an average person’s daily life, fundamental behavioural changes lead to changes in investing, lending, making payments and other financial decisions.

In fact, clients have adapted over the past few years to new forms of interactive relationships, digital engagements and AI (artificial intelligence) automation. The client-facing market has been developed. On the flip side, smart work tools and operating models for bankers and advisors are still lagging. Similarly, the need for transformation in the wealth and banking sales fields should not come as a surprise. Digital innovation and AI advancement will not only influence the direct client approach but also benefit financial teams on the ground to engage and serve clients better.

The next wave that deserves our attention is the surfacing “next-gen” operating model that empowers wealth advisory and banking to conduct business at scale in a forward-looking, behaviour-driven way. Zooming in on bankers and advisors, we see unspoken needs and desires for help, as they are overwhelmed managing their client books daily. Each client is unique and prefers different treatment; this is sometimes not explicitly stated or reflected in demographic or financial data. Prioritising clients and their accounts and activities demands smart algorithms. A simple case would involve a client named Sarah, who has many bonds and options holdings in margin accounts. With sudden macro-policy changes and market fluctuations, an AI engine predictably detects margin calls and upcoming bond maturities and subsequently pushes them front and centre to Sarah’s advisor. Her advisor makes this a priority and advises Sarah immediately. Sarah is happy with her advisor’s proactive action on her transactions and preparation for moving her money, which leave her worry-free.

Business-case applications 

1. Hybrid advisorythat smartly addresses priorities between human touches and systematic workflows. Advisors could be prompted with recommended account activity to be handled through secure, remote collaboration platforms.
2. Personalisationbased on client-behaviour and sentiment detections. Combining demographics, full balance sheets, transaction and interaction data, client behavioural tendencies and sentiments would upgrade the hyper-personalisation of rich relationships.
3. Lead generationsthat source and promote prospects in context. Graphical networks around prospects and their mapped behavioural journeys create suggested introduction points and opportunities to convert new clients.
4. Financial planningbased on analyses of clients’ balance sheets, internally and externally, using a “full family” approach. Holistic 360-degree aggregated views of liquid and illiquid assets, short-term and long-term liabilities, investment-value performance and spending trends paint a thorough client picture for the financial planner to use to develop a comprehensive roadmap to achieve a client’s goals.
5. Investment budgetingfocused on predictions of portfolio holdings, deposit balances and value generation over time.
6. Client and account prioritisationin managed books based on trends across bankers and advisors. This could identify which types of signals work better than others for some advisors and what concerns regarding client accounts should be prioritised.
7. Proactive outreachthat anticipates clients’ inquiries by engaging them in advance in meaningful and relevant conversations that vary according to their personalities.
8. KYC (know your customer) and fraud auto-detect and clearance processes that prompt appropriate next steps for bankers’ and financial advisors’ engagements with customers. Cross-border business and cash payments could be accelerated by detecting AI-automated workflows.

Furthermore, the scale of the wealth-management business is booming—a vital focus for any growth strategy. Cerulli Associates expects $84.4 trillion in wealth to be transferred from households to their heirs and charities between 2021 and 2045.¹ There is tremendous room for bank sales and field staff to engage in wealth management. How heirs in the upcoming two decades prefer to handle their inherited wealth and interact with bankers and advisors will diverge significantly. Their future engagements will reflect their preferences. No matter what processes or devices clients rely on to manage their finances, the operational keys are the decision points throughout their journeys.

Breaking down the operating dimensions 

One important dimension is to bring forward new tools and emerging technologies that embed effective AI-powered capabilities to meet client demands, including automation and personalisation that recommend insights and, ultimately, next-gen actions. These tools and capabilities should follow the guiding principles ofthe client first; flexible and common; secure and scalable; innovative and forward-looking.

Besides fintech (financial technology) and robo-advisors, large banks apply similar frameworks to various business cases to empower bankers and advisors. AdvisorHub reported that over the last six years, Morgan Stanley expanded its ML (machine learning) tools and now has a 90-percent adoption rate of recommended actions across 160,000 brokers.² Better yet, the bank recently rolled out its “bird’s eye view” that enables the managers of advisors and brokers to obtain insights across their books.²

Aside from automation, the next best action is using sophisticated business rules, analytics models and AI engines to predict client needs and, in turn, promote more relevant insights and actions, leading to improved “wallet share” and client retention. With properly implemented machine learning, we can predict the actionable insights and recommendations that best respond to a client’s needs. These are relevant to bankers and advisors, so they can holistically conduct more complicated collaborations and workflows in relationship management, planning and servicing for every client daily. Recently, the breakthrough of OpenAI’s ChatGPT has proven AI’s ability to swiftly obtain contextual answers for searches, information summaries and problem solving. This technology could be integrated and applied to new behavioural models for bankers and advisors, enabling them to quickly gain knowledge from preferred markets, products and 360-degree analyses of clients’ structured and unstructured data. Ultimately, it could accomplish tasks with maximum productivity, allowing bankers and advisors to enrich their work by managing human-touch relationships.

The other dimension, which is equally important, is to educate bankers and advisors and send them clear messages that AI and other technologies can complement their duties, help them scale their businesses and assist them in succeeding in their careers rather than replace or substitute their expertise. Technology empowers behavioural models to change in order to become more productive. This operating dimension involves tailored training, business-case education, benefit incentives and interests alignment.

Key to achieving success is bankers and advisors being fully educated and understanding the changes; creating segmented approaches to target and personalise impactful transitions using data-driven approaches for each segment coverage based on their business books (serving specific products or client segments); and considering behavioural attributes, including personal characteristics, such as self-driven, fast follower, learning from others and guided. Operating models are most successful when they embed changes into bankers’ and advisors’ day-to-day work and, critically, their career stages and personal development. For executives, real-time monitoring and measurement would accelerate the evolution of this healthy ecosystem from three perspectives: readiness(baseline versus engagement data, training and preparation), adoption (usage performance, time to complete, productivity and feedback) and business value (client prospecting, revenue and sales, operations cost reduction, banker and advisor retention).

What is the challenge, and how do we break it down? 

Academics believe that current AI technology cannot properly parse judgements. What is the definition of judgement? According to the report “Exploring the Impact of Artificial Intelligence: Prediction versus Judgment” (Ajay Agrawal, Joshua S. Gans and Avi Goldfarb [2018])³: “Judgment is the ability to recognize hidden attributes when they arise” and “Judgment is useful when it changes a decision from that which would be determined by information about the uncertain action.” Most business attempts stop at the step of making judgements. But further exploration may result in additional outcomes that can be achieved with effort. Technology advancement and business-model innovation go hand in hand. Having no breakthrough in AI techniques regarding judgement does not mean that we cannot do more in the future.

We can strive to understand judgement by breaking it down into key contributing variables, then apply predictions based on the trends of these decision variables, overlaying the AI model with additional dimensions that support bankers and advisors in arriving at the most relevant and appropriate judgements. One variable could be the client-demographic measure, in which the definition of a variable will vary and be thoroughly analysed to incorporate demographic information. One proposal to rethink client segmentation is worthy of attention. Observations show women are making more investment decisions for their households. “Women now control some $10 trillion in U.S. financial assets; by the end of the decade, that figure will rise to $30 trillion.”⁴ Behavioural finance also suggests that five client segments—the Protector, the Competitor, the Collector, the Verifier and the Simplifier—that invest and allocate assets with advisors vastly differ. The Protector is highly sceptical of experts, while the Simplifier confines his business to one advisor.⁵

The Lego partition concept breaks down complex judgement flows into “pieces” (judgement variables) and “colours” them with distinct personas (behavioural tendencies). These pieces can be modelled through AI-engine simulation and predict the trends and momentum of the judgement variables, allowing bankers and advisors to make overarching decisions, prioritisations and recommendations to their clients that are suitable for future circumstances. Once this data covering key judgement variables moves front and centre in a forward-looking manner, bankers and advisors can shape their opinions by combining the simpler recommended actions relevant to an individual client.

Take the simple scenario of a client named David, who has a birthday coming up and prefers structured note products. In the past, David invested $100,000. Today, David has a deposit balance of roughly $500,000. The key judgement variables are David’s interest in making new investments and his current deposit balance. If the AI-empowered “next best actions” project a strong tendency to invest in structured notes, and with the bonus season coming, David is predicted to expect a $1 million check. Meanwhile, a basic AI model recommends a birthday wish. With these recommended actions, David’s advisor could arrive at a judgement of reaching out with a personalised message on his birthday as well as recommending he allocate $200,000 plus in selected structured-note investments.

Once the “shape of Lego” is constructed, it acts as a powerful engine and continues evolving the “shape” with supervised machine learning, unsupervised machine learning and reinforcement learning. The AI algorithm learns by interacting with its markets and receiving positive or negative feedback. Its next best actions stay relevant and become more accurate over time. In the context of wealth advisory and banking, it empowers bankers and advisors to make proper judgements in scalable and prioritised methods. Its effectiveness can be measured in many different ways.

Key success factors include:

1. Customer engagement, 
2. Cash flow and assets under management (AUM),
3. Business volume and level of engagement,
4. New advisory clients,
5. Account attrition.

Conclusion

In summary, bankers and advisors, along with their clients, want next-gen operating models with new tools to achieve success and scale in their portfolios. Financial institutions can tackle this challenge on two dimensions: the first is to provide AI-powered platforms that are relevant, efficient and accurate; the second is to empower bankers and advisors with scalability, business-case understanding and interests alignment. To excel at the entry phase of client behavioural changes, the one edge AI technology currently cannot provide is judgement. We can strive to break down judgements into key contributing variables and apply predictions to overlay additional dimensions to the recommendation models that support bankers and advisors when arriving at the most relevant and appropriate judgements. Examples of the scenarios of clients such as David and Sarah and their predicted next best actions pave the way to the future of wealth advisory and financial services. Having executed all the components that have been laid out in a timely manner, bankers and advisors would ultimately benefit from understanding client needs in context, creating a cohesive experience that “sticks”, building trust and growing wallet share and client accounts for the long term. 

References

¹ The Cerulli Report: “U.S. High-Net-Worth and Ultra-High-Net-Worth Markets 2021,” Sources: Cerulli Associates, Federal Reserve, U.S. Census Bureau, Internal Revenue Service, Bureau of Labor Statistics and the Social Security Administration, Analyst Note: Figures in 2020 dollars.

² AdvisorHub: “Morgan Stanley Rolls Out ‘Bird’s Eye View’ Tool for Managers to Track Brokers’ Client Engagement,” Miriam Rozen, June 17, 2022.

³ Tech Policy Institute: “Exploring the Impact of Artificial Intelligence: Prediction versus Judgment,” Ajay Agrawal, Joshua S. Gans and Avi Goldfarb, February 2018, TPI Conference, Washington DC.

⁴ CNBC: “Women are gaining power when it comes to money – here’s why that’s a big deal,” Ted Jenkin, May 3, 2022.

⁵ Investopedia: “How Understanding Client Behavior Helps Financial Advisors Build Trust, Wallet Share,” Kara Greenberg, July 27, 2022.