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Gaining HNWI market share: Embracing AI-powered behavioral finance

How banks can mitigate emotional investing by infusing intelligence to hyper-personalise portfolios and experiences for attracting and retaining key clients

In brief

  • With 65% of HNWIs admitting that biases affect their investment decisions, and 79% looking to RMs to help mitigate bias, wealth managers need modernise their profiling tools.
  • AI-powered behavioral finance, and related AI technologies like generative AI, provides the deeper insights RMs need for building hyper-personalised financial plans, portfolios, and client experiences.
  • To get AI-powered behavioral finance right, banks should complete six critical deployment steps.

Regardless of a person’s net worth, it’s widely known that emotions and cognitive biases cloud investor judgement. In fact, 65% of high-net-worth individuals (HNWIs) participating in Capgemini’s World Wealth Report 2024  admitted that biases affect their investment decisions.

To combat this, over three quarters of respondents (79%) believe relationship manager (RM) guidance can help mitigate bias.

However, banks have historically relied solely upon high-level demographic profiling and primary data sources for determining biases, resulting in an incomplete picture that often contributes to decidedly unsatisfactory client experiences.

Consequently, the same percentage of HNWIs who admitted to bias, 65%, also express concern over the lack of personalised advice tailored to their changing situations.

Fortunately, behavioral finance powered by artificial intelligence (AI) offers a better way. It goes beyond traditional assessments by providing deeper insight into risk attitudes, risk tolerance, stress response, market engagement, and decision-making style.

This article discusses the essentials of behavioral finance, and related technologies, along with the steps banks can take for swift, confident, and successful adoption.

ABCs of Behavioral AI

In a nutshell, AI-powered behavioral finance integrates psychographic insights, behavioral data, and artificial intelligence to build a comprehensive 360-degree client view that is continually updated to capture the impacts of life events and other situational changes.

Further, the most effective behavioral finance solutions collect data from both traditional repositories, like financial transactions, and alternate sources, such as social media posts and other online behavior, to ensure the HNWI client portrait is complete (Figure 1).

Although early forms of behavior finance utilised traditional data analysis methods, adding AI uncovers hidden patterns, sentiments, and biases that frequently eluded previous iterations of the discipline.

Figure 1: Types of data currently collected by WM firms

Source: Capgemini World Wealth Report 2024.

Top 3 WM domains where AI drives value

In general, 75% of wealth management executives are bullish on using AI, with 49% currently use AI in some areas and 73% intent to increase enterprise AI adoption within two years. This makes adopting AI-powered behavioral finance and related AI tools, such as generative AI, a competitive imperative.

It’s a similar story for RMs as the majority of those surveyed (65%) for the World Wealth Report 2024 indicated that client preferences, unique pain points, behavioral tendencies, and other psychological insights are critical to providing personalised advice.

For most banks, this means initial adoptions of AI-powered behavioral finance, and related forms of AI, will focus on infusing intelligence into three key wealth management domains. These include:

Turbocharging financial planning and portfolio creation. Using the comprehensive, continuously-updated client profiles developed with AI-powered behavioral finance, RMs can build highly customised financial plans. By adding AI-powered behavioral segmentation, which incorporates dynamic and attitudinal behaviors into financial planning, banks can further refine HNWI client profiles to create more precise financial plans and resilient portfolios.

In addition to constantly refreshing client profiles, banks can deploy AI to monitor other data feeds, such as global market information, news, and current events. This ensures RMs can rapidly adjustment financial plans and take action based on client preferences, resulting always-optimised asset allocation.

From an advisor productivity standpoint, AI can also boost RM efficiency and effectiveness by autonomously identifying patterns, pinpointing low-correlation assets, triggering alerts, and suggesting financial planning and portfolio adjustments that align with evolving investor objectives.

Enhancing client communication and engagement. A robust behavioral finance deployment includes real-time, AI-enabled client communications accomplished via generative AI. This is vital for helping advisors supply timely, hyper-personalised advice and tailored investment strategies, as well as managing stresses commonly triggered by sudden market volatility to help keep their HNWI clients and portfolios on track.

In addition, real-time alerts about market events or life milestones can signal advisors when to reach out to clients, with AI-driven analysis determining the most effective channels and messages for client interaction.

Further, by also integrating AI-powered sentiment analysis and predictive analytics, RMs can gain deeper insights into investor sentiment, anticipate market and client sentiment shifts, and uncover potential opportunities or risks, all of which enable proactive and targeted communication.

These advantages are already proving out in the marketplace. According to the World Wealth Report 2024, 59% of WM executives who already leverage behavioral finance affirm that the technology aids with advising clients during volatile market conditions and significant life moments.

Similarly, AI-powered client acquisition marketing can identify high-potential prospects, supporting business growth and client acquisition efforts. For example, Vanguard Institutional improved its conversion rate 16% by leveraging Persado AI and generated a click-through rate 15.76% higher than the control message.

Improve performance across WM operations. AI also adds value by automating various operational tasks (Figure 2), such as document management, transaction processing, and record keeping. AI can also enhance risk management and fraud detection with real-time data analysis that identifies suspicious patterns or anomalies to help safeguard banks and their HNWI clients.

Figure 2: Leveraging AI to fuel wealth management performance

Source: Capgemini World Wealth Report 2024.

Six steps for AI-powered behavioral finance success

Building scalable enterprise AI-powered behavioral finance solutions requires taking a structured approach. This involves integrating diverse data sources by leveraging various AI and generative AI capabilities, ingesting the integrated data through AI-based sentiment analysis and predictive analytics, and implementing the derived insights to drive real-time client profiling, portfolio optimisation, and hyper-personalised HNWI experiences (Figure 3).

This holistic approach not only enhances client experiences but also empowers advisors by automating mundane tasks, optimising time, and minimising errors. For example, firms like RBC Wealth Management U.S. are already leveraging Salesforce’s Personalised Financial Engagement solution to integrate disparate data systems, create unified customer profiles, and deliver automated and intelligent customer journeys using generative AI.

However, successfully executing a structured approach is a considerable task. To ensure your enterprise can integrate, ingest, and implement efficiently and effectively to gain the necessary business value, six critical steps are recommended:

  1. Make internal data accessible: For banks, the essential data question isn’t whether they have valuable data, but whether it can be located and accessed by AI applications in real time. To do so, siloed, hidden, and mislabeled data sets must be connected, cleansed, and standardised across business units and acquired entities.
  2. Incorporate external data: Although it’s common for retailers use third-party data for obtaining deep customer insights, banks have lagged. To fully realise the promise of behavioral finance and achieve the desired business outcomes, banks must identify the right external sources and integrating them with internal data repositories.
  3. Architect robust AI infrastructure: In addition to identifying and utilising the right data sources, data must be presented to AI applications rapidly, as latency significantly impedes AI’s capabilities to derive relevant insights. Banks must design and deploy the appropriate compute, storage, networking, and cloud infrastructure to provide the necessary AI foundation.
  4. Adopt finance-specific AI and generative AI solutions: Understanding client psychographics, creating hyper-personalised financial plans, and delivering high-touch customer experiences requires adopting robust purpose-built AI applications to enable scaling rapidly and gaining business value. An example is Capgemini’s Augmented Advisor Intelligence solution, which can be used for both informing RM decisions and generating client-facing communications.
  5. Prepare for exposing AI-derived insights to clients: Although utilising AI for behavioral finance and client communications is an internal function today, HNWIs will eventually be keen for self-service capabilities in addition to personal interactions with their RMs. To ensure banks can meet this expected demand quickly and seamlessly, it’s imperative to design and architect the technology and application foundations with the inevitable future in mind.
  6. Address regulatory concerns: As with any new technology, it’s imperative to implement AI solutions in a compliant manner while also minimising risks from any misdirection or losses caused by AI applications. In addition to designing, deploying, and monitoring AI applications appropriately, it’s suggested that banks also keep a human intermediary between AI applications and the customer, at least for now.

Figure 3: Holistic approach for implementing scalable AI

Source: Capgemini World Wealth Report 2024.

Capturing HNWI market share

In the rapidly evolving wealth management landscape, integrating behavioral finance principles with AI technologies is the key to mitigating emotional investing responses, delivering superior client experiences, and positioning WM firms to stand out in a competitive marketplace.

By harnessing the power of AI, banks can gain unprecedented insights into client behavior, preferences, and biases, enabling them to provide hyper-personalised advice, tailored investment strategies, and targeted communication. By embracing transformative AI-powered behavioral finance, and related AI applications, firms can attract savvy investors, unlock new levels of client intimacy, and ensure greater engagement, trust, and brand loyalty, all of which contribute to capturing and retaining significant HNWI market share.

Meet our experts

Nilesh Vaidya

Global Head of Banking and Capital Markets practice

Kavita Nar

Head of Wealth and Asset Management Consulting, North AmericaCapgemini