Today’s wealth management clients expect highly personalized experiences tailored to their unique needs, goals, and preferences. Cookie-cutter, one-size-fits-all approaches no longer suffice in an age of bespoke everything from entertainment to online shopping.
Clients want their wealth managers and RIAs to understand them on a deep, personal level – their risk tolerances, life aspirations, communication styles, and more. They seek proactive insights and recommendations that speak to their individual situations rather than generic guidance.
Meeting this standard for hyperpersonalization may seem daunting amid mounting client demands and data. But artificial intelligence (AI) and machine learning (ML) offer wealth managers powerful tools to optimize the client experience across prospecting, planning, investing, and ongoing communication.
Forward-thinking firms are leveraging AI/ML to deliver mass personalization at scale – analyzing vast client data to derive predictive insights that enable bespoke engagement. Those ignoring these innovations risk falling behind as the tech arms race accelerates.
This article will explore key AI strategies wealth managers can implement to drive hyper-personalization across the client lifecycle.
Learn how AI-powered segmentation, planning, behavioral finance, rebalancing, and omni-channel servicing help unlock client-centricity – one individual at a time.
Client Micro-Segmentation
Client segmentation—grouping clients by shared attributes—has long helped wealth managers tailor service models.
However, traditional segmentation, relying on a few basic dimensions like age and account size, paints clients with overly broad strokes. More precise personalization requires micro-segmentation.
AI empowers wealth managers to micro-segment clients across far more granular variables by efficiently analyzing large structured and unstructured data sets. Firms can group clients into much narrower personas factoring in attributes like:
- Financial behaviors and spending patterns
- Psychographic profiles and risk tolerances
- Life stage needs and milestones
- Communication preferences and frequencies
- Product affinities and cross-sell opportunities
- Digital engagement tendencies
This micro-segmentation yields highly specific client clusters that enable bespoke engagement. Advisors can target timely outreach and recommendations while still achieving efficiency through well-defined playbooks
For example, an AI engine might identify a micro-segment of pre-retirees who are avid golfers, prefer email, have moderate risk tolerance, and show a propensity for ESG investing based on their values.
The wealth manager could push relevant thought leadership content on optimizing retirement income for their lifestyle, suggest an ESG portfolio rebalance, and invite them to an exclusive golf outing.
Goals-Based Financial Planning
While segmentation guides client engagement, achieving true personalization requires understanding each investor’s unique goals.
Goals-based financial planning has gained traction as the antidote to generic investment-centric advice. AI supercharges this approach.
AI-driven financial planning tools enable wealth managers to efficiently gather client goals data and dynamically optimize recommendations. Innovations include:
- Gamified assessments that capture goals and visualize trade-offs
- Scenario simulators that stress test goal feasibility across market regimes
- Recommendation engines that match products/allocations to specific goals
- Natural language processing that translates unstructured meeting notes into trackable goals
- Progress-to-goal tracking with alerts when clients veer off-track
This real-time optimization gives clients the sense that their plan is bespoke to their one-of-a-kind combination of objectives.
It provides the guardrails and guidance they need to stay disciplined while accommodating their personal visions of success.
For instance, a client might express their retirement dream of traveling the world. An AI-powered financial planning tool could quickly model how much they need to boost savings to afford this lifestyle with high probability, visually illustrate the trade-offs of retiring later or downsizing their home to free up cash flow, and match an optimal product mix to their risk tolerance and time horizon.
Real-time tracking could proactively flag if market volatility knocks them off-course.
Behavioral Finance Modification
Understanding client behaviors is just as critical to personalization as grasping their goals. Behavioral finance has revealed the many behavioral biases that color individuals’ financial decision-making, often to their detriment. However, consistently applying those insights across a book of clients poses challenges without AI assistance.
Machine learning helps wealth managers detect behavioral red flags and nudge clients toward optimal choices. Applications include:
- Monitoring trading/spending patterns to spot biases like overconfidence or herd mentality
- Tailoring risk tolerance assessments to counteract overstatement biases
- A/B testing framing effects in client communications to drive positive actions
- Analyzing meeting sentiment to gauge emotional states preempts panicked decisions
- Gamifying behavioral nudges like automated savings top-ups or rebalancing alerts
This behavioral augmentation gives clients a truly personalized experience attuned to their natural decision-making tendencies. It helps them feel understood while empowering them to be their best financial selves.
Say a client has a history of panic selling during volatility. Analyzing trading data, an AI algorithm could detect this pattern and trigger an automated email when markets tumble with historical context on the benefits of staying invested. Wealth managers could A/B test fear vs. greed-based frames to optimize messaging.
Tax-Smart Rebalancing
Rebalancing client portfolios to their target allocations is a perennial challenge for wealth managers. But tax ramifications add a personalization imperative, as each client’s tax situation is unique. Ignoring taxes risks suboptimal after-tax returns.
AI can power tax-smart rebalancing that optimally harvests losses and rebalances at the individual tax lot level based on each client’s situation. Innovations include:
- Automating wash sale avoidance by tracking replacement transactions across accounts
- Optimizing tax lot selection for rebalancing based on cost basis and holding period
- Simulating potential rebalancing scenarios to project tax implications before executing
- Scanning for capital loss carry forward to apply to current gains
- Spotting opportunities to donate highly appreciated shares
Tax-smart rebalancing at the individual level gives clients the sense that their wealth manager is proactively seeking their best interests. It demonstrates focus on their personal circumstances and builds trust.
For example, when markets shift and trigger rebalancing, an AI algorithm could automatically scan a client’s accounts identify optimal tax lots to sell based on their unique tax situation and losses to harvest and execute the trades. The wealth manager could deliver a personalized report illustrating the projected tax savings.
Omni-Channel Servicing
Finally, AI empowers wealth managers to personalize client engagement across an omni-channel service menu. With AI analyzing client behaviors, firms can match delivery across in-person, phone, email, text, video, and mobile app touchpoints to individual preferences. AI-driven omni-channel servicing includes:
- Predicting channel propensities and likely next-best actions based on client behaviors
- Optimizing engagement frequency and cadence at the individual level
- Tailoring content, offers, and recommendations by channel
- Routing inbound requests to the rep/chatbot best suited to the client
- Gathering cross-channel feedback to continuously refine personalization
Omni-channel personalization makes clients feel valued. It demonstrates that their wealth manager is attuned to their unique communication styles and will engage however they prefer – dynamically and proactively.
Imagine an AI engine detects a client hasn’t logged into the mobile app for a month but has high email engagement.
It could trigger a personalized email with links to the client’s latest portfolio insights and a reminder of in-app planning features that might interest them based on their goals and behaviors. If the client clicks through, the engine could prioritize an in-app message with a bespoke offer.
The Bottom Line
AI and machine learning provide the key to delivering hyper-personalized wealth management at scale. Client expectations for bespoke engagement will only intensify as tech giants innovate. Firms that fail to adapt risk irrelevance.
Implementing AI across segmentation, planning, behavioral coaching, rebalancing, and omni-channel servicing empowers wealth managers to efficiently tailor experiences to each client’s unique needs. It augments the human touch while automating previously impossible personalization.
The path to AI enablement starts with unifying client data for analysis. Firms should audit their data landscape and consolidate disparate sources. With clean, centralized data, they can begin layering on AI solutions across the client lifecycle in partnership with leading vendors.
But AI alone is no panacea without the right talent and culture. Wealth managers must commit to upskilling advisors on AI tools and fostering an insights-driven ethos. Only by marrying artificial and human intelligence can firms achieve hyperpersonalization in service of clients.
The future of wealth management is hyper-personal, and AI provides the engine. Firms that embrace this imperative stand to deepen client relationships and drive assets under management. Laggards risk displacement. Which side will your firm be on?
