Satish Avhad, Consulting Partner, Global Practice Head, Wealth Management, Wipro Consulting.
In the world of wealth management, even as assets under management have grown, rising costs and fee compression are squeezing profit margins in an increasingly competitive market. At the same time, fintech upstarts and a new generation of tech-savvy investors are raising the bar for service, expecting more personalized, digital-first experiences than traditional firms have provided.
One notable opportunity is the “mass affluent” segment (investors with roughly $100,000 to $1 million in investable assets). As forward-looking wealth management firms reimagine their service models, the path forward centers on AI and hyper-personalization to boost advisor productivity while delivering bespoke service at scale. These technologies promise to harness automation and data, allowing advisors to serve more clients in a highly individualized way without sacrificing the human touch that builds trust.
Serving The Mass Affluent Segment With Hyper-Personalization
Serving mass affluent investors at scale requires efficiency without sacrificing personalization. These clients may desire the white-glove treatment of ultra-high-net-worth individuals, but their asset levels (and the typical, approximately 1% advisory fee) can’t support an intensive one-to-one service model. The result is a classic scalability challenge: how to provide tailored advice and a holistic experience profitably to millions of emerging affluent clients.
Many firms initially developed digital tools like robo-advisors and self-service apps, along with expanded product offerings such as low-cost funds, insurance and even crypto, to entice this demographic. However, a more decisive strategy has proven to be hyper-personalization: leveraging data and AI to tailor services and advice to each client’s unique profile.
Firms are increasingly using smart sub-segmentation to group clients into ever-more granular clusters—often 20 or more micro-segments—based on age, family situation, risk appetite, life goals and even personal interests and values. Advisors can then deliver products and guidance calibrated to a client’s life journey. Lower-asset sub-segments that skew younger and digitally savvy can be served largely through automated, app-based interactions, whereas those nearer the high-net-worth threshold (around $1 million) can get periodic high-touch consultations on top of digital engagement.
Crucially, firms are using data-driven customer lifetime value (CLV) models to prioritize which mass affluent clients merit more frequent human outreach, ensuring advisor time is used where it most impacts long-term value.
With segmentation as the foundation, wealth managers can layer on innovative engagement tactics. Gamification is one such tactic gaining traction, turning financial goal achievement into a game-like journey with rewards or progress tracking to motivate clients.
Alongside gamified features, firms are able to deploy timely nudges and actionable insights driven by analytics, gently steering clients toward smart behaviors (like rebalancing a portfolio or upping retirement contributions) at just the right moments. Importantly, these recommendations should feel empowering rather than prescriptive. In this sense, clients should be offered interactive tools (say, a “what-if” calculator for retirement planning) so they can explore decisions with a sense of control.
To enable personalization at scale, firms can deploy tracking engines that continuously learn and refine their strategies. Additionally, wealth managers can leverage AI/machine learning (ML) to continuously adjust client segments and offers based on what works. Dedicated analytics teams and AI specialists develop the insights, which are then delivered to advisors and business leads via intuitive dashboards.
At Wipro, for example, our platform draws on both first- and third-party data to inform these insights. The objective is to create an agile, data-driven feedback loop: as clients engage—or disengage—with personalized services, the system adapts, updating its approach to improve engagement and outcomes over time.
The Hybrid Model: High-Tech Plus Human Touch
Hyper-personalization using AI augmentation is leading toward a hybrid advisory model where human advisors and digital platforms collaborate to deliver a better client experience. Rather than replace advisors, technology can amplify their reach, allowing one advisor to effectively serve more clients than was previously possible.
I’ve found that an ideal formula for this approach appears to be “smart automation and a targeted human touch.” Routine interactions (basic portfolio tweaks, informational updates or product education) can be handled through slick digital interfaces and AI guidance, while human advisors step in for the high-impact moments (major life events, complex planning or emotional reassurance).
The goal here is productive hybridity—not automation at all costs, but automation when and where it enhances the client’s journey. Firms that master this balance can even outflank digital-only fintech rivals by offering something fintechs lack: the comforting expertise of a real advisor, precisely when it matters most.
When navigated strategically, this hybrid approach can benefit both clients and advisors. Capturing mass affluents early in their financial lives means firms can assist them on their journey to becoming high-net-worth clients of tomorrow. With AI and analytics handling menial work, advisors can focus on complex problem-solving and strengthening relationships, which are the rewarding parts of the job. In this way, hyper-personalized, AI-enabled models elevate the advisor’s role to an orchestrator of a high-tech toolkit, extending their capacity and insights.
A New Era Of Advisor-Client Synergy
What’s unfolding in wealth management is not just a tech upgrade, but a fundamental realignment of how advisors and clients interact. AI and hyper-personalization are enabling advisors to be more proactive, informed and client-centric than ever before, while empowering clients with more engaging and tailored experiences.
As more large financial institutions begin using hyper-personalization at scale, early adopters of AI are expected to be at the forefront of industry growth in the coming years. My recommendation is to use technology to be more human, not less.
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