[To understand how modern financial data technology and advanced research are responding to the evolving challenges of portfolio management — and what they mean for the relationship between adviser and client — we spoke with Joseph Wickremasinghe, Executive Director of MSCI Research.

Our conversation on InvestTech solution development explores how research and technology come together to support wealth managers, with a particular focus on strengthening the client relationship and the allocation conversations at the heart of it.

A few of the challenges explored included helping investment managers effectively manage portfolios across public and private allocations and personalization at scale. The data points referenced throughout our discussion come from their 2026 MSCI Wealth Trends Report and MSCI Research.]

 

How exactly does your research and technology work together to design portfolio management solutions for wealth managers? Do you have a particular innovation process?

It starts with a real problem, not a model looking for a use. Our process is to observe where advisers are stuck, formalize it as a research question, then translate the answer into something that lives inside the workflow. The discipline throughout is that research and engineering work together from the start; a brilliant model an adviser cannot act on in the moment is not a solution.

Take a simple, well-established fact — that more than 90% of a portfolio’s return variability is explained by asset allocation. That tells us the highest-leverage place to put better data and models is the allocation decision itself and the conversations around it. So, we build analytics that let an adviser look across a client’s entire set of accounts at once, rather than one at a time.

Our broader conviction is that the future of investment technology is an intelligence layer — high-quality data and models that empower the workflow, delivered through platforms or, increasingly, AI agents. We do not produce reports; we produce trusted, reusable components that the adviser’s tools, and eventually their agents, draw on consistently.

How do you determine which investment areas or topics to focus on?

We follow the pressure points in the industry and the evidence in the data. Right now, three signals are loud.

 First, geopolitical risk is reshaping allocation — 86% of wealth managers report clients are concerned about global uncertainty and tariffs, and they are reallocating accordingly, with 61% planning to increase developed non-US exposure.

Second, private markets are moving to the core: 71% expect to raise private and alternative allocations.

Third, personalization has become the baseline, with 98% of new HNW portfolios carrying some customization.

We prioritize the areas where advisers face the most complexity and where better data and models would most directly improve the client conversation. The test is always whether research can change a decision an adviser makes for a client — not whether it is intellectually interesting. That keeps us focused on the public-private convergence, scalable personalization, and the risk and transparency tools that let advisers explain their choices with confidence.

Where do you see modern investment technology changing advisers' day-to-day conversations with clients most?

In three places. First, the asset allocation conversation. With a complete, household-level view and a common risk framework, an adviser can move past account-by-account performance — “your 401(k) returned 8.2%” — to the questions clients actually care about: “Am I on track to retire?” and “What happens if markets fall 30%?” The intelligence layer turns those into evidence-based answers rather than reassurance.

Second, the rebalancing conversation. Coordinating across accounts lets an adviser rebalance in a tax-aware way and explain why a trade is happening — surfacing concentration risk that’s invisible when accounts are viewed separately.

Third, the alpha-seeking conversation. For clients who want to pursue excess return through factor tilts, thematic exposures, or private markets, the same models let an adviser show where that alpha is coming from and what risk it adds, instead of selling a product.

The common thread is that better data and models shift the adviser from a technician reporting the past to a partner shaping future outcomes — and that’s the relationship clients are least likely to leave.

How does a shared, common analytical language change those conversations, and what does the technology actually contribute?

Trust is built on consistency. When the risk numbers an adviser presents reconcile with the numbers the investment team used, and both trace back to the same intelligence layer, the client gets one coherent story rather than a marketing version and an analytical version.

The technology is what makes that practical — surfacing concentration risk hidden across accounts, coordinating tax-aware rebalancing, and answering “what if markets drop 30%?” with real numbers. We use the MSCI Multi-Asset Class factor model to run those scenarios, so a shock to one part of the portfolio flows through to the rest based on actual factor exposures and correlations. That lets an adviser show, not just assert, what role each position plays and why a rebalance makes sense.

It is also the foundation for agent-assisted advice: an agent can only support a conversation if it reasons over the same trusted data the adviser trusts. The relationship stays human — but it is backed by a common analytical language that makes the adviser more credible, more responsive, and freer to spend time on judgment rather than reconciliation.

How do you help wealth managers address growing personalization demands around portfolio construction and alignment?

Personalization at scale is fundamentally a measurement problem: how do you keep hundreds of customized accounts aligned to a model when each client has different tilts, exclusions, constraints, and even international preferences?

Part of the answer is the MSCI Similarity Score, which takes a factor-based view of risk and return rather than comparing exact holdings, giving a single score for how closely any portfolio tracks a target — so an adviser applying a client’s thematic preferences (53% of advisers cite thematic exposure as a top driver) can immediately see whether they have drifted.

The other part is separating the target from its implementation. The model portfolio defines the risk budget and asset-class weights; the client’s values or style then forms an implementation layer on top. The same risk budget can be expressed through cap-weighted indexes, ESG or climate-aligned indexes, factor tilts, or thematic exposures — different implementations, identical allocation.

That is how you deliver direct indexing and SMAs with international exposure at scale, including for values-based investors, without each account becoming a manual, bespoke exercise that erodes the firm’s capacity.

How do you help advisers manage portfolios across public and private allocations and communicate the value of alternatives to clients?

The core challenge is that public and private assets have historically lived in separate analytical worlds, so advisers could not see a portfolio’s true total risk. Our work brings them onto a common factor framework, so a private credit, private equity, or real-estate allocation can be assessed alongside public holdings rather than treated as a black box.

That changes the allocation conversation in two ways. It lets the adviser show genuine diversification — the lower correlation that makes private credit attractive — rather than asserting it. And it quantifies the trade-off.

In our analysis, introducing a 10% private allocation improved the risk-return profile across private equity, credit, and real estate, with private credit producing the largest uplift. In a multi-generational case, adding a 15% private allocation improved expected return by roughly half a percentage point at comparable risk.

That is consistent with the broader MSCI Research estimate that a 15% private allocation may add about 40 basis points annually while maintaining similar market risk. With 83% of wealth managers now calling a robust private-asset suite essential, that evidence-based case is what advisers need.

Can you give a concrete example of how this plays out for a real client?

A common one is the concentrated portfolio. Picture a 45-year-old technology executive with $8M in assets, over 70% of it in company stock, who wants to retire in ten years. The traditional account-by-account view misses the real problem: their entire net worth rides on one sector.

Using the factor model, we can quantify it — in a repeat of the 2022 tech correction, where several large tech names fell more than 50%, their portfolio would draw down nearly 30%, enough to force a delayed retirement. That reframes the allocation conversation: this client does not need aggressive growth, they need to protect a number they have nearly reached, because the downside of missing retirement far outweighs the upside of excess returns.

So, you reposition for much better downside protection while keeping enough upside to meet the goal. We apply the same factor logic to thornier instruments too — translating opaque structured products into their true equity and bond exposures via delta and duration, so they do not sit in an “other” bucket distorting the portfolio’s real risk. In every case, the data turns an abstract worry into a concrete, defensible conversation.

What do you see as the next key advancements in InvestTech for wealth managers?

The throughline is the maturing of the investment intelligence layer and how it gets delivered. In the near term, the biggest gains come from fixing the data foundation — 44% of wealth managers feel the segment lags on AI, and that is a data problem more than an appetite problem, even as 95% plan to increase AI investment.

Once the data is clean and connected, agents become genuinely useful: an agent that can monitor household-level drift, surface a concentration risk, run a scenario, or draft a goals-based allocation proposal grounded in trusted models gives advisers real capacity back.

I would also expect a more holistic, household-level view of allocation to become standard, continued progress on the public-private convergence, and better tools for translating complex instruments — private funds, structured products — into a common factor language. But the consistent theme is that technology should strengthen the advisory relationship, not replace it.

The future I see is advisers spending less time wrangling systems and more time on judgment and relationships, supported by an intelligence layer and AI agents that handle the analytical heavy lifting beneath them.

 

The Institute for Innovation Development is an educational and business development catalyst for growth-oriented financial advisors and financial services firms determined to lead their businesses in an operating environment of accelerating business and cultural change. We operate as a business innovation platform and educational resource with FinTech and Financial Services firm members to openly share their unique perspectives and activities.

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