[“InvestTech” has been an emerging, informal sub-category within FinTech, used to describe technology applied to the investment management process — research, portfolio construction, trading, risk, and analytics. Unlike its better-defined siblings — RegTech, WealthTech, and InsurTech — the term still isn’t an industry standard.
That is beginning to change as the investment-technology arena draws more serious attention. Rising complexity — client personalization, the integration of public and private assets, the limits of holdings-based comparison, and the constant need for risk mitigation — is driving a wave of innovation.
To understand where this is heading for wealth managers, we spoke with Alex Kokolis, Managing Director, Head of the Wealth Management Segment at MSCI Wealth — a division of MSCI dedicated to wealth managers globally, with a suite of portfolio management solutions to scale personalization and create capacity for growth, leveraging over 50 years of expertise in indexes, risk, sustainability, climate and private capital.]
How did your previous professional experiences lead and motivate you to get involved in advanced research and investment technology?
I spent much of my career close to the client portfolio — in roles where the gap between what the data could tell you and what the technology actually delivered to a decision-maker was painfully wide. You could have brilliant research sitting in one system and a client portfolio sitting in another, with no common language between them.
That disconnect is what pulled me toward investment technology. I became convinced that the value wasn’t in any single model or dataset, but in connecting them — turning research into something an investment professional could act on in the moment, inside their own workflow.
At MSCI we have more than 50 years of work in indexes, risk, sustainability, climate and, increasingly, private capital. The motivation for me was taking that depth and making it usable: not a library of analytics that experts admire, but an intelligence layer that quietly powers the everyday decisions advisers and portfolio managers make for their clients.
What types of investment manager challenges did you determine needed to be addressed?
Three stand out. First, scaling personalization. Our 2026 Wealth Trends research found that 98% of new high-net-worth portfolios now include some form of customization, and 53% of advisers name thematic exposure as a top driver. Personalization has gone from premium feature to baseline expectation — but most firms can’t deliver it across hundreds of accounts without breaking their operating model.
Second, the public-private convergence. Some 71% of wealth managers expect to increase allocations to private and alternative assets, yet the data, due diligence, and risk tools for privates lag far behind public markets.
Third, fragmented data. AI and automation only work on clean, connected, decision-ready data, and most firms are still reconciling mismatched records across CRMs, reporting, and analytics systems. Until that foundation is solid, every downstream ambition — personalization, private markets, AI — stays shallow. Those three pressures kept surfacing, and they shaped where we focused.
How have investment technology solutions traditionally been developed and applied?
Historically, InvestTech was built as a set of standalone applications — a risk system here, a portfolio-construction tool there, a reporting package somewhere else. Each solved a real problem, but each was a silo with its own data model, its own assumptions and its own interface.
Firms ended up stitching them together with manual processes and spreadsheets, and the analytics rarely agreed with one another because they didn’t share a common foundation. The result was that sophisticated capabilities stayed in the hands of specialists rather than reaching the adviser at the point of decision. Technology was something you went to, rather than something embedded in how you already worked.
That model was serviceable when portfolios were simpler and client demands were more uniform. It breaks down the moment you try to personalize at scale, blend public and private assets, or layer AI on top — because none of those things respect the boundaries between yesterday’s separate tools.
How do you see it evolving to better support asset and wealth managers?
The shift is from closed, standalone applications toward open ecosystems — flexible environments where high-quality data, research, and models can be combined and delivered wherever the work actually happens.
Our view is that the future of InvestTech is an investment intelligence layer: a connected foundation of trusted data and models that empowers the investment workflow rather than sitting beside it. That layer can be delivered through a platform, or increasingly through agents that act on the adviser’s behalf — surfacing the right analytic, flagging a risk, drafting an allocation proposal.
The signal from the market is strong: 95% of wealth managers plan to increase AI investment over the next three years and 68% see it as vital to competitiveness. But the same research shows 44% feel the wealth segment lags the broader industry, largely because of data fragmentation. The winners will be those who treat data and models as an open, interoperable layer — not another silo.
What specific benefits does designing InvestTech into an ecosystem provide for investment managers?
The biggest benefit is consistency. When research, risk and portfolio construction draw on the same intelligence layer, the numbers an adviser shows a client reconcile with the numbers the investment team used to build the portfolio — there’s a single, common language across the firm. That consistency is what makes personalization scalable: you can tailor across hundreds of accounts without each one becoming a bespoke, manual exercise. An ecosystem also future-proofs the firm.
Rather than ripping out and replacing tools, you plug in new data, new models, or new asset classes — private credit, direct indexing, thematic exposures — as client demand evolves. And it’s where agents become genuinely useful: an agent is only as good as the data and models beneath it, so an open, high-quality intelligence layer is the precondition for automation that advisers can actually trust.
The end result is capacity — advisers spend less time reconciling systems and more time on the relationship and the advice itself.
Regarding the “open operating system for wealth,” what were the biggest technical and operational challenges firms faced when integrating disparate models and data sources into their existing tech stacks?
The hardest problems were rarely the flashy ones. Technically, the core challenge was reconciliation — mismatched historical data, inconsistent identifiers, and models built on different assumptions, so two systems would give you two different answers for the same portfolio.
AI makes this worse, not better, because automated recommendations inherit every gap in the underlying records. Operationally, firms had layered tools over years, each with its own workflow, and asking teams to change how they work is harder than any data migration.
The lesson we took is that an “open operating system” can’t just be an integration project; it has to be opinionated about data quality and a common analytical foundation, while staying genuinely interoperable with whatever a firm already runs. You meet advisers inside their existing stack and CRM rather than forcing a rip-and-replace. Get the intelligence layer and the identifiers right first, and the workflow benefits — personalization, private-market visibility, agent-assisted analysis — follow.
Could you elaborate on the gaps you observed in how firms handle due diligence and benchmarking for private assets?
Private markets have moved toward the core of the portfolio — 83% of wealth managers told us a robust suite of private-asset solutions is becoming essential — but the supporting infrastructure hasn’t kept pace.
On due diligence, advisers often work with inconsistent, self-reported, infrequently updated data, with no common identifier to tie a private fund back to comparable exposures.
On benchmarking, the holdings-based comparisons that work for public equities simply don’t translate; you can’t line up a private credit fund against a public index and learn much. The deeper gap is risk: without a consistent factor view that spans public and private, advisers can’t see the true diversification a private allocation adds, or the concentration it might hide.
That matters for the client conversation, because the case for privates is quantitative — MSCI Research estimates that a 15% allocation to private assets may add roughly 40 basis points of expected return annually while maintaining similar market risk. You can only make that case credibly with data and models that treat public and private on common terms.
How do you plan to differentiate your data and models for AI agents from competitors, especially as more players enter this space?
Agents are only as good as the intelligence beneath them, so the differentiation is in the layer, not the chatbot on top. Three things matter.
First, quality and breadth of data and models across asset classes — over 50 years of indexes, risk, sustainability, climate, and now private capital, all built on a consistent framework, so an agent reasoning across a whole portfolio is drawing on one coherent foundation rather than bolted-together feeds.
Second, a common analytical language — factor-based risk and tools like the MSCI Similarity Score let an agent compare any two portfolios meaningfully, which is exactly the kind of judgment you want to automate.
Third, transparency: in a regulated, relationship-driven business, advisers won’t act on a black box, so our models are explainable and auditable. As more players enter, many will compete on the interface. We’re competing on the trusted data and models the agents depend on — because that’s the durable advantage, and it’s the part that’s genuinely hard to replicate.
Beyond providing data and models, what role does MSCI see itself playing in the education and training of financial advisers on complex topics like private assets and non-US direct indexing?
A significant one, because adoption is ultimately a confidence problem. Our research shows advisers rank “difficulty educating and convincing clients” among the top hurdles to direct indexing, and private markets carry their own literacy gap. Better data and models help, but advisers also need the frameworks and the language to carry these ideas into client conversations.
So we see ourselves as a partner in capability-building, not just a data vendor — translating research into practical guidance on how a private allocation behaves in a portfolio, how direct indexing delivers tax and customization benefits, and how international exposure changes the risk picture.
That’s particularly relevant now: 61% of advisers plan to increase developed non-US allocations and 62% expect direct indexing to grow, so the demand for fluency is rising fast. The intelligence layer and the education around it reinforce each other — the data makes the advice rigorous, and the education makes the data usable at the point of client conversation.
This article was originally published here and is republished on Wealthtender with permission.
About the Author
Bill Hortz
Founder Institute for Innovation Development
