The system of record is becoming an increasingly important differentiator in the asset servicing space. Why do you think that is the case?
Lior Yogev, FundGuard chief executive and co-founder
If you look at the investment process, it ultimately depends on having clean, trusted, and authoritative datasets to support both investment and post-trade activities.
The industry has long been seeking a high-quality, all-in-one view of the portfolio to support trading, risk management, and cash management. Historically, this has been difficult to achieve, largely because the necessary technology wasn’t in place.
Across traditional funds and assets – such as listed securities, asset-backed instruments, and simple derivatives – there were systems designed to manage those areas. However, when it came to hedge funds, alternatives, and private markets, each segment evolved its own specialised systems. As a result, firms have been operating across highly fragmented platforms and data models, attempting to consolidate information only at the reporting stage. Even then, the data was not available in real time in a way that could effectively support front-office decision-making.
This fragmentation extended into areas like tax and financial reporting, creating further inefficiencies. Over time, it led to the proliferation of multiple operational data stores, many of which were not fully aligned with the underlying fund data. Because data was repeatedly transformed and manipulated, this introduced errors and inconsistencies.
As a result, the industry is now increasingly focused on establishing a single source of truth: a system of record that provides an authoritative dataset for trading, cash management, and risk analysis, rather than relying on disconnected solutions.
When you look at asset owners, they invest across a wide range of asset classes, from treasuries and public equities to private markets, funds and real estate. Achieving a true total portfolio view has been a longstanding objective, but one that has been constrained by legacy technology.
FundGuard, along with other emerging modern platforms, is now working to re-architect this approach. The goal is to build a modern data platform where trades are reflected in the portfolio in real time, eliminating the need for end-of-day or batch-based updates.
In this model, firms can view their entire portfolio holistically, with a clear understanding of cash positions and risk exposure, while also maintaining seamless reconciliation with custodians. At the same time, traders and portfolio managers are equipped with timely, accurate data to make better-informed investment decisions.
All of this is centred on a single, authoritative system of record.
A lot of asset managers and asset owners are often constrained by a spaghetti network of interconnected platforms. How much of a barrier is that when you come in to help implement your solution, whether on top of or alongside those existing systems?
At FundGuard, our origins are rooted both in technology – specifically building enterprise-grade cloud solutions with deep industry expertise. We understand the challenges that come with legacy infrastructure.
From day one, AI has been core to what FundGuard does. One of the first use cases we focused on was fast and intelligent onboarding, because getting clients up and running quickly is critical. We didn’t want to get bogged down in multi-year onboarding and data-mapping projects.
When you think about it, all the data we consume – whether it’s trades or market data – is ultimately structured, and we know what we’re looking for. By applying machine learning, and more recently generative AI, we’ve been able to make data transformation much more efficient.
As a result, we can take clients into production in less than three months in some cases. This speed is only possible because we use AI to ingest and interpret data coming from different formats and channels – whether through APIs, message buses, or files, which unfortunately, are still very common across financial institutions.
We’ve been able to leverage AI to accelerate onboarding by quickly understanding incoming data and applying the knowledge we’ve built from previous implementations. This allows us to identify what the data represents, map it into FundGuard’s model, and iterate rapidly.
In some cases, we can get client portfolios up and running within days, already producing results on the platform. We can then show a clear comparison between FundGuard outputs and what existed in the legacy systems.
That’s ultimately why we’re able to move at a speed that traditional players in the industry haven’t been able to match.
What does a next-generation system of record actually look like in practice for clients?
At a high level, it’s real-time and it spans all asset classes. That includes listed securities, derivatives, OTC instruments, and private assets – all brought together in a single platform.
It’s also very rich in the data it provides. Clients can understand everything from cash positions to where their securities are held, as well as manage events like corporate actions and the elections associated with them. The goal is to have full transparency and control across the entire portfolio.
From a technology perspective, scalability is critical. In both asset management and wealth management, platforms are attracting increasing numbers of investors, and the volume of products and transactions is significant. A modern system of record has to support that scale while still delivering meaningful analytics.
Connectivity is another key component. The system needs to integrate seamlessly with machine learning models, AI capabilities, and visualisation or business intelligence tools. At FundGuard, the idea is that AI is not an add-on, but an integral part of how the system operates.
This also extends to reporting and dashboards. Clients need access to information that is both detailed and aggregated, presented in a clear and intuitive way, and available in real time. Ultimately, everything needs to exist in one place and be easily consumable.
All of this is about addressing the full spectrum of client needs, across both traditional and alternative asset classes, while delivering the level of timeliness, analytics, and user experience that people now expect in other areas of their lives.
There’s also a growing recognition across the industry that trying to solve these challenges at the reporting layer – by pulling together disparate, often outdated data sets and then transforming them – simply doesn’t work. That approach sacrifices real-time capabilities and introduces complexity.
At the same time, the market itself is evolving. Trading is becoming increasingly continuous, moving toward a 24/7 model. This is already evident in digital assets, and as more assets become tokenised, it’s likely to extend across other asset classes and venues.
As a result, the underlying technology must be able to support a real-time, always-on environment, delivering rich, accurate information while enabling the responsible use of AI.
Firms that don’t address these challenges at the source – by modernising their system of record – will find themselves increasingly limited in how effectively they can leverage advances in AI and other technologies.