Seeking higher returns and more balanced portfolio risk, investment firms have spent years looking for ways to give regular investors a piece of the private market pie. Recent shifts in private portfolio allocations indicate that efforts could now be paying off. Oliver Wedlake, Senior Director, Wealth Management EMEA at Canoe Intelligence writes

According to the Deloitte Center for Financial Services, public investment in private markets across the European Union could grow from €924b ($1.1trn) today to a staggering €3.3trn by 2030, as retail investors continue to tread into private markets.

The persistent growth begs a few interesting questions. The first is why are individual investors seeking opportunities in a market noted for its lack of transparency and inherent complexity? The second is, why now?

For most retail investors, private markets are like a distant landscape hidden behind a wall of fog. They know there is land—or opportunity—there, but it’s impossible to see it clearly. Fewer disclosure requirements make performance harder to assess, while illiquidity and lengthy investment horizons can result in long lock-up periods that should—and often do—deter investment.

As it turns out, both questions can be answered simultaneously. Simplified access to alternative investment funds (AIFs) acted as an initial catalyst to the retailisation of private markets. While still dominated by private investors, data from the European Securities and Markets Authority (ESMA) indicates that retail investor share of the AIF market amounted to $11.3% in 2023, a number predicted to grow to 30% by 2030.

Investment product innovation has also played a role in lowering entry barriers by offering a measure of liquidity. Semi-liquid fund structures blend public and private asset classes within a single portfolio, offering redemption opportunities through open-ended or limited-liquidity formats. Taking the concept a step further, evergreen funds overcome the long lock-up periods and limited liquidity that have traditionally shut retail investors out of the market by allowing contributions to be made over time and providing cash-out opportunities during specified periods.

In other cases, favourable regulation has boosted access to new investment types. Once hailed as the primary gateway for retail investors to enter private markets, the European Long Term Investment Fund (ELTIF) was instead plagued with a series of challenges. In ensuing years, regulatory initiatives and continued product innovation have addressed key issues, resulting in ELTIF 2.0. By eliminating minimum investment thresholds and portfolio caps, while expanding the range of assets open for investing, ELTIF 2.0 has acted as an additional incentive for retail investors to enter private markets.

Despite these recent innovations, opportunities for retail clients remain plagued by complexity. Private assets are still less transparent, harder to value, and more difficult to exit than publicly traded investments, raising yet another question: how can everyday investors who may not fully understand the exposure balance risk?

Asset servicers and fund administrators are finding the answer in deep data analysis, gaining granular insights from fund-level documentation because it provides the essential legal and operational framework for an investment, defining everything from fee structures to investor rights. However, sourcing this complex level of data and turning it into meaningful intelligence is far from straightforward.

Smarter data needs smarter systems.

If there is one thing investment professionals agree on, it’s the value of comprehensive insight when it comes to evaluating investment opportunities—private or public. According to PwC, 59% of asset and wealth managers are in the process of adopting big data analytics or considering investments in data technology. The move underscores the importance of data but ignores a primary limitation.

Most established analytics systems in use today rely on structured inputs. Known data is organised into a specified format, parsed by big data models that in turn spit out insights meant to inform decision-making.

A significant challenge with alternative investments is the lack of standardised data. Since funds are not required to follow a specific format for documents– like quarterly reports, financial statements, investor letters, updates and partner capital account statements– each fund often uses its own unique layout. A single fund can even change its document format from one quarter to the next, creating further inconsistencies.

This inconsistency is a major obstacle for traditional analytics. The data is unstructured and unregulated, making it impossible for conventional analytics engines to automatically process and analyse. Instead of predictable, uniform data, you’re faced with a system where each fund follows its own unique formatting rules.

This is where the advancement of AI-driven intelligence comes in. Utilising large language models, AI-driven systems, automate data capture and then validate it for accuracy before delivering deep-level insights.

For asset servicers and administrators managing retail-focused alternative products, this means greater efficiency in creating tailored investor communications, even as reporting volumes grow.

Retail clients demand more, and AI-insights delivers.

In contrast to traditional private investors, retail clients engaging with alternative products demand a higher level of transparency.

AI-powered automated data extraction consolidates raw unstructured information from diverse sources, eliminating the need for manual data entry. Advanced engines then analyse inputs and turn unconnected data points into actionable information, making it faster and easier to generate highly tailored reports on aspects such as liquidity, risk, tax reporting, and cash flows.

By integrating with leading reporting systems, platforms like Canoe Intelligence further streamline the output of investor communications, allowing asset servicers and administrators to pre-select parameters and receive automated reports based on real-time alternative data. Automation allows for faster and more frequent communications without the tremendous effort required when manually collating and analysing data and generating reports.

In short, retail investors are changing the rules by saying “no” to the manual process of handling individually formatted quarterly reports. And, with the time they’re gaining back from switching to automation, they’re empowered to ask for more. So, now, they’re demanding additional data, such as holdings summaries, operating metrics, and transaction attributions, that allows them to get deeper into each asset and provide a clearer, more comprehensive picture of their portfolios.

AI-powered intelligence extracts data, drawing real-time connections across sources, to deliver clarity and confidence. For asset servicers and administrators, that means faster turnaround times and the ability to scale personalised communications without scaling headcount.