Hedgeweek® SS&C - Seismic Shift: The Hedge Fund Technology Report

SEISMIC SHIFT

THE HEDGE FUND TECHNOLOGY REPORT 2026

EXECUTIVE SUMMARY

Technology has fundamentally reshaped warfare throughout history. Strategy, logistics and willpower still matter, but technological superiority has mostly turned the tide of conflicts – by outranging, outsmarting or simply outpacing the opposition.

Thankfully the hedge fund industry is not at war. But the dynamic may be recognisable to many of you. Performance still depends on judgement, conviction and the basics of risk and capital allocation. Increasingly, though, the firms that pull ahead are the ones with better infrastructure underneath. The morning meeting prepared by an AI agent overnight. The credit agreement passed in minutes rather than days. The data layer clean enough that the next deployment is twice as fast and a fraction of the cost.

Our 2026 Hedge Fund Technology Report, in collaboration with SS&C, sets out to understand how that race is actually being run in 2026. Where the money is going. Who is buying what. Whether technology is delivering on its promise or whether the industry has confused activity with progress. And, increasingly, whether funds are spending on the right things in the right order.

The picture that emerges from our survey of over 100 hedge fund managers is one of structural commitment but uneven conviction. Budgets are up almost everywhere. The destination of the spend is narrower than it sounds. The largest and smallest funds are converging on similar things but for different reasons. The middle of the market is doing something else altogether. And nearly a quarter of managers admit that their infrastructure isn’t where it needs to be.

For managers, we hope that this serves as a benchmark. For allocators, a window into the operational maturity of the funds you’re underwriting. For both, a snapshot of an industry that knows it needs to invest – but isn’t always sure it’s investing in the right things.

MANAS PRATAP SINGH

A key source of data in this report is the results of our Hedge Fund Technology Survey conducted in Q2 2026. The survey collected responses from over 100 hedge fund managers globally, spanning AUM ranges from under $100m to over $5bn, and strategies including equity long/short, multi-strategy, fixed income/credit, global macro, managed futures and digital assets.

Further insights were gathered during interviews with industry experts, including representatives from SS&C Technologies and Feri Trust, plus named and unnamed CTOs and COOs at multiple hedge funds.

1

The race is on: Spending is up almost everywhere

61% of funds increased technology budgets over the past 12 months. Within that group, 79% of funds stated investment priority sits in just three categories: AI and machine learning, trading and portfolio management systems and data infrastructure. The era of buying selectively has begun.

2

AI investment is bimodal

KEY FINDINGS

3

Asset class expansion is pulling spend forward

The largest and emerging funds are concentrating on AI.

The middle of the market is focussing elsewhere. 40% of funds with over $5bn in AUM named AI as their top priority.

So did 30% of sub-$500m funds. But $500m–1bn funds chose trading systems first by a wide margin.

73% of funds that have expanded into private credit, direct lending or structured products increased their technology budgets. 63% of them report unanticipated operational challenges. New asset classes are quietly rewriting the operational rulebook for funds that thought they could absorb the complexity.

4

The funds behind the curve are reaching for the wrong thing

A quarter of managers describe their technology infrastructure as behind where it needs to be. Of those, two thirds are prioritising AI and trading systems. Just 7% are prioritising data infrastructure –the foundation everything else sits on. The industry is buying the visible layer before fixing the one underneath.

5

AI works for document processing. Almost nothing else, yet

Document processing is the only AI use case showing broad tangible impact (42%). 30% of funds say AI is not yet having a tangible impact at all. The industry’s biggest priority for 2026 is delivering in one workflow and disappointing in most others.

PART I: THE SPEND STORY

How and Why Hedge Funds Are Investing in Technology in 2026

THE RACE THAT STARTED TWO YEARS AGO

Two years ago, much of the technology spending at hedge funds we’re seeing today was just getting started. Many AI tools had just become broadly available, the trade press was full of breathless coverage, and the question every CTO faced in budget meetings was some version of ‘what’s our AI strategy?’ Firms experimented, adopted broadly and worked out what stuck.

In 2026, the question has changed a bit. Spending is still up: 61% of funds in our Q2 2026 survey increased their technology budgets over the past 12 months, 12% of them significantly. Only 2% reported any cut, and none significantly (Chart 1.2). The race hasn’t slowed. But the firms running it have changed how they run.

Nishant Gurnani, partner at quantitative hedge fund Versor Investments, articulates the shift. Two years ago, he says, the budget conversation was “about breadth” – adopting the best tool for each problem and seeing what worked. In 2026, the centre of gravity has moved “from breadth to depth.” What gets approved easily now is “anything that

consolidates workflows, not expands them.” The standalone point solution that doesn’t connect to the rest of the stack faces far more scrutiny than two years ago.

The phase of casual experimentation seems to be over. Most funds know what they want and increasingly know what they don’t.

That doesn’t mean spending is slowing. At Iguana Investments, a younger London-based manager, CEO Fahd Ahmed says technology budgets have risen by 30% to 50% over the past two years – with the largest line item being a third-party consultant acting as an outsourced CTO. The underlying logic is the same one driving spending across the industry: AI is making it possible to do things that previously required hiring.

Phillip Chapple, COO of London-based Monterone Partners, describes a more measured version. “Definitely more spend on developing AI tools,” he says, “but still a firm focus on the end use case and value.” His warning – which the rest of this report keeps returning to – is that plenty of firms have gone “down the rabbit hole” and ended up with limited benefit.

WHERE THE MONEY ACTUALLY GOES

Headline budget growth is one signal. The destination is another. And here the story sharpens.

Among the funds in our survey that increased budgets, three categories absorb nearly four fifths of stated investment priority (Chart 1.3):

•AI and machine learning: 32%

•Trading and portfolio management systems: 24%

•Data infrastructure and analytics: 22%

In spending terms, the industry is investing in three things. Operational categories that occupy enormous attention in the trade press –investor servicing, regulatory technology, cybersecurity – are not where much of the new money is going.

Deepak Gurnani, founder of Versor, describes the deeper change beneath the dollar split. At Versor, the largest share of 2026 spend is going to replacement and modernisation rather than net new capability – rewriting code built for an earlier era of compute, retiring legacy systems. The hardest category to defend internally, they note, is maintenance: “It is genuinely difficult to make the case for spending on something that already works.” But under-investment there creates compounding technical debt that eventually forces a more expensive cycle.

Many hedge funds like Versor seem to not be buying shiny new things. They are tidying up.

UP ACROSS THE BOARD

Cut by fund size, the spending picture splits further (Chart 1.4).

The conventional industry narrative is barbellshaped: the largest and smallest funds invest, the middle conserves. The data tells a more interesting story. Over half of the hedge funds under the $500m mark are spending more now than they were in the past. And that share rises to two-thirds for funds running between $500m and $1bn in AUM. Scale doesn’t seem to bring the costs down, however, as two in five billion dollar plus hedge funds are also spending more on technology in 2026.

The shape that emerges is closer to that of a trajectory of runners doing a 100m sprint. All moving quickly in the same direction. The very largest funds are spending at scale, compounding their advantage. The smallest are building from scratch. The middle of the market is doing the work needed to scale and that the others with lower AUMs may end up needing soon enough.

THE MACRO EFFECT

Technology spend isn’t moving in isolation. It is being pulled forward by where funds are investing their capital.

Of the respondents who reported expanding into private credit, direct lending, structured products or other complex asset classes, 73% had increased their technology budgets in the past year. Among funds that hadn’t expanded, the figure was 54%. A 19 percentage point gap (Chart 1.5).

But it’s the operational consequences that reveal what’s really happening. Of the funds

that have expanded, 63% report unanticipated operational or technology challenges (Chart 1.6). For credit-focused strategies, that figure rises further. Funds are moving into less liquid, less standardised territory and discovering – often too late – that their existing operations weren’t built for it.

Volatility didn’t make funds spend less on technology. Geopolitical disruption didn’t make

them pull back. What’s shaping technology decisions in 2026 is the slow drift of hedge funds into asset classes their operations were never designed for. The macro environment isn’t a brake on technology spending. It’s the engine driving it.

A COO at a fund that recently expanded into private credit puts it bluntly: “We knew the documents would be more complicated.

We didn’t appreciate how the volume would compound. Within six months of putting the strategy on, we had three people whose entire job was processing notices and chasing data that should have been structured.

The technology investment wasn’t optional, it was overdue by the time we made it.”

BHAGESH MALDE

General Manager, SS&C GlobeOp

Hedge fund spending on technology has increased across the board, with no respondents reporting cuts. What’s driving this shift, and how should managers think about optimising spend?

Hedge fund technology spending is rising because the capabilities of modern platforms and AI have fundamentally changed. What was experimental even two years ago is now operational. Firms can automate workflows, process large volumes of documents, generate code, monitor risk, and update systems of record with far less manual intervention. Over the past 12 months in particular, managers have moved from exploring AI to actively looking at how it can improve productivity, scalability and operating leverage.

The challenge now is less about whether to invest and more about how to invest intelligently. Optimising spend starts with identifying where technology can remove low value manual work and allow teams to focus on higher impact activities. The firms seeing the best results are approaching AI

and automation with clear operational objectives rather than treating it as a broad innovation exercise.

The harder part is measurement. Managers need to tie technology investment to tangible outcomes such as reduced operational costs, faster processing times, lower error rates, improved client service or the ability to scale without adding headcount. That can be difficult in a market where pricing models, token costs and AI capabilities continue to evolve rapidly. But ultimately, successful firms will be the ones treating technology investment with the same discipline as any other capital allocation decision, linking spend to measurable ROI.

Document processing is where AI is delivering the most tangible impact. What made it the breakthrough use case, and what’s next?

Document processing became the breakthrough AI use case because it solved one of the industry’s biggest operational pain points: manually extracting and validating data from emails, notices and legal documents.

At SS&C, we acted as “customer zero” internally before bringing these capabilities to clients. We started applying AI to loan agent notices more than five years ago and now process more than three million notices annually, including large spikes at monthand quarter-end. Custom-trained neural networks have significantly outperformed traditional rules-based software. Having seen these results internally, we now offer these capabilities to our clients either through pre-built agents or by building custom agents to serve their specific needs.

For complex documents like 800-page credit agreements, we use large language models with retrieval-augmented generation, breaking documents into chunks stored in a vector database. We achieve over 60% straightthrough processing at field level.

The next frontier is code generation and analysis of complex datasets. The new models have made significant advances through reinforcement learning with verifiable rewards, and those capabilities are only starting to be exploited.

Very few funds run fully automated AI extraction on complex documents. The majority are semi-automated or fully manual. What’s the actual blocker?

The biggest blocker is governance and trust. AI models are probabilistic, not deterministic. They can be wrong, hallucinate, and be oblivious to it, so you must check the output. This is why all of our AI-enabled capabilities and agents we offer to clients are built and run on our governance-first AI Gateway platform. The platform enables enterprises like ours to ensure AI is used responsibly, with robust security, compliance visibility and control built in.

Our position is AI in financial services must be governed, transparent and auditable because you are dealing with sensitive information across your workloads. For workflows like loan agent notices, we can systematically validate outputs against structured data: does the notice reference a facility we hold, does the amount reconcile to the general ledger, are rates within expected ranges. If those checks pass, the process can move straight through automatically.

For more complex documents like credit agreements, the document itself is often the only source of truth, so human verification remains critical. That is where retrievalaugmented generation (RAG) becomes powerful. Instead of asking someone to search through an 800-page PDF manually, the system presents the reviewer with the question, the extracted answer and the exact document chunk it came from. It keeps humans in control while dramatically improving speed and efficiency.

Investor onboarding seems to be one of the most manual workflows in our survey. Why has investor servicing proved so resistant to automation?

Partly, it’s the lack of standardisation in the investor onboarding process, which often comes down to customer terms, side letters and special arrangements for certain investors. We do apply automation to incoming investor documents, using AI to extract the data, with human review to check it’s correct. An end-to-end digital process would be preferable. We see that happening

in some areas already, and broader adoption would help reduce costs and improve the investor experience.

NAV review is one of the workflows where AI is least visible today, yet it’s also one of the most consequential. What does an AIpowered NAV review process look like?

NAV review is unlikely to ever become 100% AI automated because it sits at the core of what we do and ultimately requires human judgment, accountability and oversight. Unlike document extraction, NAV review involves complex valuation decisions, interpretation and reasoning which cannot simply be reduced to straight-through processing.

Where AI is delivering value today is in augmenting the process. One major area is anomaly detection. NAV workflows generate large volumes of data, and AI is very effective at identifying unusual patterns, outliers or unexpected P&L movements which may warrant further investigation. That helps teams focus attention faster and more efficiently.

The newer and more exciting capability is AIassisted reasoning. Modern models can now analyze complex valuation issues, identify potential root causes and explain why a valuation movement occurred. That progress over the past 12 months has been significant.

But this is also where governance matters most. AI can still be confident and wrong, particularly in nuanced financial workflows. Our view is AI should support NAV review, not replace it.

This is where process orchestration becomes critical. At SS&C, we wanted to ensure humans, AI agents and digital workers would be able to collaborate in one secure, connected, scalable environment. That’s how we created WorkHQ, our agentic orchestration platform. The platform enables enterprises like ours to coordinate people, automation and AI across end-to-end workflows, ensuring governance, security and compliance and embedded into every process. Our view of the future is a human-in-the-loop model where AI accelerates analysis and surfaces insights, while experienced professionals remain

responsible for validating conclusions and making final decisions.

What separates funds that are genuinely ahead operationally from those that just think they are?

The leading funds have exceptional control over their data and processes. They know

exactly what they process, how much is fully automated, what’s still manual, and what their delivery times are versus expectations. They have KPIs defined and real-time transparency on whether they’re hitting them. They know how they’re going to move the needle on further automation and have a plan to execute it via internal systems or leveraging service providers’ economies of scale. The technology landscape is moving the bar on what operational excellence looks like.

PART II: WHAT FUNDS ARE TRYING TO SOLVE

The Priorities, the AI Reality Check and the Sequencing Question

THE PRIORITY THAT ISN’T WHAT IT SEEMS

Asked to name their single biggest technology investment priority for the next 12 months, managers gave the answer the trade press would expect (Chart 2.1):

•

•AI and machine learning: 32%

• Data infrastructure and analytics: 24% Trading systems: 22%

As Deepak Gurnani puts it, “AI models need data, and you have to get your data organised and cleaned to feed the machine.” Mid-sized firms whose trading stacks haven’t been touched in years are recognising that putting AI on top of broken infrastructure produces a Ferrari engine in a car with bad brakes. The unglamorous work has to come first.

THE AI REALITY CHECK

Abig chunk of the industry has decidedAI is the priority. Is it actually delivering?

The honest answer is: in one workflow, yes. In most others, not yet (Chart 2.3).

AI tops the list. Data infrastructure and trading systems follow. Together those three absorb 76% of stated priority. The narrative writes itself: the industry is going all-in on AI.

But the topline can be misleading. Cut the data by fund size and a slightly more nuanced picture emerges (Chart 2.2). Among funds above $5bn, two in five respondents named AI as the top priority. Among funds below $100m, AI was the leading choice at 29%.

But in the middle – particularly $500m to $1bn – AI is not the priority. Trading systems is, by a wide margin (75% of that cohort). The $1bn-5bn bracket is the most distributed, with no single category exceeding 25%. The funds in the middle of the market are not chasing AI headlines. They are doing the foundational work others may eventually have to come back to. This isn’t lethargy. It’s sequencing.

Document processing is the breakthrough use case. Everything else clusters behind it.And the second-largest answer – 30% sayingAI has not yet had a tangible impact at all – should give every CTO pause.Ayear on from peakAI hype, the gap between what funds are investing in and whatAI is delivering remains wide for most of the industry.

The interviews reveal a striking consistency about whereAI does work.At Monterone, Chapple says the firm has built tools to interrogate 11 years of accumulated research materials – with the caveat that “it is important to always be able to audit and validate the results.”

At Versor, the Gurnanis report the clearest gains in coding productivity.According to Deepak Gurnani, “code sits at the centre of the investment process, and the improvement in how quickly we can write, test and review code has been material”.

AI and machine learning

Trading and portfolio management systems

Data infrastructure and analytics

NAV and fund accounting

Cybersecurity

Investor onboarding and servicing

Regulatory reporting and compliance

Reconciliation and exception management

Other

Document processing and data extraction

Not yet having tangible impact

Risk management and compliance

NAV review

Investor reporting and communications

Reconciliation and exception management

Loan and credit operations

At Iguana, Ahmed echoes the same split: AI has been useful for modelling, marketing and data aggregation but has not yet delivered for idea generation – partly a function of the firm’s reticence about feeding specific process materials into public models.

Across all three firms, the same pattern emerges. AI works for research, code and document processing. It does not yet work reliably for much of operational or investment decisions.

The Gurnanis are sharpest on the limitations. The reliability of LLM outputs for quantitative

and numerical reasoning is the area where expectations have most outpaced reality, they say. Outputs can sound authoritative while being subtly wrong – which, in an investment context, requires verification that “somewhat offsets the speed gain.”

Marcus Storr, Head of Alternative Investments at Feri Trust, sees the same boundary from the allocator side. Across the equity long/short managers Feri allocates to, Storr describes a now-common pattern: in-house AI agents that process overnight investment banking research and produce the morning meeting note before the team arrives at eight. “It has analysed it,

Investor onboarding and subscription/ redemption

Regulatory reporting

Reconciliation and break resolution

NAV calculation and review

Data aggregation from multiple sources

None

summarised it, sliced it, diced it and compared it against the fund’s long and short positions.”

That’s genuine value. But the boundary between AI as efficiency tool and AI as decision-maker remains firmly intact. “It’s primarily being used as an efficiency tool – to filter, analyse, summarise and produce research that human investors use to make decisions.”

For now, that’s the consensus. AI is a research accelerator. It is not yet, for most firms, an investment decision-maker.

WHERE AUTOMATION HASN’T LANDED

The flip side of AI’s wins is its persistent failures. Which workflows are still primarily manual (Chart 2.4)?

Investor onboarding tops the list. Only 10% of funds use fully automated AI extraction on complex documents such as loan agreements and subscription documents. 37% remain predominantly manual.

The interviews suggest the reasons are partly structural and partly regulatory. Chapple notes that the challenge with investor onboarding is “ensuring that automation does not miss any nuances or complications” – the regulatory stakes are high, and the analysis often becomes “measuring potential time savings versus the amount of checking required.”

At Iguana, Ahmed’s firm outsources the bulk of onboarding to a third-party transfer agent, which has automated data ingestion, sanctions and KYC checks – but final approval is always conducted by a staff member. “Given the sensitivity of onboarding and the ramifications of a false negative,” he says, the verification layer is non-negotiable. Nishant Gurnani adds that allocator-specific requirements “resist standardisation,” and that the limiting factor is primarily regulatory rather than technological. The pattern is consistent. The workflows where AI hasn’t landed are the ones where being wrong has the biggest consequences. Document processing can potentially be forgiving – a misread field gets caught in review. Investor onboarding, NAV calculation and regulatory reporting are not.

THE SEQUENCING QUESTION

The most uncomfortable finding in the entire survey is this one. A quarter of funds rate their technology infrastructure as behind where it needs to be.

When asked what they’re prioritising to fix it, the split is striking (Chart 2.5).

Two thirds of behind-the-curve funds are prioritising AI or trading systems. Just 7% are prioritising data infrastructure. The funds most willing to publicly admit they’re behind are reaching for the most visible layer before fixing the foundation everything sits on.

The operational leaders interviewed for this report are notably more cautious. Chapple, asked what he would tell a peer fund planning to spend heavily on AI to close the gap, offers a deliberately quiet answer: “Learn from other firms’ mistakes. Seek advice on what has actually worked for others.” Ahmed is sharper. He is “always wary of large catch-up spends” –they often yield less than optimal results in fastmoving environments where today’s investment may be redundant in months. He recommends a three-year roadmap rather than a one-off sprint, with a dedicated AI engineer ideally on a contract basis.

Nishant Gurnani is the most direct of all. “Do not confuse spend with strategy,” they say. “The firms that will emerge with a durable advantage are not necessarily the ones that spent the most. They are the ones that thought carefully about where AI fits into a differentiated investment process and built their systems accordingly.” Their parting line is the report’s central argument in seven words: “process clarity first, then tooling.”

That, in the end, is what 2026 is really about. Not whether to spend. Not even what to spend on. But the order in which to spend.

Note: Sample limited to 15 funds rating their infrastructure as behind where it needs to be.

CONCLUSION

The story of hedge fund technology in 2026 is not the story of AI adoption. It is the story of a market learning, two years in, that the easy answers were oversold and the hard ones are still ahead.

Budgets are up. The destination of the spend has narrowed. The firms running the race have stopped sprinting and started sequencing. The largest funds are buying capability they have the scale to compound. The smallest are building from scratch. The middle is fixing what got missed when the music was loudest.

AI is delivering in one workflow – document processing – and disappointing in most others. The firms admitting they’re behind are reaching for the most visible fix when they likely need the most fundamental one. The CTOs and COOs interviewed for this report are unusually consistent on the point: process clarity comes first, then tooling. Start with coding. Avoid large catch-up spends. Learn from others.

For allocators, the diligence question of 2026 is sharper than it looks. The 25% of managers who say they’re behind aren’t hiding it. Whether they’re spending on the right things, in the right order, is the question worth asking.

The era of unrestricted technology spending continues. The era of disciplined sequencing is just beginning.

CONTRIBUTORS:

Manas Pratap Singh Head of Hedge Fund Research manas.singh@globalfundmedia.com FOR SPONSORSHIP & COMMERCIAL ENQUIRIES: Please contact sales@globalfundmedia.com