AI in Fund Finance
Measured Progress, Real Impact

Last week, Oxane participated in a joint industry webinar on the evolving role of AI in fund finance. Hosted by the Fund Finance Association as part of its Fund Finance University program, the session featured expert perspectives from Oxane and Ontra and explored practical AI applications along with the critical guardrails needed to deploy AI in fund finance operations. Given the strong interest in AI across the industry, Kanav shares his perspective on how AI is shaping the fund finance landscape—and what professionals should be preparing for, from implementation strategies to the importance of maintaining human oversight as AI scales across complex operations.

Imagine a junior analyst who has read every deal memo, can process 500-page credit agreements in minutes, remembers every LP commitment, and knows exactly how each fund's borrowing base model differs. Now imagine they never sleep, never forget, and adapt every time new data comes in.

That’s what AI is starting to feel like—not a replacement, but a remarkably well-informed second brain. In that sense, AI isn’t just another upgrade. It’s changing the relationship between professionals and the systems they rely on. So where does it fit into financial workflows?

The reality vs. the hype
With every other tool claiming AI capabilities, the most pressing questions fund finance professionals are asking today are: what is truly AI, what isn’t, how much actual impact is it making or does it remain a future ambition? The answer is becoming increasingly clear. AI is not a passing trend, or a short-lived experiment. It is part of a continuing evolution in how work gets done across financial operations. Far from replacing existing workflows, AI is being embedded into them. It is enhancing, not disrupting, how professionals operate by accelerating turnaround times, reducing manual effort, and supporting smarter, more confident decisions.

AI use cases in practice
AI is easing the operational overload in fund finance, by transforming manual, repetitive tasks, particularly in portfolio monitoring workflows where the sheer data volume creates operational bottlenecks. Our approach uses agentic frameworks that divide complex workflows into smaller, well-defined tasks. Each agent performs routine operations with strict guardrails—automatically handling repetitive work while flagging anything uncertain for human review. All key decisions route to analysts, maintaining human-in-the-loop oversight. Here's how this plays out across workstreams.

In communication and document management, AI categorizes email content and flags actionable items, reducing the need to manually sift through long email chains and attachments. Enhanced ETL processes use AI to identify, extract, and standardize data from unstructured and structured sources. AI scans through annual reports and extracts company financials. It can go through credit agreements and extract financial and information covenants, converting these into standardized configurations.

In managing ongoing utilization, AI checks if all the required inputs are in place and triggers borrowing base calculations, pre-fill lender templates, run analysis so analysts in charge can make well-informed decisions. For portfolio intelligence, AI enables conversational exploration of portfolio data through chat-based interfaces, allowing natural language queries instead of custom report building. Across all applications, AI provides speed and structure, while strategic decision-making stays exactly where it should be - with people. These aren’t test cases, these are real examples of AI delivering efficiency where it matters and where it was unthinkable a few years ago.

AI with guardrails
In fund finance, AI has the potential for meaningful and scalable adoption, but only when it comes with built-in guardrails like secure deployment, private data storage, embedded approval layers, and zero data retention agreements, all of which are fast becoming standard. Just as critical are investments in training models accurately, and designing systems with containerized boundaries. At the center of it all is the human-in-the-loop model, which keeps people in control, ensuring AI speeds up decision-making, not taking it over. When deployed with clear oversight, robust data controls, and aligned with enterprise controls, AI earns the confidence of both users and decision-makers.

From what we’ve seen at Oxane, the most effective AI use cases aren’t about full autonomy. They’re about solving real operational pain points. AI delivers the most value when it takes over high-volume, unstructured but repetitive tasks, so investment teams can scale with confidence, make faster, better-informed decisions, and focus their energy on building relationships and trust.

And from our conversations, AI adoption in fund finance is already underway, and the momentum is growing.