7 AI plays for streamlined and profitable fund administration

1. Streamlining reconciliations for reliable reporting

Fund administrators maintain data across multiple systems, including accounting platforms, custodian feeds, market data providers and reporting tools. Because those sources arrive in different formats and at different times, reconciliations can be slow, manual and error-prone without the right tools — delaying NAV calculations and straining investor reporting.

AI-enabled aggregation can centralize and normalize data, automatically highlight anomalies and feed results directly into fund accounting workflows.

“The real value of AI comes when exceptions are filtered to focus on what matters,” said Karl Rohloff, Grant Thornton Advisory Services Director. “Operations teams need to know why something was flagged so they can act with confidence instead of chasing noise.”

When implemented effectively, AI-driven aggregation reduces NAV cycle times, lowers error rates and allows administrators to scale without proportional headcount growth. Performance can be measured through NAV accuracy, exception clearance rates, hours saved and completeness of audit logs.

Beyond reconciliations, AI can also support the integrity of financial statements by automatically scanning for inconsistencies across entities and flagging any discrepancies for review. AI-powered tools also assist in preparing financial statement notes by extracting relevant insights, summarizing complexities and ensuring disclosures meet regulatory standards and changes in accounting rules.

2. Streamlining system setup through document intelligence

Fund administrator personnel often spend days reviewing lengthy limited partnership agreements (LPAs) and private placement memorandums (PPMs) to extract fund-specific terms such as fee structures, carried interest, hurdle rates, redemption provisions and investor rights. Manual review slows fund setup and increases the risk of inconsistencies across systems.

AI can streamline this work by scanning documents, capturing key terms and translating them into standardized data fields that flow directly into fund administration platforms. Many large financial institutions are building AI tools that scan contracts, extract key clauses and standardize results across systems — replacing weeks of manual review with near-instant analysis.

“To make these tools reliable, firms must establish strong validation frameworks and exception-handling processes that safeguard accuracy when models misinterpret complex legal language,” said Michal Kazio, Grant Thornton CFO Advisory Services Experienced Manager.

3. Automating fund fee calculations

Calculating performance fees and distribution waterfalls is one of the most complex and high-risk workflows in fund administration. Each fund may have unique carried interest, hurdle rates or catch-up provisions, and errors can quickly lead to investor disputes, regulatory scrutiny or financial losses. These processes also require human oversight.

“AI’s role here is to handle the mechanics,” said Brian O’Sullivan, Grant Thornton CFO Advisory Partner. “You can use natural language processing to pull calculation rules directly from LPAs or PPMs and turn them into usable logic. Rule-based engines can then interpret different waterfall structures, while anomaly detection flags unexpected results.”

Together, these measures help firms strengthen credibility with both investors and regulators by moving processes beyond spreadsheets and single points of dependency. Performance can be assessed through calculation accuracy and improved turnaround time.

4. Real-time reporting

Most funds still publish NAVs on a lagged basis, considering the amount of time it takes to complete all components in the calculation. This delay is increasingly misaligned with investor expectations and today’s real-time data capabilities, driving a shift from the traditional accounting book of record model to the more dynamic investment book of record approach.

AI can accelerate NAV cycles by automating data aggregation and validation across positions, cash and multiple pricing feeds, with administrators reviewing exceptions before release. At the same time, outsourcing books of record to third-party fund administrators is becoming more prevalent — and with it, growing expectations for faster reporting, even if on a preliminary or estimated basis.  

“Automation can take NAV production a long way, but it can’t remove the human element,” Kazio said. “The key is building controls into the process — reviews, dual sign-off and audit trails — so speed doesn’t come at the expense of accuracy. Human-in-the-loop means AI-driven automation pauses at critical points so administrators can review and approve before moving forward.”

By combining automation with oversight, firms can shorten NAV production from historic to same-day reporting. Progress can be measured through metrics such as NAV turnaround time, pricing accuracy and investor satisfaction scores.

5. Making investor reporting a differentiator

Investor and client reporting has long been a pain point in fund administration. Administrators often juggle complex template management in Excel, customizing reports one by one — a process that is slow, costly and prone to errors. Legacy transfer agency systems add to the challenge by offering little flexibility for bespoke reporting.

“Managing templates has always been a headache in fund administration,” Rohloff said. “Many investment managers want something tailored to their brand to go out to their investors, and off-the-shelf reports rarely fit. What firms really need are frameworks that make personalization scalable without endless manual maintenance and rework.”

AI can scale personalization by pulling from multiple data sources to generate tailored reports automatically, adapt templates to client preferences and even produce narrative commentary, all while reducing reliance on spreadsheets. With the right safeguards for data security and scalability, AI-enabled reporting can cut generation time by 50 to 70%, translating into faster delivery, higher client satisfaction and ultimately stronger retention.

6. Scaling client service

Investor relations teams handle thousands of routine queries each year — NAV lookups, transaction status, document requests — all of which are critical for service but repetitive, leaving staff with less time for higher-value client engagement. AI can automate much of this work by acting as the front line for client inquiries, pulling data from source systems and generating clear, auditable responses.

This approach can automate 60 to 80% of routine queries, lowering service costs, speeding up response times and freeing up staff to focus on higher-value client interactions. The resulting automated data analysis can assist in uncovering trends on a macro level, which allows for more standardized responses to individual requests. This helps reduce the number of queries over time.

7. Streamlining trade settlement

Settlement breaks and delays are a persistent headache for fund administrators, often driving up costs and operational risk. AI models can ease that pressure by automating reconciliation across brokers, custodians and internal records, flagging discrepancies instantly instead of relying on manual cycles. Natural language processing helps standardize data from multiple sources, while anomaly detection highlights unusual settlement activity early, reducing breaks and exceptions.

Predictive analytics can also monitor settlement cycles to anticipate failures, drawing on historical patterns, counterparty behavior and market dynamics. Automated workflows then trigger remediation steps, accelerating resolution and supporting compliance with evolving requirements, such as T+1 settlement.

Together, these capabilities lower costs, reduce operational risk and improve efficiency, with key performance indicators including the percentage of trades automatched, exception resolution time and settlement failure rates.

AI in fund administration is already proving its value — cutting manual work in documentation, reporting, NAV production and more. The firms seeing the biggest impact are those that link each use case to clear business goals, build the right data and system foundation and scale adoption iteratively.

"AI won’t replace the judgment and skill fund administrators bring to the table. But it will ultimately take away the manual work that slows them down, allowing teams to focus on higher-value work, which enhances their service to investment managers and investors,” O’Sullivan said. “Increasingly, this also means the ability and aim to perform real-time audit testing — continuously scanning transactions, reconciling data and highlighting anomalies as they occur. That level of real-time oversight gives both managers and auditors earlier visibility, strengthening trust and raising the standard of governance across the industry. Several of our clients are already moving in this direction at pace.”