Artificial intelligence (AI) is transforming fund administration, helping with data extraction, enhancing analytics, and supporting compliance and cybersecurity, moving beyond standalone features
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AI has shifted from being experimental to foundational in fund administration. Previously, it was primarily used to automate individual tasks such as reconciliation, reporting, and data entry.
Today, the focus is on thinking strategically, connecting data across systems, interpreting insights, and enabling smart decision-making throughout the fund lifecycle. Alter Domus recently analyzed 300 RFPs to gain insight into evolving client needs. The shift is clear: managers are no longer questioning whether to use AI, but how deeply it can be integrated into their operations. The emphasis has shifted from basic operational efficiencies to building infrastructure that supports transparency, scalability, and an improved investor experience.
I think there are a few. One example is AI-powered natural language processing (NLP), which can extract key data points from investor communications and fund documents, improving accuracy and easing the manual lift required to comb through that much documentation.
Machine-learning algorithms can help spot anomalies and inconsistencies within portfolios, including NAV calculations and transactional outliers, by analyzing historical trends.
Beyond standalone tools, the industry is seeing the shift where AI is increasingly embedded into broader workflow tools, supporting data validation, risk scoring, and predictive analytics. In firms where these are adopted, such capabilities can support more informed and timely decision, enhance internal knowledge sharing, and assist with NAV validation.
These tools are not ‘silver bullets’, as people will remain an important part of the fund administration process, but AI is offering building blocks for more operational insights.
When we talk about private equity, the big focus is really on KPI-level data, so that means both at the portfolio company and the fund levels. Managers are looking for tools that let them extract performance trends, ESG metrics, and other narratives that can boost LP engagement. AI can start to bring these structures and bring better reporting and scenario analysis from the private equity perspective.
Now, if we look at private credit or private debt, those managers really need real-time visibility for covenant monitoring, default risk, risk scenarios, and cash flow forecasts. It’s an almost daily operation, and that’s what’s driving interest in how AI can help administrators scale up without sacrificing data quality.
Then, looking at real estate and infrastructure, asset managers use AI to help translate operational performance. So, this means measuring and monitoring occupancy rates, lease maturity, and even utilization. When you have that information, it turns into actionable insights for the fund’s performance. There’s also more frequent regulatory scrutiny in real estate and cross-jurisdictional tax exposure. So, AI can play a big role in automating compliance in those spaces, which is a unique nuance for real estate.
AI isn’t a one-size-fits-all solution, but it can be tailored to support operational efficiencies and reporting needs for each asset class.
The regulatory space is always very complex, and rule books change with frequent updates and different moving timelines. I think fund administrators and managers alike are under pressure to ensure the accuracy of compliance with the regulations as they come through.
AI, in my view, has the potential to help firms simulate compliance scenarios. Certainly, it can flag risks that come up and then automate the audit-ready documentation for regulators. It can help in everything from automated exposure, calculations, embedding rules, and check mechanisms.
It’s important to strengthen the life cycle as you think about compliance metrics and reducing the errors that we would have today from manual oversight of these processes. When we looked at the RFP analysis that we did in our latest report, nearly all fund managers who prioritize EU or cross-border distributions asked about AI capabilities or enablement when it comes to the compliance functions, especially those that would reduce the burden of reporting. Compliance can be expensive for legal teams, operations, as well as internal and external legal teams. I think that the shift with AI brings agility around regulatory compliance. It will be a core differentiator in the market, while also being cost-effective.
Cybersecurity used to be an IT concern, and it is no longer just an IT issue. It’s fundamental to building trust in any client and service provider relationship. There’s great pressure from investors to have an infrastructure that can respond very quickly to any kind of threat of cybersecurity.
AI can be used to flag abnormal behaviors or patterns, which support intrusion detection. With real-time monitoring, we can go beyond basic rule-based systems and anticipate potential breaches. AI can also help with faster recovery time from incidents. This is definitely a top due diligence item that we see from the LPs.
The market is starting to see early use cases where AI can help spot patterns in fund flows, investor behavior, and even market stress indicators. It’s not a crystal ball, but the data helps us see historical trends and flags liquidity pressures earlier in the process to help managers get ahead with capital activity. Fund administrators can support clients with faster and more informed decisions.
Predictive analytics requires strong governance and data quality, so it’s critical that we continue to support having the best data and make sure there’s good governance on top of it.
One of the main challenges is fragmentation. AI depends on clean, structured, and integrated data, but many firms still use disconnected systems. Since you can’t extract data from a black box, people remain essential in this process. Skilled professionals must interpret AI outputs and turn them into action.
In addition, regulation around AI is evolving, so we must monitor developments closely. The firms that succeed will build modern infrastructure, rather than plugging AI into legacy frameworks. They will rethink data flow across their ecosystem, fully integrating tools into tech platforms, architecture, and teams so clients can make faster, better-informed decisions. We believe that prioritizing adaptability and creating solutions that evolve with our clients is key.
About
Jessica Mead leads Alter Domus’ North American operations, driving the delivery of fund administration and debt capital markets solutions for private capital clients. She plays a key role in shaping service models for institutional investors, aligning operational execution with regulatory and legal frameworks.
A seasoned legal and business executive, Jessica has helped scale Alter Domus since its 2008 acquisition of Cortland Capital Market Services, serving as General Counsel and Head of Legal, Risk, and Compliance, and as a member of the Group Executive Board. She holds a J.D. with honors from Drake University and a B.A. in Business and Political Science from Coe College.
This article originally appeared in Service Providers in Private Markets 2025.
This is a sponsored opinion by Alter Domus. The views expressed are provided as of September 2025, do not constitute an endorsement, recommendation, or any other advice, and are subject to change. The following content does not necessarily reflect the views of BlackRock, Preqin, or any of its affiliates. Alter Domus is not affiliated with Preqin. Preqin received compensation from Alter Domus in exchange for publishing this content.