Artificial intelligence is dominating conversations about the future of financial services. But AI is only one part of a much bigger transformation taking place across investment operations. The more significant opportunity comes from bringing together three capabilities: high-quality data, intelligent analysis and automation.
That convergence was a recurring theme in a recent SS&C webinar, Beyond Compliance: Unlock Operational Alpha with Regulatory Excellence & Securities Recovery. The discussion, led by Richard Clark, SS&C GlobeOp Regulatory Solutions, Managing Director, & Ethan Mills, SS&C GlobeOp, International Hedge Fund & Class Action Recovery Services, Business Development, and moderated by Giles Smart, SS&C GlobeOp Regulatory Solutions, Managing Director, examined different ways to unlock operational alpha.
Data Comes First
AI cannot solve a data problem that has not been addressed at the source. Investment managers often need to combine information from multiple systems before they can produce a regulatory filing or operational analysis. Accounting data, investor records, portfolio information and prime broker data may all contribute to the final result. That makes data aggregation, normalization and validation fundamental.
A trusted data foundation gives technology something reliable to work with. Without it, automation can simply make an existing problem happen faster. With it, a much wider range of possibilities opens.
AI Can Move Teams From Processing to Analysis
Regulatory reporting provides a useful example. Reporting teams can spend significant amounts of time working through large datasets, comparing periods, checking calculations and investigating discrepancies.
AI can help accelerate those activities. Rather than manually searching through a large dataset, users can increasingly interact with information conversationally, asking questions and quickly identifying areas that require attention.
This doesn't remove the need for expert review. In a regulated environment, human oversight remains critical. But it can change where that expertise is applied. The team spends less time finding information and more time interpreting it.
Automation Tackles Repetitive Work
AI and automation are related, but they solve different problems. Some operational tasks do not require sophisticated analysis at all. They simply require someone, or something, to perform the same sequence of actions repeatedly.
Regulatory filings require information to be entered into a portal. Historically, that task has been performed manually, but digital workers can automate that type of activity, taking validated information from an existing system and completing repetitive steps on behalf of the user. That can turn a manual process that takes days into one that can be completed much more efficiently. The broader model evolves:
The Real Opportunity Comes from Connecting Workflows
The most compelling use cases emerge when these technologies are applied across organizational boundaries. Consider the connection between regulatory reporting and securities litigation recoveries.
Form 13F data is produced for a regulatory purpose. But the same holdings information can potentially be analyzed to identify securities class action settlement opportunities. Once the data is available in a structured and validated form, it can be passed into another process that compares holdings against eligible cases.
The data has effectively been given a second job. That is an important characteristic of mature operational technology: maximizing the usefulness of information that the organization is already collecting.
AI is Changing Technology Development
The impact of AI is not limited to the end user. It can also change how operational technology itself is built. AI-assisted development can accelerate the design and coding of new tools and workflows, potentially allowing teams to build and iterate more quickly. That creates a feedback loop:
Those capabilities can, in turn, improve the technology and workflows again.
From Efficiency to Operational Alpha
For investment managers, this convergence creates a broader opportunity than simply reducing manual work. Operational efficiency is important, but the greater opportunity may come from using technology to uncover possibilities that weren't visible when processes operated independently.
Regulatory data can support litigation recovery analysis. Automated workflows can free specialists to focus on higher-value activities. AI can help teams interrogate information that would otherwise take considerable time to analyze manually.
None of these capabilities need to operate in isolation. The future of investment operations may therefore be less about choosing between AI, automation or data and more about connecting all three.
The firms that build that connection effectively can move beyond asking how to make an operational process cheaper or faster. They can start by asking a more valuable question: What else can our data, technology and people accomplish together?
Watch the on-demand webinar to learn more.