Private credit has grown considerably in scale and complexity over the past decade, and the data infrastructure supporting it has struggled to keep pace. Loan data remains one of the most operationally intensive areas in the asset class, as large volumes of data arrive in formats that make automation challenging. Agent notices, credit agreements and amendment documents come in as PDFs, emails, and spreadsheets, each structured differently, and requiring someone to read, interpret and extract the information that matters. Despite many automation efforts in the industry, this asset class remains overly manual.
The result is a familiar set of friction points. Teams spend hours on data entry; errors are introduced during rekeying, and downstream systems receive delayed or inconsistent information. For firms managing larger portfolios, these problems exacerbate with volume. Managing loans at scale means more notices, more manual touchpoints and more opportunities for the data to degrade before it reaches the people who need it.
AI is changing this equation, though not always in the way the market conversation suggests. The technology is not displacing operations teams or making judgement calls on complex loan terms. Instead, it is being used to handle the extraction work and augmentation of incomplete datapoints, comparing and reconciling prior trends and future expectations. This is a meaningful shift from mundane manual processing and month-end reconciliation chaos to elevated human engagement and expert validation
Critically, humans do not disappear from the process. Effective implementations pair AI capabilities with human-in-the-loop expertise, where specialists review exceptions, handle complex cases and work with industry participants to confirm data where confidence is lower. The result is a workflow that captures the efficiency gains of automation while elevating the accuracy standards that loan operations require. Governance and controls remain intact as AI accelerates the process.
For private credit managers evaluating where automation fits into their operating model, the most useful frame is not what AI replaces, but what it makes possible. It can help achieve more scalable processing, cleaner data that drives investment decisions and operations teams that can focus on value-add analysis and exception management throughout the loan lifecycle creating smoother month- and quarter-ends.
SS&C continues to successfully evolve this model within our Loan Solutions platform, applying AI-native workflows to address the data challenge of turning unstructured and incomplete loan data into actionable, integrated, and trusted fuel for investment management decisions.
Explore our solutions page to learn more.
