AI has the potential to improve how private credit loans are valued, but adoption among managers remains limited, despite strong interest.
A recent report from Apex Group shows strong demand from private credit firms for more frequent and responsive pricing for internal use, with respondents citing liquidity insights, reporting accuracy, and risk monitoring as potential benefits.
Having access to, and being able to rapidly process, large volumes of financial data using AI would mean that private credit managers can significantly improve how they evaluate loans, said Eddie Kelly, global head of product - private debt at Apex Group.
“This means firms can make quicker decisions and value investments with greater accuracy,” he said. “What would have taken weeks, if not months, would be done in days. It changes the way these firms will operate to drive growth and deliver returns for their investors.”
However, despite the enthusiasm for these benefits, only 4 percent of survey respondents are currently implementing some form of AI-driven automated valuation models.
By comparison, AI is being used more widely in other areas of private credit operations. For example, 26 percent of respondents are using AI to pull data from loan and credit documentation, and 21 percent are using it for financial statement processing.
In practice, valuation still relies on third-party pricing services, broker quotes, and mark-to-model approaches.
“The valuation of private credit has always been a massive headache,” said Kelly. “We still have a lot of clients that are manually valuing loans using valuation committees.”
The report stated that implementation remains limited because the operational and data infrastructure required to support automated valuation is still developing. Respondents remain cautious about relying on automated outputs unless the models and the underlying data can be clearly understood, audited, and governed.
Automated valuation requires continuous, structured, and auditable data flows, yet many firms operate with semi-automated extraction processes that combine manual inputs with AI-assisted tools, the firm said.
Where underlying data architecture remains incomplete or fragmented, it becomes difficult to run valuation models reliably or frequently enough to support near real-time pricing.
