Every credit desk now has a general-purpose AI assistant. The open question is whether its output can hold up to scrutiny.
Credit work needs output that is:
Repeatable. Same certificate, same answer, from any analyst, every period.
Auditable. Every figure traces back to its source.
Defensible. You can explain it to an examiner, an auditor or a borrower.
Prompting a chatbot gives you none of the three. Building AI into the workflow can give you all of them.
Our new research note sets out the evidence for both approaches. It draws on field experiments, enterprise surveys, technical studies of model behavior and U.S. supervisory guidance, and applies them to portfolio monitoring in asset-based and private credit lending, where the same borrowing base certificates, covenant packages and financial reports arrive every period across hundreds of borrowers.
The note closes with a decision framework for matching the mode to the task, and the questions to ask a vendor before buying.
Twenty-one pages. May be shared with attribution.
Trust, in a lending operation, is built on repeatability. That is what embedding is for.