Better Than the Average Analyst, at Scale: How AI is Redefining Deal Structuring

Tailored Credit

With the analytical framework established, the focus shifts to architecture. Deal structuring in private credit is where underwriting judgment translates into financial product design — and it is one of the most consequential points in the credit lifecycle. Get the structure wrong and even a sound credit assessment produces a poor outcome. Get it right and the alignment of terms, covenants and capital structure becomes a source of competitive advantage in its own right.

AI is changing what is achievable at this stage, not by replacing human judgment, but by dramatically expanding the range and rigour of the inputs that judgment can draw upon.

Where AI has a Genuine Edge

In deal structuring, agentic AI models' comprehensive forecasts are central to creating financial models that map the possibility space and enabling analysts to structure a loan that accurately prices credit risk. AI agents can interpret and structure qualitative data at scale, management commentary, market signals, covenant benchmarks across comparable transactions — and then test whether those assessments correlate with realised outcomes.

This back-testing capability is particularly valuable in private credit, where the absence of liquid market pricing means that structural decisions cannot be easily corrected after closing. The ability to run hundreds of structuring scenarios, stress-test them against historical analogues and validate their internal consistency before a deal reaches investment committee is a material improvement on the efficiency-focused use cases for AI prioritised by most asset managers today.

"AI is leveling the playing field. Optimising deal structure from an art to a science."

— Oron Maymon, Co-Founder & Chief Science Officer, Liquidity.

Resolving the Structuring Dilemma

Credit structuring has historically required navigating a fundamental tension: how to protect investor returns through robust covenants while preserving enough borrower flexibility to support the operational performance that services the debt. There is no set formula for resolving this dilemma, but AI agents can augment human capabilities and work towards addressing it.

AI systems that operate continuously within the parameters established during the analytical phase can run structuring scenarios against this dilemma in near real time, testing covenant design, repayment profiles and capital structures against a distribution of possible outcomes rather than a single base case. The credit professional receives not a single recommended structure, but a structured view of the trade-offs — enabling faster, more defensible decisions when presenting before an investment committee.

The leading institutions in private credit have already recognised this. Structuring deals to enable AI-driven value creation is now identified as a critical success factor across the investment lifecycle. The tools exist, the question is, who is using them?

‍

References

3 — Better Than the Average Analyst, at Scale: How AI is Redefining Deal Structuring

• [1] Dong, Gang Nathan, Can AI Replace Stock Analysts? Evidence from Deep Learning Financial Statements (2025)
• [2] Neuberger Berman (2025)
• [3] BCG (2025)

‍