AI Agents for Credit Memo Preparation and Risk Summarization

Commercial credit decisions are made in committee, but they are made on the memo. The paper that reaches the table has to state the borrower and the group it belongs to, the facility and its structure, spread financials with the adjustments explained, covenant headroom, collateral and its valuation basis, industry conditions, the rating rationale, the mitigants relied on, and every exception to policy the deal needs.

Almost none of that is judgement. It is retrieval, transcription, cross-checking and formatting, pulled from the spreading system, the core banking platform, the CRM, the document repository, filed accounts, valuation reports and last year’s file. Analysts spend the bulk of a credit review assembling that evidence and a minority of it thinking about the risk — which is exactly the wrong ratio, and exactly the ratio an agent can change.

The design constraint is equally clear: the agent drafts the memo, and it never forms the credit opinion.

Draw the line before you draw the architecture

The single decision that determines whether this project is a documentation improvement or a regulatory problem is where you put the boundary.

On the safe side of the line: locating documents, extracting figures with their source location, spreading comparisons across periods, restating the facility structure, summarising covenant terms as written, listing collateral with valuation dates, drafting descriptive sections, and flagging what is missing or inconsistent.

On the other side: assigning or suggesting the risk rating, estimating default likelihood, recommending approval or decline, or sizing a limit. Those are quantitative estimates used in a business decision, and in the US they bring the work inside model risk management expectations — now set out in SR 26-2, the revised guidance issued in April 2026 that superseded SR 11-7 and SR 21-8. Banks already run validated rating models under that regime. An agent that quietly produces a competing opinion in prose has created an unvalidated model with a friendly interface.

The useful framing for the credit committee is that the agent is responsible for everything the memo asserts as fact, and the analyst remains responsible for everything the memo concludes. That division also maps cleanly onto the documentation expectations in the EBA’s guidelines on loan origination and monitoring, which are specific about the information a creditworthiness assessment must be based on and how it is recorded.

The memo template is the schema

Credit memo formats are already prescribed by credit policy, which is unusually helpful: the agent does not need to invent a structure, it needs to fill a known one and prove each field.

Treat the bank’s own template as a typed schema rather than a prose target. Each section becomes a set of fields, and each field carries three things: the value, the source location it came from, and a status — evidenced, derived, or missing. A derived figure (“EBITDA adjusted for the one-off disposal”) carries the derivation as well as the inputs.

That structure is what makes review fast. A credit officer reading a conventional AI draft has to fact-check prose. A credit officer reading a field-level draft can see that seventeen fields are evidenced with links into the filed accounts, four are derived with visible arithmetic, and three are flagged missing. Review then concentrates where the uncertainty is, which is the whole point of the exercise.

It also makes the output auditable in the sense a reviewer actually needs. This is the same decision-receipt discipline that governed agent workflows use elsewhere: an answer without its evidence trail is an opinion.

What the agent has to reach, and under whose permissions

A credit memo is a multi-system artefact, so the agent needs governed reach across all of them — and the access model matters more than the coverage.

• Structured systems of record — spreading, limits, exposures, collateral registers, arrears history. These belong behind scoped, read-only tools over the enterprise database, not behind free-text retrieval, because the questions are aggregate questions.
• Document repositories — facility agreements, security documents, valuation reports, board packs, covenant certificates. These belong in permission-aware private RAG, where the customer-level entitlements that already govern the credit file are enforced at retrieval time.
• External filings and market material, where the institution subscribes to it, kept clearly separated from internal sources so provenance stays legible in the memo.

Every one of these runs under the requesting analyst’s entitlements. Credit files are among the most tightly walled data in a bank — deal teams, sectors, related parties, wall-crossed information — and an agent that reads with a service account has broken a control that took years to build. The least-privilege tool pattern applies without modification here.

For most institutions this is also the argument for running the work on private infrastructure. A credit memo contains the borrower’s unpublished financials, the bank’s internal view of their risk, and the pricing it is prepared to offer. That combination is not a candidate for an external inference endpoint, which is the general case for on-premises AI in financial services.

The five failure modes worth designing against

1. Narrative carry-forward. Prior memos are the most fluent source in the file and the most dangerous. Use them for structure; exclude them as evidence. A mitigant that was real in 2024 is an assertion in 2026 until re-evidenced.
2. Obligor and group confusion. Mid-market groups have subsidiaries with near-identical names, shared directors and intercompany guarantees. Resolve the entity from the identifier in the system of record, never from the name in a document, and state the group tree in the memo.
3. Period and basis mismatch. Management accounts against audited, local GAAP against IFRS, pre- against post-adjustment, reporting currency against local. Every figure needs its basis attached or the comparison is fiction — the same failure mode private RAG hits on tables and spreadsheets.
4. Smoothing the negatives. Fluent summarisation dilutes bad news: a breach becomes “a covenant matter”, a legal claim becomes “an ongoing discussion”. Constrain the covenant and arrears sections to deterministic extraction and status flags rather than free narrative.
5. Silent interpolation. A missing figure that gets estimated to keep the table tidy is the defect that survives review, because it looks exactly like the figures around it. Missing must render as missing.

Rollout that earns the committee’s trust

Start with annual reviews and renewals of performing exposures rather than new-money transactions. The file already exists, the counterfactual is known, and the failure cost of a weak draft is an analyst’s correction rather than a mispriced deal.

Run the agent in parallel for a defined period: it drafts, the analyst writes the memo as they always did, and you compare. The measures that matter are field-level citation coverage, the rework rate on evidenced fields, whether the gap register caught the things the analyst had to chase anyway, and how often committee returned a paper for missing information. Those numbers are also what a validation function will ask for.

Then keep the material actions gated. Circulating a memo to committee, updating a rating, or writing anything back to the limits system belongs behind an explicit human approval step, with the approver, the moment and the version recorded. The agent’s job is to give a credit officer a defensible draft an hour after the request instead of a week — with every number traceable to the system it came from, and every gap named rather than filled.