The AI-Ready Credit Memo Template

1. A Credit Memo Is a Template Problem

A credit memo looks different on every deal and is the same underneath. Borrower, market, financials, structure, covenants, risks, a recommendation. Same skeleton, same order, deal after deal.

That sameness is not a weakness. It is the opening. A document with a fixed shape is a template, and a template is the one kind of writing AI drafts well, because you are not asking it to invent a form, only to fill one you already trust.

This guide is that template. Not a description of one, the thing itself, section by section. For each section: what belongs in it, what a strong version looks like, what AI can draft from the data room, and what you have to own and verify yourself.

The promise is narrow and worth saying plainly. A good template plus AI gets you a strong first draft fast. The credit call, the risk framing, and the conviction behind the recommendation stay human, on every deal, without exception. The mechanics of the drafting tool itself, the setup and where it breaks, sit in Claude for credit memos. This guide is about the memo.

2. The Template at a Glance

Before the section-by-section detail, here is the whole memo on one page. Ten sections, what each holds, what an AI draft pulls from, and the part that stays yours.

Read the table as a division of labor. The middle column is what a model assembles from the file in minutes. The right column is why a credit professional still spends the afternoon on it. The rest of this guide takes each row and says what good looks like. For the same map across deal and portfolio memos rather than credit alone, the IC memo and board pack guide covers the wider pattern.

3. Recommendation and Executive Summary

The recommendation is the first page the committee reads and the last page you should write. It carries the ask: the borrower, the facility, the amount, the pricing, the structure, and a clear recommend or decline, with the two or three reasons that decide it.

Good looks like this. A member who reads only this page knows what they are voting on and why. Nothing is hedged, and the real risk is not buried on page nine. If the deal turns on one thing, that one thing is named here, in plain words.

AI drafts a summary well, and it drafts this one best of all, because a summary is compression and compression is a model's strongest move. Once the sections beneath it exist, point the model at the full memo and it returns a tight recommendation page in your house phrasing, ready to sharpen.

What you own is the whole of it. The recommendation is a judgment with real downside, and it belongs to a person who will argue it in the room and answer for it after. A model can assemble the words. It cannot hold the conviction, and it cannot be accountable when a regulator or an LP asks why you made the loan. Draft with AI, decide as a human, and never let the file read as if the model made the call.

4. Borrower and Business Overview

This is the section a committee member reads to picture the company. What it does, how it makes money, who owns it, who runs it, and how it got here. Products, customers, contracts, and the sponsor thesis if there is one.

Good looks like specific. Not a technology company serving diverse end markets, but a maker of two products sold to four customers, one of which is a third of revenue. Concentrations, key-person exposure, and customer churn belong here, not softened, because the overview is where the shape of the risk first shows.

AI drafts this straight from the data room. Feed it the CIM, the management deck, and the company materials, and it returns a clean overview in your order and your format. It reads a long CIM faster than any analyst, and it does not skim the appendix where the concentration is disclosed.

What you own is what the pitch left out. A CIM is a sales document, and a model summarizing it carries its optimism unless a human pushes back. The overview a credit team trusts is the one where an underwriter has already asked what the seller would rather not say.

5. Industry and Market

This section sets the weather the borrower operates in. Sector demand and its drivers, cyclicality, the competitive set, regulation, and where this company sits within it. The point is not a term paper on the industry. It is the handful of forces that decide whether the cash flow you are lending against holds through the life of the loan.

Good looks like a clear view on the cycle. Is this demand structural or late-cycle. What happens to the borrower in a downturn that has happened before in this sector. A market section that only cites growth and tailwinds has not done its job.

AI drafts a solid first pass from the CIM and from outside research, and it is genuinely useful for the gathering: pulling sector reports, sizing the market, and laying out competitors in minutes. The tools that gather that outside material vary in reach and reliability, and the landscape is in the best AI tools for private credit.

What you own is the cycle read. A model reflects the consensus in its sources, and the consensus is usually most confident at the top. The judgment about where this sector sits, and what a real downturn does to this borrower, is exactly the call the memo exists to record, and it stays with the credit team.

6. Financial Analysis and Spreading

This is the heart of the memo. The revenue and EBITDA story across periods, the margins and their direction, the leverage and coverage that size the credit, working capital, and free cash flow against debt service. Everything the recommendation stands on runs through here.

Good looks like a narrative tied to numbers a committee can trust. Not EBITDA grew nicely, but EBITDA of a stated figure, up a stated amount, with the add-backs listed and each one judged. Adjusted and reported are both shown. The quality of earnings is addressed, not assumed.

AI drafts the narrative around a spread you have already built. It turns the periods into prose, describes the trend, and frames leverage and coverage in your house language. The extraction and spreading that feed this, reading financials into a usable model, are covered in AI credit underwriting.

What you own is every number. A language model can produce a clean, confident, wrong figure, because it is writing prose, not running the calculation. So leverage, coverage, and every metric that drives the credit gets reconciled to the spread or the source before the memo moves. Keep borrower financials on an account your firm controls, Claude Team or Enterprise rather than a consumer plan, so the numbers are not feeding a public model. A wrong leverage figure is not a typo in a credit memo. It is a mispriced loan.

7. Transaction, Structure, and Sources and Uses

Two sections that belong together, because they answer the same question from two sides: what is the money for, and how is it put in. The transaction and structure section carries the purpose, the facility, the amount and pricing, the security, and where you rank in the capital structure. Sources and uses is the arithmetic beneath it: where every dollar comes from and where it goes.

Good looks like a structure a reader can see protecting them. The security package is specific, the ranking is clear, and the equity contribution is real cash, not rolled paper dressed up as equity. Sources and uses ties out to the dollar, and the equity check is what the sponsor claims it is.

AI drafts both from the term sheet and the model. It lays out the facility and the uses in your format, builds the sources-and-uses table, and it will flag when the two sides do not reconcile if you ask it to.

What you own is whether the structure actually holds. The security, the ranking, the equity cushion, and the intercreditor position are the difference between a recovery and a workout that goes nowhere. That is a credit judgment, and it does not move to the model.

8. The Covenant Package

The covenants are where a credit protects itself after close. This section lists the financial covenants, the levels, the headroom against the base case, and, just as important, the definitions that decide whether a level is ever actually tripped.

Good looks like headroom you can trust and definitions you have read. A leverage covenant set with a wide cushion and an EBITDA definition full of uncapped add-backs is not protection, it is theater. The strong version states the headroom, stress-tests it against a downside case, and calls out the definition that could let the borrower add back its way out of a breach.

AI drafts the covenant section from the term sheet and the draft credit agreement, laying out each covenant, its level, and the headroom against your model. It is good at pulling the definitions into one place where a human can actually compare them.

What you own is whether the headroom is real. The gap between a covenant that binds and one that never triggers lives in the definitions, and reading them is a lawyerly, adversarial task that a model assists but does not finish. Verify the headroom against a downside, not just the base case, before you rely on it.

9. Risks and Mitigants

This is the section a credit committee reads hardest. The risks, sorted by category, business, financial, structural, management, and market, each paired with the mitigant that makes the deal bankable anyway, or, honestly, does not.

Good looks like a real ranking. A model tends to state every risk in the same flat, even tone, and a strong risk section refuses to. One risk is usually the whole deal, and the memo says so. Each mitigant is tested, not asserted, and the residual risk that survives the mitigant is named.

AI drafts a thorough first pass, and thoroughness is its gift here. It surfaces risks from across the diligence and pairs each with a candidate mitigant, so nothing obvious is missed. It is a strong checklist that starts from the whole file rather than from what the analyst happened to remember at midnight.

What you own is severity and emphasis. Which risk is the deal, which mitigant actually holds, and how hard to press: those are the judgments the committee is paying the underwriter for. The model flattens; the credit professional ranks. That is the line the whole memo is built on.

10. ESG, Other, and the Appendices

The last stretch of the memo is the part that is easy to rush and expensive to skip. ESG and other items: sanctions and KYC screening, material ESG flags, legal and litigation, insurance, and anything that does not fit the earlier sections but a committee needs to see. Then the appendices: the spreads, the comps, the org chart, and the source list behind the memo.

Good looks like a clear escalation of what matters and an honest record of what does not. Not a wall of boilerplate, but the two or three items a committee should actually weigh, flagged plainly, with the rest documented for the file. The appendices are complete enough that anyone can trace a number in the memo back to where it came from.

AI drafts the screening summaries and assembles the appendices well, and this is where an agentic mode earns its place, pulling the diligence reports into a consistent summary and building the source list as it goes. The productized version of that assembly, wired into your template and your committee process, is investment committee memo automation.

What you own is materiality. A screening tool flags a hit, but only a person decides whether it is a false positive or the reason to walk. And the audit trail in the appendices is what lets you answer, months later, why the memo said what it said. That answer has to be a documented human rationale, not the model recommended it.

11. Where to Start

Adopt the template first, before any tool. Write your ten sections down in this order, with a house standard for what good looks like in each, and you have something an analyst and a model can both work inside. Most firms discover their real template was never written down, only carried in a few senior heads.

Then run your next live deal through it. Draft each section from the data room, verify every figure against the source, and keep the recommendation and the risk ranking human. Do that across three or four deals and you will know exactly what the draft saves and where it needs a hand, from evidence rather than a pitch. The step-by-step of the drafting itself is in how to automate the credit memo with AI, and the wider arc for a credit shop is in the complete guide to AI in private credit.

If you want the template built around your firm's format and credit box rather than a generic one, that is what an AI Readiness Sprint produces: your sections, your standards, and the memo workflow proven on real deals in one to two weeks. A good template plus AI gets you a strong first draft fast. The credit call stays yours.