Should AI Write Your Credit Memos?

Yes for the data sections, no for the risk narrative and the recommendation. AI is very good at the mechanical two thirds of a commercial credit memo: spreading the financials, computing ratios, building the debt schedule, summarizing bank activity, and describing what the numbers show. It is unreliable at the third that matters most in an exam, which is judging whether a risk is mitigated and whether the deal should be approved. The practical split is to automate everything that can be traced to a source document and keep a named credit officer accountable for everything that cannot.

That answer is not a hedge. It maps onto a real line inside the memo, and once you see where the line falls the buying decision gets a lot simpler.

Can AI write a credit memo?

Technically, yes, and several vendors sell exactly that. Feed a model the tax returns, the interim financials and the loan request, and it will return something that reads like a credit memo: an executive summary, a borrower description, a financial analysis section, a list of risks with mitigants, and a recommendation. It takes minutes instead of hours, and the prose is usually cleaner than what a tired analyst produces at 6pm.

The problem is that a credit memo is not judged on whether it reads well. It is judged on whether every assertion in it is true and supported, because someone is going to test that. A committee member will question a coverage figure. Loan review will pull the file in a year. An examiner will ask why a policy exception was granted. Generated prose is fluent by construction, which means an unsupported claim in it looks exactly like a supported one. That is the whole risk in a sentence.

What part of the credit memo is safe to automate?

The reliable test is whether the output can be checked against a source document in under a minute. If it can, automate it. If checking it requires re-deriving someone's judgment, do not.

Look at what falls on the yes side. It is most of the hours. Published estimates and the analysts we talk to put a full commercial write-up at four to eight hours, and the overwhelming majority of that is assembling and verifying data, not composing the argument. Automating only the safe two thirds still takes the biggest bite out of the week, which is why the honest version of this technology is worth buying even with the limits stated plainly.

Where AI-generated credit memos go wrong

Three failure modes come up repeatedly, and none of them are exotic.

The confident mitigant. The model identifies a genuine risk, customer concentration at 40 percent of revenue, and then writes that it is mitigated by a long-standing relationship and diversification efforts underway. Neither claim came from a document. It came from the pattern of how credit memos usually read. A reviewer skimming for structure will pass it, because structurally it is a correct memo.

The number with no parent. A generated memo states EBITDA of $1.42 million. Where did it come from? If the tool cannot point at the page and the line, you now own a figure you cannot defend, and the only way to defend it is to redo the spread yourself, which was the work you were trying to avoid.

The silent policy drift. Add-backs are the classic case. Whether owner compensation, one-time legal costs or a related-party rent adjustment get added back is a credit policy question with a right answer at your institution and a different right answer down the street. A general purpose model will apply the most common convention, and it will do it consistently and invisibly across every file until loan review notices that the portfolio's coverage ratios have quietly loosened. Our guide to calculating add-backs in business cash flow covers where the judgment actually sits.

What do examiners think about AI-drafted credit memos?

There is no rule that forbids it, and there is also no rule that shelters you. US banking supervisors have been consistent for years that a bank owns the outcomes of the models and the third parties it uses, that model risk has to be managed and documented, and that outsourcing an activity does not outsource responsibility for it. Applied to a credit memo, that means the questions in an exam will be the ones you would expect: who reviewed this output, what did they check, how do you know the figures are right, and what happens when the tool is wrong.

A credit department that can answer those questions with a documented review step and source citations behind the numbers is in a defensible position. One that cannot show which parts of the memo a human actually verified is in a much worse position than if it had never automated anything, because the volume of unverified work is now much larger.

Does AI replace the credit analyst?

No, and the vendors claiming it does are selling you a governance problem with a subscription attached. What changes is the shape of the job. An analyst currently spends most of a deal keying returns into a spread template and hunting for support pages, and a small slice of it thinking about whether the repayment source is real. Automating the first part does not eliminate the analyst, it moves them into the part of the work that a committee, a regulator and a loan loss provision all actually care about. Departments that adopt this well usually end up doing more deals with the same headcount and spending more time per deal on the risk, not fewer people doing the same deals faster.

How do you pilot credit memo automation without betting the department?

Run it in parallel on files you have already decided. Pick ten closed deals across your normal mix, one renewal, one multi-entity borrower, one messy sole proprietor, and put them through the tool as if they were live. Then compare, in this order:

1. Spread accuracy line by line against the analyst's original. Not the ratios, the underlying lines. Ratios can agree by accident.
2. Traceability. Pick fifteen figures at random and try to get from each one to the source page. Time it. This is the number that predicts how the tool behaves in an exam.
3. Add-back and normalization treatment against your written credit policy, not against what the analyst did, because the analyst may also have drifted.
4. What it does with a bad file. Feed it a scanned, skewed, partially handwritten set of statements. Every vendor demo uses clean documents. Your pipeline does not.
5. Total time to a committee-ready package, including your review step. If review takes as long as writing did, the tool has moved the work rather than removed it.

Two practical notes from files that go sideways in pilots. Small borrowers frequently hand over a bookkeeping export rather than prepared financial statements, and it is worth turning that export into a proper P&L and balance sheet before anything tries to spread it, because garbage in this step propagates through every ratio downstream. And borrowers with multiple related entities will break naive tools completely, which is why the global cash flow analysis test belongs in every pilot.

So should you let AI write your credit memos?

Let it write the parts you can check. Automate the spread, the ratios, the coverage math, the debt schedule and the bank statement analysis, insist that every extracted figure stays linked to the page it came from, and put that into the memo template your committee already reads. Then write the risk narrative and the recommendation yourself, because that is the part you will be asked to defend and the part that is genuinely credit work.

That split is deliberately how credit memo automation software is built here: LenderAnalyzer produces the analysis and the citations and stops before the narrative. If what you want instead is a finished document with the prose already drafted, that is a different category of product and there are vendors who do it well. Just make sure you know which one you are buying, and that whoever signs the memo has read every number in it.

Related reading: what to include in a commercial loan credit memo, how to spread a financial statement, and how banks assign a credit risk rating.