AccountingBench: Evaluating LLMs on real long-horizon business tasks

Hey all, member of the benchmark team here! The goal for this project was to see how LLMs well could do bookkeeping without an overly opinionated scaffold. We gave them access to processed transaction records and code execution tools, but it was up to them to choose exactly how to use those.

Claude and Grok 4 did reasonably well (within CPA baselines) for the first few months, but tended to degrade as more data came in. Interestingly, the failures aren’t exclusively a context length problem, as we reset the context monthly (with past decisions, accruals/deferrals, and comments available via tool calls) and the types of errors appear to be more reward hacking vs pure hallucinations.

Accounting is very interesting in an RL-first world as it is pretty easy to develop intermediate rewards for training models. We are pretty sure that we can juice the performance more with a far more rigid scaffold, but that’s less relevant from a capabilities research perspective. We’re pushing down this research direction and will see how it goes.

Let us know if you have any questions!

I love the site design.

> There's an obvious question looming here — if the models got so confused, how did they consistently pass the reconciliation checks we described above? It may seem like the ability to make forward progress is a good proxy for task understanding and skill, but this isn't necessarily the case. There are ways to hack the validation check – inventing false transactions or pulling in unrelated ones to make the numbers add up.

This is hilarious. I wonder if someone is unintentionally committing fraud by blindly trusting LLMs with accounting. Or even worse, I bet that some governments are already trying to use LLMs to make accounting validators. My government sure wants to shove LLMs into digital government services.

Reading through the LLM log entries, it's just astounding the amount of depth current models are capable of. It's almost hard to comprehend that this is even possible. Yeah the current ones mess up after a while, but ... the future is going to be very interesting.

I sent this to accounting friends and this aligns with what I've been going through trying to use LLMs to create a game from scratch. Seems like the current best use case for language models (even with agent mode) is to feed it exactly what you want to get out, essentially turning it into a better auto complete. Still saves tons of time, but it isn't a panacea.

> Ledger balances are calculated by summing all transactions per account. The differences should be as close to zero as possible, with small differences allowed for pending transactions such as weekly Stripe payouts.

That's not quite right. I'm not an accountant, but pending transactions (posted, but not cleared) should be factored into the balance of account, or at least the "available balance" - which is more important the the "current balance".

The idea that you can "allow" accounting discrepancies as "those are probably pending" is wild.

We've been on this train of not caring about the details for so long but AI just amps it up. Non-deterministic software working on things that have extremely precise requirements is going to have a bad outcome

A company may be OK with an AI chatbot being so bad it results in 5-20% of customers getting pissed off and not having a 5-star experience. The SEC and DOJ (and shareholders) are not going to be happy when the books are off by 20% or when a bridge is 5 inches too short to reach the other side

> Needless to say, a human accountant would never behave in these ways. In fact, we explicitly prompt against this behavior in no uncertain terms, but the instructions – and the entire spirit of the task – are lost in the interest of making forward progress. Claude and Grok keep trying until they find some way to get past the checks, even if it explicitly violates their instructions and the core goal.

I recently read a similar thing here on HN. There the model was making commits with some problem like tests failing, then the human added a pre-commit hook, then the model started editing the hook to make forward progress, then the hook was made read-only, then the model was trying to make it writeable...

To me it feels like the model clearly does not have an understanding of what is happening, what the goal is and if it is really making progress towards the goal. And this lack of understanding is an actual problem. You can paper over it for a short while, but as here and in the other article, over a longer experiment it results in failure.

Remember that test where you ask a LLM whether 9.11 or 9.9 is the bigger number? [Just checked gpt-4o still gets it wrong]

I don't think you'll find many sane CFOs willing to send the resulting numbers to the IRS based on that. That's just asking to get nailed for tax fraud.

It is coming for the very bottom end of bookkeeping work quite soon though, especially for first draft. There are a lot of people doing stuff like expense classification. And if you give an LLM an invoice it can likely figure out whether it's stationary or rent with high accuracy. OCR and text classification is easier for LLMs than numbers. Things like concur can basically do this already.

But can't it, literally, hallucinate raw data at any point in the run?

An LLM is like a jackhammer, it works very well when you hold it tightly. If you let it loose it will sort of work for a while then it starts destroying everything around it.

A serious problem for many accounting start ups who so far faked it till it will work. In other words, they still need to do more manual labor than they thought. They will never be profitable and it will take years, if ever, until AI will substitute the local accountant.