The case for human-in-the-loop statement review

There are two failure modes in bank statement review. One is manual: a person reads every page, transcribes every figure, and eventually makes a mistake because that's what happens when humans do repetitive transcription for hours. The other is automated: a model reads every page and makes decisions a lending team never actually reviewed, because nobody has time to check work they didn't do.

Human-in-the-loop review is the position between those two failure modes, and it's a position with an actual shape, not just a compromise. The model does extraction — the part it's reliably good at, and the part that's tedious and error-prone for a person to do by hand. The assessor does judgment — the part that requires context the model doesn't have: what's normal for this customer, what's worth flagging, what a lending decision actually needs to defend.

The important detail is where the boundary sits. Extraction should carry a confidence score, not a silent guess. Corrections should be logged with a reason, not just a new value. And nothing should reach a lending decision that a person hasn't actually seen — not approved in the abstract, but seen, on this statement, for this customer.

That's not a slower version of automation. It's a different product entirely — one where the AI's job is to make a human's judgment faster and better-informed, not to replace the judgment itself.