Fine-tuning on reviewed work, with a person approving every release

How lenders can turn reviewer corrections into better models while keeping comparison, approval, rollback and training data inside their own walls.

In most lending operations, people already check the work AI produces. A reviewer opens an extracted bank statement, corrects a misread salary credit, fixes an employer name, confirms a date. That correction is recorded once, in the case file, and then forgotten. The next document of the same kind goes through the same model and meets the same mistake.

This is a waste of the most valuable thing an operations team produces: careful human judgement on real documents. The fix is not complicated in principle. Close the loop, so that checked work becomes training data, and govern the loop so that no new model reaches production without a person agreeing it is better.

Why reviewer corrections are the best training data

Generic models are trained on broad public data. They have seen few Indian bank statements, salary slips from regional employers, or the particular layouts a lender's channel partners send. Reviewer corrections fill exactly that gap.

They have three properties that are hard to get any other way:

They come from your documents. The formats, languages and quirks are the ones your staff actually face.
They are labelled by people who know the domain. A credit operations reviewer knows the difference between a salary credit and a reimbursement. A general annotation vendor may not.
They concentrate on errors. A correction exists because the model got something wrong. Training on corrections teaches the model where it is weak, not where it is already right.

Two cautions apply. Corrections must be captured in a structured way, the field, the original value and the corrected value, not as free-text notes. And confirmed values matter as much as corrected ones; a set made only of corrections gives a skewed picture of the work.

Compare before you release

A fine-tuned model is a candidate, not an upgrade. It may be better on the documents it was trained on and worse elsewhere. The only way to know is to test it on examples it has never seen.

A sound process:

Set aside a held-out set of reviewed examples that are never used for training. Refresh it over time so it reflects current documents.
Run both the current model and the candidate on that set.
Show the results side by side, field by field, so a reviewer can see where the candidate improved, where it regressed and where it changed nothing.
Pay particular attention to regressions on fields that matter most to credit decisions, even if the overall picture looks better.

Side-by-side comparison matters because summary scores hide detail. A candidate can look better overall while getting worse at reading account numbers, and that is a regression nobody in credit would accept.

A person approves, and the previous version stays ready

After comparison, a named person decides. Not the training job, not a threshold in a script. An approver looks at the comparison and accepts the candidate or rejects it, and that decision is recorded with their name and the evidence they saw.

Two further controls make this safe to run routinely:

Rollback. The previous model stays deployable. If the new one behaves badly on live work, operations can return to the earlier version quickly and without retraining.
Separate roles. The person who trains a model should not be the one who approves it, and approving should be separate from deploying. This is ordinary maker-checker discipline, applied to models.

RBI's FREE-AI committee report points in the same direction. It recommends that regulated entities "implement robust model governance mechanisms covering the entire AI model lifecycle", including human oversight for medium and high-risk uses, and that they "establish fallback mechanisms and periodically test the fallback workflows". It also recommends a comprehensive internal AI inventory covering all models. A recorded approval, a ready rollback and a list of which model version ran when are practical ways to work towards that guidance.

Keep training inside your walls

Reviewed documents contain personal and financial data: names, account numbers, salaries, sometimes masked identity numbers. Sending them to an outside service to train a model raises obvious questions about where the data goes, who can see it and whether it is retained.

The simpler answer is to train where the data already lives. Open-weight models can be fine-tuned on the lender's own GPUs, on-premise or in the lender's own cloud account. The training data, the candidate models and the evaluation results then stay under the lender's control, subject to its existing security and retention policies.

This also makes the model itself an asset of the lender. It is not a configuration on someone else's platform that can change without notice.

How the pieces fit

In Inferova, corrections from the Parse review queue become training data in LLM Studio. A new model is compared against the current one on held-out examples, shown side by side, and served only after an approver accepts it, with the previous version kept ready for rollback and separate roles for training, approval and deployment. Approved models run on the lender's own GPUs.

Whatever tooling you use, the questions are the same:

Are reviewer corrections captured as structured data?
Is there a held-out set the model never trains on?
Can an approver see the old and new models side by side before deciding?
Who can train, who can approve, and who can deploy, and are they different people?
How quickly can you return to the previous version?
Where do the training data and the models physically sit?

If you would like to see this loop run on sample documents, you can request beta access at inferova.com/demo.