AI loan-review summaries: clarify the work the pilot will change

The prompt already names summarisation. You do not need to reopen every possible use of AI. You do need to confirm the boundary if the proposed workflow is unclear:

“Will the tool prepare document summaries only, with reviewers checking them and retaining all lending decisions?”

The interviewer confirms that it will. The tool is there to help reviewers understand the file. It is not being authorised to approve loans or change the lender’s credit policy.

That answer narrows your analysis to the usefulness of the summary, the time reviewers spend checking and correcting it, and whether it fits the existing process.

Ask:

“What improvement would justify taking the tool beyond the pilot, and how will we check that the summaries remain reliable?”

Suppose the target is to reduce average review time from twenty to twelve minutes per application, including verification. On comparable applications, the completed summaries must have no higher rate of material factual errors than the manual process.

Now there are two requirements. A summary that is quick to read but takes longer to verify has not delivered the time target. A faster process that omits important facts has not delivered the quality requirement.

Checking key statements and looking for omissions takes time. Include that verification work in the measured process.

“Accurate” is too broad to run a useful pilot. I would ask the lender’s reviewers to define which errors materially affect their work: a wrong financial period, an incorrect amount, an omitted obligation or an unsupported statement, for example.

Agree whether the case’s quality target refers to the draft summary or the summary after reviewer correction. The distinction matters. A raw summary may contain errors that reviewers successfully fix, but those fixes consume time. Conversely, a quick review may leave a material error in the completed output.

Track both draft errors and errors remaining after verification. Use the completed-output measure for the agreed target and the draft measure to understand the work needed to reach it.

The lender’s specialists should set the review criteria. You are structuring the business evaluation, not inventing a new lending standard.

To test the proposed success measure, the interviewer asks you to consider a hypothetical pilot result. The vendor’s demonstration takes seven minutes to read and use a summary, apparently saving thirteen minutes against the twenty-minute manual review.

The interviewer shares Exhibit 1: the complete reviewer workflow in this hypothetical pilot. It adds checking and correction to the vendor’s seven-minute figure:

The process saves four minutes per application, or 20%, rather than reaching the required eight-minute saving. It misses the twelve-minute target by four minutes.

For a scale check, assume 1,000 applications a month. Four minutes saved per application would release about 66.7 reviewer hours. That is capacity, not automatically a reduction in cash spending; the lender may use it to handle more applications or improve service.

If the decision also requires a financial return, include tool fees, integration, training and the actual use of the released time. Do not label every saved hour a payroll saving while staffing remains unchanged.

Application complexity can overwhelm the result if the AI-assisted group receives easy files while the manual group receives difficult ones. Use comparable document types and complexity, and distribute work so that individual reviewer speed does not determine the apparent benefit.

Record time spent on failed runs and applications that revert to the manual process. Excluding them would measure only the occasions when the tool works smoothly.

I would also inspect the distribution of review times. An attractive average can conceal a subset that regularly needs substantial rework. That may suggest a narrower pilot population rather than a company-wide rollout.

These are practical design choices that follow from the opening questions. They make the three-month pilot capable of answering the decision.

Your opening can now lead into a clear approach:

“I’ll assess a supervised summarisation pilot that keeps lending decisions with reviewers. We’ll compare total reviewer time, including checks and rework, against the twelve-minute target and test completed-summary errors on comparable applications.”

A pilot can be worthwhile even when the tool has not yet proved the final target. Its purpose is to resolve the uncertainty. Explain what you will measure, who judges the summaries and what result would justify expansion, revision or stopping.

Practise the AI-launch case-opening drill. Clarify the tool’s role before estimating benefits. For another example where the process boundary changes the economics, see AI customer support.