A lender can use AI on most of a loan file and leave approval with the credit team. The software reads documents, checks the details against outside sources, scores the file and routes it. The underwriter then decides, with a prepared file and the exceptions marked. Turnaround falls because the waiting shrinks, and the credit policy stays where it was.
Why the decision is the wrong place to start
A credit committee exists to exercise judgement. A proposal that hands that judgement to a model asks the committee to give up its job, and many committees would decline. A proposal that prepares the file and leaves the judgement alone asks for far less, and the same committee can approve it.
The split also matches where the hours go. A decision takes minutes. The file around it takes much longer.
A loan file in six stages
| Stage | What happens | AI role |
|---|---|---|
| 1. Intake | Applicant uploads salary slips, bank statements, KYC documents | Read each document and pull out the fields |
| 2. Completeness | Someone notices what is missing | Check at upload and ask for the missing item at once |
| 3. Verification | Bureau pull, KYC record, bank statement analysis | Run the checks and log each one |
| 4. Scoring | Apply the credit policy to the verified data | Compute the score from rules the credit team owns |
| 5. Routing | Choose an underwriter or clear the file | Send clean files forward, send exceptions to a person |
| 6. Decision | Approve, decline or ask for more | Stays with the underwriter |
Stages 1 to 5 are assembly. Stage 6 is judgement. The design automates the first and leaves the second.
What each part does
Document reading
Indian lending runs on paper that arrives in many shapes: salary slips from different employers, scanned bank statements, GST returns, identity proofs. A clean PDF is easy. A phone photo taken at an angle is the harder case, and it is the one customers send.
The software attaches a confidence score to each value it reads. A low score sends that value to a person, so a poor read cannot slip through. Set the threshold per document type, using real rejections, because a salary slip and a bank statement fail in different ways.
Checks against outside sources
The file is checked against credit bureau data (CIBIL, Experian, Equifax or CRIF), the central KYC record, the bank statement analysis, and the applicant's own declarations. Each check is a logged event with its time and result.
When the applicant's declaration and the documents disagree, the underwriter sees both figures next to each other.
A scorecard the credit team can read
Put thresholds and weights in configuration the credit team can open and change, with each change logged. A policy that only an engineer can read has left the credit function's hands.
Straight-through and exception lanes
A file that passes every check with high confidence moves straight to the underwriter's approval queue, with the evidence attached. Any file with a mismatch, a doubtful read or a policy exception goes to a person, and the message names the problem. The design of that lane decides whether it works, and the article on human-in-the-loop design covers it.
The audit record
Each file leaves a trail: the documents received, the values read and their confidence, every check and its result, the score, the cut-off in force, and the reviewer's name. With that record, an auditor can replay any decision months later.
The Reserve Bank's rules on outsourced IT work point the same way: a lender needs audit rights over any provider that touches its files. The clauses are set out in security and IP for offshore engineering teams. For what to record in a log, see the contents of an AI audit log.
Checks before go-live
- Bias on the scorecard. Test approval rates for groups that law protects and for fields that stand in for them. A pin code can quietly encode community or area.
- Reasons for a decline. The system must give the underwriter, and through them the applicant, a reason that a person can read.
- Where data is processed. Payment data has a storage rule of its own; see the security and IP article for the Reserve Bank's text.
- A test set built from your own files. Score the system on real past applications with known outcomes before it touches a live queue. See building an AI evaluation set.
The order of work
Start with document reading, because it is the largest block of effort and the easiest to measure. Add verification second. Add routing and straight-through handling last, once you have measured enough confidence to set the threshold from data instead of a guess.
Run the new path beside the old one for a period. Compare the files each path would have cleared, and review every difference with the credit team before you switch.
Questions buyers ask
Can AI approve loans on its own?
Technically yes. For a regulated lender, the sound first step is to automate preparation and leave approval with a named person. Authority moves later only if the credit committee decides it should, and only with the evidence the log provides.
Which part of origination gains the most from automation?
Document intake and verification. They are repetitive, they follow rules, and they sit before the decision. The decision itself changes least.
How do we measure whether it worked?
Record the baseline before you start: hours from application to decision, files per underwriter per day, and the share of files returned for missing documents. Measure the same three afterwards.
Where Brihat Infotech fits
Brihat Infotech built this pattern for a consumer-lending NBFC; the figures are on the NBFC loan origination case study. It builds AI agents and automated workflows with confidence routing, action limits and a full audit trail. For the wider picture, see the enterprise AI agents guide.