KYC and loan-file document AI for banks and NBFIs

Typical cut in KYC cycle time

Field accuracy on structured docs

Lower cost per verification (60-80% range)

The challenge

For banks, NBFIs and cooperatives, onboarding a customer or processing a loan file still means a person reading a stack of documents: citizenship papers, PAN, bank statements, salary slips. It is slow and expensive. Published figures put manual KYC review at $1,500 to $3,000 per client, with onboarding often taking five to ten business days, and around 70% of firms report losing customers because the process was too slow. In Nepal, strict NRB AML and KYC rules make the checks non-negotiable, so the answer is not to skip them but to do them faster and more consistently.

Our approach

Here is how we would build it. Documents come in as scans, photos and PDFs and are classified by type. A document-AI model reads each file and extracts the fields that matter (name, ID number, address, income) with a confidence score on every field. A verify layer cross-checks the data: do names match across documents, is the ID valid, do the figures agree, is anyone on a sanction or PEP list. Then the decision layer branches. Clean, high-confidence files pass straight through to approved. Anything low-confidence or flagged is routed to a compliance officer with the evidence attached. The officer decides; the model learns from the outcome.

Expected impact

Against published benchmarks, document AI in banking typically cuts KYC cycle time by around 70%, moving onboarding from days to under a day, and lowers cost per verification by 60 to 80%. Extraction accuracy reaches up to 99% on structured documents and 85 to 95% on messier ones, improving as reviewers correct edge cases. For a Nepali lender, that means faster approvals, fewer lost customers, and a clean audit trail that stands up to NRB scrutiny, without cutting a single compliance corner.

KYC and loan files are a paperwork mountain, and for a Nepali lender the checks are not optional. This blueprint shows how document AI can do the routine reading fast while a compliance officer keeps the final say on anything risky.

Clean files pass, risky files get a human

The value is in the branch at the end. High-confidence files are approved automatically, so people spend their time only on the files that genuinely need judgement, with the evidence already gathered for them.

Built with

Frequently asked

Is this a real bank case study?

No. It is a solution blueprint showing how we would approach the problem. The figures are cited industry benchmarks framed as achievable, not results delivered for a named client.

Does automating KYC create compliance risk?

Handled well, it lowers risk. The system enforces the same checks every time, screens against sanction and PEP lists, and logs every step for audit. Anything uncertain goes to a human officer rather than being approved automatically.

Can it work with Nepali documents and NRB rules?

Yes, that is the point of a tailored build. The model is trained on the document types a Nepali lender actually sees, and the verify and decision layers encode the specific AML and KYC checks the NRB requires.