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Work · Banking & NBFIs · Nepal
KYC and loan-file document AI for banks and NBFIs
A blueprint for a document-AI system that reads KYC and loan files, verifies them, and auto-approves the clean ones while routing risky files to a compliance officer.
blueprint04 Jun 2026
70%
Typical cut in KYC cycle time
99%
Field accuracy on structured docs
-60%
Lower cost per verification (60-80% range)
This is a solution blueprint — a reference architecture we can build for your business. The figures above are cited industry benchmarks for this class of system, not results claimed for a named client.
The pipeline we'd build
4 stages
Stage 01 · Upload
Documents arrive as scans, photos and PDFs, and are classified by type.
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.
What we did
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.
The outcome
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.
Stack
Next.jsOCR / Document AIPythonRules engineSanction/PEP screeningPostgreSQLCloud (AWS or GCP)
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.
KYC document-AI, layer by layer
How a loan file moves from a stack of scans to an approval. Tap a layer.
OCR & extract
A document-AI model reads each file and pulls the fields that matter (name, ID number, address, income), with a confidence score on every field.
OCR · layout model · field extraction
Clean file
Passes checks with high confidence, approved automatically.
Flagged file
Routed to an officer with the evidence, decided by a person.
Human-in-the-loop by design: the model handles the routine files so people spend their time on the genuinely risky ones.
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.
At a glance
Client
Solution blueprint
Sector
Banking & NBFIs · Nepal
Service
Neural AI
Kind
blueprint
Headline result
70% · Typical cut in KYC cycle time
Handover
Documented, tested code in your repository
Questions we were 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.
Same problem, different business?
We'll send the architecture and a realistic timeline for your version of this — no obligation.