BFSI · Use Case
KYC Document Extraction: 95%+ Accuracy, Human-Checked.
Onboarding a customer means pulling structured data off passports, national IDs, proof-of-address documents, and incorporation papers, then keying it into your KYC system. A model fine-tuned on your own historical onboarding records does the extraction at 95%+ field accuracy. Anything it is not confident about is flagged for an analyst rather than guessed.
The Problem
Manual keying is slow, and it is where onboarding errors start.
Every new customer file is a stack of documents in different layouts, languages, and quality. Someone reads each one and types the fields into the onboarding system. It is slow at volume, it is monotonous, and transcription slips here surface later as KYC data-quality findings.
The verification decision, whether the identity checks out, is a separate step. This use case is about getting clean structured data into that step faster.
Extraction, not the KYC decision
The model reads fields off documents. It does not decide whether to onboard a customer or clear an alert. Those calls stay with your KYC and compliance team.
The Approach
A fine-tune on your own onboarding records.
We fine-tune a model on your historical ID and onboarding documents and the verified field values already captured against them. It learns the document types you actually see and the layouts your customers actually submit, which a generic OCR or document-AI service does not. RAG & LLM Fine-Tuning covers how the fine-tune and its evaluation are run.
Each extracted field comes with a confidence value. Fields above your threshold flow straight through; anything below it is queued for an analyst to confirm, so the 95%+ headline accuracy is a floor that human review lifts further.
Confidence-gated straight-through
You set the confidence threshold. High-confidence fields auto-populate the onboarding record; low-confidence fields wait for a person. No field is written from a guess.
Guardrails
The same delivery standard as every BFSI engagement.
Deployed in your cloud tenant on model weights you own. Identity documents used for fine-tuning never leave your environment. Every low-confidence field is checked by an analyst, and the model is red-teamed before production.
Built for examiner scrutiny
This is the boundary published sitewide for BFSI: full delivery on extraction, analytics, and decision-support, with human sign-off on anything that gates a customer relationship or a regulatory filing.
Who This Is For
High-volume onboarding teams.
Retail banks, fintechs, NBFCs, and payments or e-money firms onboarding customers at volume, where KYC data quality is examined and onboarding turnaround is a competitive number.
FAQ
Common questions about KYC document extraction.
Does the model decide whether to onboard a customer?
No. It extracts fields from documents. The verification and onboarding decisions stay with your KYC and compliance team.
How is 95%+ accuracy possible across so many document types?
The model is fine-tuned on your own historical onboarding records, so it learns the specific document types and layouts your customers submit rather than a generic global set.
What happens to fields the model is unsure about?
They are queued for an analyst to confirm. Only fields above your confidence threshold auto-populate the onboarding record. No field is written from a guess.
Where do the identity documents go?
Nowhere. Fine-tuning and extraction both run inside your cloud environment on model weights you own.
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