Document Processing Automation
Automated KYC, statement parsing, and loan pre-qualification — cut manual review workload dramatically while improving accuracy.
Client snapshot
The challenge
A team of 12 analysts was manually reviewing PAN, Aadhaar, bank statements, GST returns, and ITRs for every loan application — averaging 40 minutes per file.
Human fatigue caused errors — missed signatures, wrong amounts carried forward, fraud cases slipping through.
The NBFC wanted to scale loan volume 5× but couldn't hire analysts fast enough without drowning in quality issues.
Our approach
- 01
Document-class pipeline
Built separate extraction models for each document type — PAN, Aadhaar (with masking), bank statements (12+ bank formats), GST, ITR. Each has its own validation rules.
- 02
Structured extraction + validation
Every extracted field is cross-checked: PAN format, Aadhaar checksum, bank statement totals match transaction sum, salary matches ITR, GST number verified live with GSTN.
- 03
Pre-qualification scoring
Structured data flows into a rule-based + ML pre-qualifier that flags each application: auto-approve, auto-reject, or send to human.
- 04
Fraud flags + human-in-the-loop
Anomaly detection flags suspicious patterns (inconsistent income, tampered statements, duplicate PANs across applications) for priority human review.
What we built
- Multi-document OCR + extraction pipeline (PAN, Aadhaar, statements, GST, ITR)
- Validation layer with cross-document consistency checks
- Rule-based + ML pre-qualifier with auto-approve / auto-reject / human-review tiers
- Fraud anomaly detection with priority queue for flagged cases
- Analyst dashboard showing flagged fields, extraction confidence, audit trail
- Full audit log and compliance export for RBI inspections
Results
- 70% faster processing — average file review dropped from 40 min to 12 min
- 99.2% field-extraction accuracy across 10,000+ processed files
- 6× daily throughput handled by the same-sized analyst team
- Fraud catch rate up 3.4× thanks to cross-document consistency checks
Tech stack
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Client identity kept confidential under NDA. Metrics reflect the actual project at the time of delivery — full decks available on request.