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FINANCE

Document Processing Automation

Automated KYC, statement parsing, and loan pre-qualification — cut manual review workload dramatically while improving accuracy.

70%Faster Processing
99.2%Extraction Accuracy
Daily Throughput

Client snapshot

Client
NBFC handling personal and small-business loans; reviews 800+ loan applications/day
Location
India
Timeline
8 weeks phased rollout across document types
Our team
2 AI engineers, 1 compliance consultant

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

  1. 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.

  2. 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.

  3. 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.

  4. 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
99.2%Extraction Accuracy
Daily Throughput
  • 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

Azure Document Intelligence + custom fine-tunesOpenAI GPT-4 Vision for edge-case documentsGSTN API for live verificationPython + FastAPI + CeleryPostgres + S3Private VPC deployment for RBI compliance
We can now say yes or no to a loan in 12 minutes instead of a day. And we catch more fraud than we ever did manually. That's the whole business.
Chief Risk Officer, NBFC (NDA)

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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.