A leading Indian NBFC approached Omovera to automate loan document processing and credit underwriting. What took 30–45 minutes of human effort per file became a fully automated AI pipeline in just 8 weeks.
Overview
A leading Indian NBFC approached Omovera to automate loan application processing and credit underwriting. The company struggled with slow turnaround times, manual document verification, and data entry inefficiencies that had become a bottleneck to scale.
Omovera’s goal: build a fully functional AI-powered document intelligence system in 8 weeks — capable of reading, extracting, validating, and structuring data from thousands of loan-related documents with near-human accuracy.
The Challenge: Before Automation
- 100% manual document review — team members opened PDFs and scans to extract fields like salary, PAN, and account number.
- High error rates — inconsistencies across bank statement formats led to data mismatches.
- Slow turnaround — each application took 30–45 minutes of manual effort.
- Limited scalability — couldn’t process more loans without adding more people.
Omovera’s Solution: Document Intelligence Engine
1. Smart Document Classification
AI classification layer auto-detects document type — bank statement, salary slip, PAN, or Aadhaar — without requiring a predefined template.
2. AI-Powered Data Extraction
OCR automation + NLP pipelines fine-tuned for Indian document layouts. Delivers clean, machine-readable JSON for downstream underwriting.
3. Automated Validation & Reconciliation
Extracted data auto-validated against business rules and cross-checked with bureau data (CIBIL). Anomalies flagged automatically.
Architecture Snapshot
- Frontend: Document upload portal (web + mobile)
- Processing Core: Modular AI pipelines using Python, FastAPI, and lightweight transformer models
- Data Layer: Secure AWS S3 storage + PostgreSQL metadata tables
- Integration Layer: REST APIs connected to NBFC’s Loan Management System (LMS)
- Monitoring Dashboard: Streamlit for real-time processing status
Timeline — Built in Just 8 Weeks
| Phase | Duration | Key Deliverables |
|---|---|---|
| Week 1–2 | 2 weeks | Requirement discovery, sample data ingestion, model benchmarking |
| Week 3–4 | 2 weeks | OCR + NLP pipeline prototyping and field mapping |
| Week 5–6 | 2 weeks | API integration, validation logic, and dashboard design |
| Week 7 | 1 week | UAT, performance testing — 95%+ field accuracy achieved |
| Week 8 | 1 week | Cloud deployment, training, and documentation handover |
Results & Business Impact
| Metric | Before AI | After Omovera AI |
|---|---|---|
| Document Processing Time | 30–45 mins/file | 3–5 mins/file |
| Manual Verification | 100% | <20% (exceptions only) |
| Error Rate | 8–10% | <1% |
| Operational Capacity | 200 files/day | 2,500+ files/day |
| Employee Productivity | Static | 4× higher throughput |
| New Hires Required | 5 additional staff needed | Zero — no new hires |
ROI Summary
📉 Cost savings of ~₹18 lakh annually
⏱️ 85% reduction in turnaround time
🚀 Fully scalable — zero additional hiring
Compliance & Data Security
Client Feedback
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