Overview
A leading Indian NBFC (Non-Banking Financial Company) approached Omovera to help automate one of its most document-intensive workflows — loan application processing and credit underwriting.
The company was struggling with slow turnaround times, manual document verification, and data entry inefficiencies. What used to take hours of human effort per file had become a bottleneck to scale.
Omovera’s goal: Build a fully functional AI-powered document intelligence system in just 8 weeks — capable of reading, extracting, validating, and structuring data from thousands of loan-related documents with near-human accuracy using advanced AI document processing and OCR automation technologies.
🎯 The Challenge
Before Automation:
- 100% manual document review — team members had to open PDFs, screenshots, and scans to extract fields like salary, PAN, and account number.
- High error rates — inconsistencies across bank statement formats and ID proofs led to data mismatches.
- Slow turnaround time — each application took 30-45 minutes of manual effort.
- Limited scalability — the company couldn’t process more loans without adding more people.
The NBFC’s management wanted a system that could:
- Handle diverse document types — salary slips, bank statements, Aadhaar, PAN, and PDFs.
- Deliver structured JSON output usable by their credit engine.
- Be cloud-ready, API-integrable, and secure (DPDP-compliant).
- Be deployed in 8 weeks or less, without adding new tech staff.
🧠 Omovera’s Solution: Document Intelligence Engine
Omovera’s AI and automation team designed a custom document AI engine built around three core capabilities for AI workflow automation:
1. Smart Document Classification
Using a lightweight AI classification layer, the system auto-detects the type of document — whether it’s a bank statement, salary slip, PAN, or Aadhaar — without requiring a predefined template.
This allowed seamless processing of thousands of file variants from different banks and employers using intelligent AI document processing.
2. AI-Powered Data Extraction
We used OCR automation + NLP pipelines fine-tuned for Indian document layouts. The system detects tables, numeric data, and named entities using:
- Optical Character Recognition (OCR) with noise handling
- Layout parsing for structured fields (e.g., “Net Salary,” “Account Number”)
- Named entity recognition (NER) for applicant, employer, and IFSC extraction
- Heuristic validation (e.g., checking PAN structure, Aadhaar checksum)
The output is a clean, machine-readable JSON object ready for downstream underwriting workflows, making this a true AI implementation partner solution.
3. Automated Validation & Reconciliation
The extracted data is auto-validated against business rules and cross-checked with bureau information (like CIBIL).
Any anomalies (e.g., salary mismatch, missing employer name, invalid bank code) are flagged automatically for manual review in this AI for NBFCs solution.
🧩 Architecture Snapshot
The final document intelligence system included:
- 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: Built using Streamlit for real-time processing status
Document Upload → Auto Classification → OCR & NLP Extraction → Validation Engine → JSON Output → LMS Integration
All components were containerized using Docker for rapid deployment and scaling as part of our AI customization company approach.
⚡ 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
After implementation, the NBFC achieved measurable improvements across multiple KPIs through enterprise AI automation:
| Metric | Before AI | After Omovera AI Implementation |
|---|---|---|
| Document Processing Time | 30-45 mins per file | 3-5 mins per file |
| Manual Verification | 100% | <20% (only exceptions) |
| Error Rate | 8-10% | <1% |
| Operational Capacity | Limited to 200 files/day | Scalable to 2,500+ files/day |
| Employee Productivity | Static | 4× higher throughput |
| Hiring Requirement | Needed additional 5 staff | None — zero new hires |
📉 Cost savings of ~₹18 lakh annually
⏱️ 85% reduction in turnaround time
🚀 Fully scalable solution with no additional hiring
🛡️ Compliance and Data Security
Given the NBFC’s regulatory obligations, Omovera implemented:
- DPDP Act and RBI data-handling compliance
- Encrypted data at rest and in transit (AES-256, HTTPS)
- Role-based access controls and audit logs
- Auto-deletion policies for sensitive uploads after processing
This ensured that the solution was not just fast — but also secure, auditable, and regulator-ready.
🔮 The Broader Impact: Foundation for AI Scaling
What began as a single document automation project soon became a foundation for enterprise-wide AI transformation.
The same AI in lending engine is now being extended to:
- Extract employer information for credit risk scoring
- Automate loan agreement checks and signatures
- Power customer self-onboarding bots through WhatsApp
Omovera continues to support the NBFC in adding new modules for alternate data, AI-assisted KYC, and portfolio analytics, delivering comprehensive Omovera AI services.