Processing 6+ million financial documents monthly with 99%+ accuracy—turning unstructured bank statements, pay stubs, and tax returns into verified data lenders can trust.
99.2%
Accuracy Rate
6M+
Docs/Month
<5s
Processing Time
Document Pipeline
Ingestion
Multi-format upload (PDF, image, fax)
Classification
AI identifies document type
Extraction
OCR + ML extracts fields
Validation
Cross-reference checks
Delivery
Structured JSON output
The Challenge: Ocrolus processed over 6 million financial documents monthly for lending institutions—bank statements, tax returns, pay stubs, and alternative income documentation—but inconsistent scan quality, diverse formatting, handwritten annotations, and non-standard templates created processing bottlenecks. Manual review queues for flagged documents were creating 2–3 day delays in loan decisions, undermining the fast-funding commitments that defined Ocrolus's competitive positioning.
The Solution: AGIX built a document intelligence pipeline combining computer vision, NLP, and custom extraction models trained specifically on 50+ financial document types. The system handles degraded image quality, identifies handwritten fields, reconciles data across multi-page documents, and cross-validates extracted figures for internal consistency—automatically routing genuinely ambiguous cases to human reviewers with highlighted uncertainty regions rather than entire document reviews.
The Impact: Document processing accuracy reached 99%+, reducing manual review requirements by 82% through intelligent triage that directed reviewer attention to genuinely uncertain cases. Processing time for complex multi-document loan files dropped from 3 days to 4 hours. Lender clients reported 40% faster loan decisioning cycles, enabling same-day funding commitments that became a key competitive differentiator in the consumer lending market.
Financial documents come in thousands of formats. Each bank has its own statement layout. Pay stubs vary by payroll provider. Tax forms change yearly. Traditional OCR breaks down when it can't anticipate the structure—and in lending, one wrong number can change a credit decision.
Standard OCR reads text but doesn't understand context. "$1,234.56" could be a deposit, withdrawal, balance, or fee. Without understanding document structure, extraction fails in unpredictable ways.
Lenders need results in seconds, not hours. Manual review doesn't scale. Ocrolus was spending 2,000+ hours monthly on manual QA review—and still missing edge cases.
"We had a QA team manually reviewing extractions, and they were drowning. Every new bank format meant more edge cases. We needed AI that could learn from corrections automatically—not just follow rules."
— Rachel Martinez, Director of Data Operations
-78%
Manual Review
1,560 hours saved/month
<5s
Processing Time
Down from 3+ minutes
$1.8M
Annual Savings
Labor cost reduction
89%
Fraud Detection
Altered document catch rate
"The breakthrough was the continuous learning system. Every time our QA team corrects an extraction, the model learns from it. We've gone from needing rules for every edge case to having AI that adapts to new formats automatically. Last month we handled a new bank format with zero manual template work."
Rachel Martinez
Director of Data Operations, Ocrolus
We build document processing systems that learn from corrections and handle new formats automatically.
Talk to our AI systems architects about your specific challenges. Get a personalized roadmap for implementation.