Ocrolus : AI Document Intelligence Engine for Automated Fintech Workflows

Fintech

Ocrolus – AI Document Intelligence for Fintech Automation

Ocrolus transformed its manual underwriting processes by implementing our AI-powered document processing engine. Using advanced OCR and NLP, the system extracts structured data from bank statements, pay stubs, invoices, and identity documents with over 99% accuracy. It identifies inconsistencies, flags anomalies, and validates documents instantly—speeding up loan approval times dramatically. The AI continuously learns from edge cases, improving precision with every document scanned. Fintech lenders now benefit from faster turnaround times and lower operational costs. Our solution made their KYC and underwriting workflows fully intelligent and audit-ready.

Project Overview

Client: Ocrolus (Trusted by 400+ lenders and financial institutions)
Challenge: High operational costs due to manual document review + delays in underwriting pipelines
Goal: Implement an AI document engine to:
- Extract structured financial data from a wide range of documents
- Detect fraud indicators and automate KYC/underwriting checks
- Scale to handle thousands of documents per day with minimal human intervention
Team: 7 (2 OCR Experts, 3 NLP Engineers, 1 QA Lead, 1 Compliance Advisor)
Timeline: 5.5 months (Development → Compliance Testing → Production Rollout)

“GenX didn’t just automate our workflows—they gave us superhuman underwriting speed with confidence in every click.”

VP of Automation & Risk, Ocrolus

The Challenge

Critical Pain Points:

Loan decisions were delayed by slow, error-prone document verification
Manual review missed key red flags and inconsistencies in financial documents
High compliance burden in proving document validity during audits

Technical Hurdles:

Processing mixed-format PDFs, images, and scanned files with varying quality
Achieving high precision across diverse document templates and layouts
Building explainable AI that could justify data extraction and anomaly detection during audits

Tech Stack

Component	Technologies
OCR & Document Parsing	AWS Textract, Tesseract, LayoutLMv3, PDFMiner
NLP & Validation	spaCy, Scikit-learn, Python Rule Engines
Backend & APIs	Node.js, FastAPI, PostgreSQL, Redis
Cloud Infrastructure	AWS Lambda, S3, CloudWatch, Step Functions
Monitoring & Compliance	Sentry, Vanta, SOC 2 Auditing Logs

Key Innovations

OCR and NLP extracted data from pay stubs, bank statements, and IDs with high precision. AI flagged anomalies instantly and improved with edge case training. Automation cut manual processing while accelerating approval times.

99.2% Accuracy on Structured Extraction

Parsed thousands of financial docs with table-level precision

Result: 61% faster underwriting decisions across partner lenders

Anomaly Detection for Document Fraud

Caught forged or tampered documents in milliseconds

Result: 33% drop in manual compliance escalations

Self-Improving AI Engine

Actively learned from flagged edge cases to boost precision

Result: 28% fewer human overrides needed over time

Our AI/ML Architecture

Core Models

OCR & Layout Parser Engine:
- Hybrid OCR stack (AWS Textract + Tesseract + LayoutLMv3)
- Normalizes complex layouts into key-value pairs and tables
Anomaly & Consistency Validator:
- Rule-based + ML-driven checks for outliers (e.g., mismatched SSNs, altered digits)
- Entity recognition for names, dates, and financial line items
Self-Learning Accuracy Enhancer:
- Active learning module that retrains on edge case feedback from underwriters
- Improves extraction accuracy across document variations

Data Pipeline

Sources
- Uploaded documents (bank statements, ID cards, pay stubs, tax returns)
- Loan origination systems, CRM platforms
- 3rd-party validation APIs (SSN, KYC checks)

Cloud Processing: Lambda coordinates S3 → Textract → failover to OCR

Integration Layer

Plug-and-play with loan origination systems and CRM tools (e.g., Salesforce, HubSpot)
REST APIs for real-time processing
Audit trail generator for compliance documentation