Back to news
Use CaseApril 1, 2026· 7 min read

How JP Morgan Uses LLMs for Document Analysis at Scale

JP Morgan's AI-powered document analysis system processes millions of financial documents annually, reducing review time by 90% while improving accuracy.

By Agentic DailySource: VentureBeat

The Problem

JP Morgan processes over 12,000 commercial lending agreements annually, each running 100-300 pages. Manual review by lawyers and analysts previously took 360,000 hours of work per year — a massive cost center with inherent human error risks.

The AI Solution

The bank developed COIN (Contract Intelligence), an AI platform that uses fine-tuned LLMs to:

  • Extract key terms, covenants, and clauses from complex legal documents
  • Compare terms against standard templates and flag deviations
  • Identify potential risks and compliance issues
  • Generate structured summaries for analyst review

Results & Impact

  • 360,000 hours of manual work reduced to seconds per document
  • Error rate dropped from 1.5% (human) to 0.1% (AI-assisted)
  • $150M+ in annual cost savings from operational efficiency
  • Analysts now focus on judgment-intensive work rather than data extraction

Technical Approach

The system uses a multi-stage pipeline: OCR for scanned documents, a fine-tuned LLM for information extraction, a knowledge graph for cross-document analysis, and a human-in-the-loop verification interface for high-stakes decisions.

#Finance#LLM#Document Analysis#Banking
Share:
Keep reading

Related stories