The Challenge

London Stock Exchange Group (LSEG) needed to extract and process large volumes of unstructured financial data from historical and real-time sources.

The existing process relied heavily on manual effort, leading to inconsistencies, inefficiencies, and high operational costs.

LSEG faced challenges with the extraction and structuring of data from various sources, including:

• Securities and Exchange Commission (SEC) filings.
• Securitized Remittance Reports (SRR).
• Municipal Bonds (MB).
• Notices of Redemption (NoR).
• Historical documents spanning over 50 years.

The challenge was to automate this process while maintaining accuracy, data integrity, and compliance with financial regulations.

The Project

• The solution involved deploying an AI-powered Semantic Extractor to digitize, categorize, and process LSEG’s unstructured financial data.

  • AI-powered OCR and NLP Models: Trained on financial documents to extract key data points.
  • Automated Classification: AI assigned categories based on predefined financial metadata.
  • Seamless Data Integration: Extracted data was structured for direct use in financial reports and analytics.

Project Delivery

1.Document Parsing and Digitization:

• AI scanned and converted physical and PDF documents into structured digital formats.
• Natural Language Processing (NLP) extracted critical financial details.

2.Data Validation and Structuring:

• AI-driven checks ensured extracted data was accurate and compliant with industry standards.

3.Historical Archive Processing:

• Over 120,000 historical documents were digitized and processed, reducing reliance on manual retrieval.

Benefits

• 90% Reduction in Processing Time: Automated workflows accelerated document handling.
• 95%+ Accuracy: AI minimized manual errors.
• Cost Savings: Automation reduced labor costs.
• Data Accessibility: Digitized archives improved searchability and retrieval.

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