Private Fund Data Extraction and Monitoring Pipeline using LLMs

Problem statement:

To develop an advanced financial document analysis system that leverages Large Language Models (LLMs) to automatically extract, process, and structure complex financial information from investment fund PDFs, while ensuring high accuracy and maintaining data integrity across visual and textual elements.

Approach

1. PDF Processing and LLM Integration:
   • Conversion of PDFs to machine-readable format
   • Using LLMs with multimodal capabilities (like GPT-4V) for visual and textual understanding
   • Implementation of prompt engineering strategies for accurate data extraction
2. Information Extraction Module:
   • LLM-based extraction of structured financial data:
     • Fund names and details
     • Financial metrics
     • Portfolio compositions
     • Management information
   • Few-shot learning approach with examples for better accuracy
   • Structured output templates for consistent data formatting
3. Graph Extraction:
   • Multimodal LLM processing for graph interpretation
   • Extraction of:
     • Data points from charts
     • Trend lines
     • Legends and labels
     • Numerical values
   • Conversion of visual data to structured format
4. Database Management:
   • Schema design optimized for LLM-extracted data
   • Version control for monthly updates
   • Validation system to verify LLM outputs

Observation

• LLMs show high accuracy in understanding context and extracting relevant information
• Multimodal capabilities help in interpreting both text and graphical data
• Need for careful prompt engineering to ensure consistent outputs
• Some edge cases require human verification
• LLM processing time impacts overall pipeline performance

Optimization steps and Results

1. LLM Performance Optimization:
   • Prompt template optimization for faster processing
   • Batch processing implementation
   • Caching of similar document structures
   • Fine-tuning strategies for specific financial document types
2. Accuracy Improvements:
   • Implementation of validation rules for LLM outputs
   • Cross-verification between text and graph data
   • Confidence score system for extracted information
   • Human-in-the-loop for low confidence extractions
3. Pipeline Efficiency:
   • Parallel processing of multiple documents
   • Optimized database operations for updates
   • Automated quality checks and error reporting

Contribution

1. Technical Innovation:
   • Novel application of LLMs for financial document processing
   • Advanced prompt engineering techniques for financial data
   • Integration of multimodal LLM capabilities for graph extraction
2. Business Impact:
   • Significantly reduced manual processing time
   • Higher accuracy in data extraction
   • More comprehensive data capture including graphical information
   • Scalable solution for handling multiple funds
3. Process Improvement:
   • Automated end-to-end pipeline
   • Reduced human intervention
   • Better handling of unstructured data
   • Improved accuracy in graph data extraction