Risk Factors Visualization
Stanford Law School hackathon project automating risk factor analysis in SEC filings (10-K and 10-Q) using NLP and interactive graph visualization.
Overview
Developed a Risk Factors Visualization Tool during Stanford Law School hackathon to automate risk factor analysis in SEC filings. Automated extraction and categorization of risk factors from legal documents with interactive graph visualization.
The Challenge
SEC filings (10-K and 10-Q) contain critical risk information buried in dense legal text spanning hundreds of pages. Investors and analysts need to quickly understand risk relationships and dependencies across multiple filings, but manual analysis is time-consuming and prone to missing important connections.
The Solution
Utilized NLP with LangChain's GPT-3.5-turbo model to extract and categorize risk factors from legal documents. Parsed data from the SEC EDGAR database using BeautifulSoup for data extraction. Implemented graph visualization of risk factor relationships using NetworkX and PyVis, rendered within a Streamlit web app for interactive exploration. Ensured secure environment configuration with python-dotenv for managing sensitive information. Designed user experience in Figma.
Impact & Results
Automated risk factor extraction from SEC filings
Reduced analysis time from hours to minutes
Interactive network graphs showing risk relationships
Comparative risk analysis across multiple companies
Recognized at Stanford Law School Hackathon
Technologies
Tags
ClosingTheDivide - Global Tech Access
Multi-national 501(c)(3) nonprofit expanding technological access. Led team that donated 1,350+ computers, raised $285K, and established 12 international computer labs.
Alex AI
Winner of 2023 TedAI & Microsoft Hackathon in San Francisco. AI-powered business creation tool that identifies optimal business structures and auto-fills documents.