End-to-End Financial Report Automation & OCR Pipeline
An autonomous system monitoring stock exchange portals, downloading financial filings, and extracting structured data via OCR, reducing data availability lag from 48 hours to 15 minutes with zero manual entry.
- Client
- Quantitative Investment Firm (NDA)
- Industry
- Finance
- Type
- AI & Automation
- Year
- 2024
- Our role
- AI Automation & Backend Architect
- Timeline
- 12 weeks
- Team
- 1 dev, 1 design
Overview
A quantitative asset management firm relied on timely financial data from company filings, but reports were locked in non-searchable PDFs across multiple exchange portals.
Process.
- Built a headless browser automation layer for portal navigation and document download.
- Developed a custom OCR pipeline with OpenCV table detection for financial statement extraction.
- Added a Pandas-based data cleaning and normalization engine.
- Exposed results through a React dashboard for monitoring and manual triggers.
What we built.
- feat24/7 automated monitoring of stock exchange portals for new filings
- featHeadless browser scraping with anti-bot evasion (rotating agents, throttling)
- featOpenCV-powered table boundary detection before OCR processing
- featHigh-fidelity extraction of Balance Sheets, P&L, and Cash Flow statements
- featMathematical validation engine (Assets = Liabilities + Equity checks)
- featTerminology mapping layer normalizing diverse financial terms to unified schema
Hard parts.
fixDynamic exchange portals with anti-bot protections
Implemented sophisticated headless browser automation with randomized user-agent rotation, request throttling, and human-behavior simulation, achieving consistent document retrieval.
fixStandard OCR failing on complex multi-column financial tables
Integrated OpenCV computer vision to detect table boundaries before OCR execution, ensuring 98% data alignment accuracy across merged cells and vertical text.
fixInconsistent financial terminology across company filings
Built a Python mapping layer that normalizes diverse terms (e.g. 'Revenue' vs 'Turnover') into a unified database schema, enabling cross-company analysis.
fixLarge PDF volumes causing processing bottlenecks
Built an asynchronous processing queue using FastAPI background workers, enabling hundreds of pages to be processed in parallel without system degradation.
Built with
Python FastAPI React.js Selenium OpenCV Tesseract Pandas SQL
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