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)
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. Analysts spent 48+ hours manually gathering and entering data after each release. We built an autonomous pipeline that monitors portals, downloads reports the moment they're published, extracts all financial data via OCR, validates it mathematically, and outputs ML-ready structured datasets.
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.
Key Features
Challenges & Solutions
Implemented sophisticated headless browser automation with randomized user-agent rotation, request throttling, and human-behavior simulation — achieving consistent document retrieval.
Integrated OpenCV computer vision to detect table boundaries before OCR execution, ensuring 98% data alignment accuracy across merged cells and vertical text.
Built a Python mapping layer that normalizes diverse terms (e.g. 'Revenue' vs 'Turnover') into a unified database schema, enabling cross-company analysis.
Built an asynchronous processing queue using FastAPI background workers, enabling hundreds of pages to be processed in parallel without system degradation.
Results
Data Availability Lag
post-release
Manual Data Entry
zero touch
Table Extraction Accuracy
alignment accuracy
Math Validation Error Rate
across thousands of rows
Analyst Research Time
review only
ML Pipeline Readiness
time-series output
Goals
- •Create a hands-off data acquisition layer for the firm's prediction engines
- •Achieve sub-15-minute data availability after report publication
- •Ensure mathematical integrity of all extracted financial data
- •Eliminate analyst time spent on data gathering and entry
Tech Stack
- •Python
- •FastAPI
- •React.js
- •Selenium
- •OpenCV
- •Tesseract
- •Pandas
- •SQL
Target Users
- •Financial analysts
- •Portfolio managers
- •Data scientists and quant researchers
Key Learnings
- •For financial institutions, data integrity outranks speed — mathematical validation was the most valued feature
- •Computer vision preprocessing is essential before OCR on structured financial documents
- •A unified terminology mapping layer is what makes cross-company data actually usable
- •Async processing queues are non-negotiable for high-volume document pipelines
Future Plans
- •Move to direct vector storage for RAG-based natural language querying of reports
- •Add earnings call transcript analysis via speech-to-text
- •Expand coverage to international exchanges and EDGAR filings
- •Build automated anomaly detection for flagging unusual financial movements