Financial Intelligence Terminal
A student-focused finance intelligence platform that aggregates financial news and converts it into structured learning outputs (key drivers, risks, and concept links).
Context & User Problem
Finance students face a significant challenge: the constant flood of financial news from multiple sources creates noise rather than clarity. Traditional news aggregators simply collect articles without providing the structured learning context that students need.
Key problems identified through user research:
- Students spend excessive time filtering irrelevant news
- Lack of structured frameworks to analyze market events
- Difficulty connecting news to fundamental finance concepts
- No centralized tool for tracking key drivers and risks
The goal was to create a platform that transforms noisy financial news into structured learning outputs—helping students understand not just what happened, but why it matters and how it connects to finance theory.
System Overview
The Financial Intelligence Terminal is built as a modern web application with a clear separation between data ingestion, processing, and presentation layers.
Frontend
React + TypeScript for interactive dashboards and guided analysis views
Backend
Python services for news processing, NLP tagging, and API orchestration
Data Layer
SQL database for structured storage with efficient query patterns
Data Pipeline
The core of the system is a four-stage pipeline that transforms raw news into structured learning content.
Ingestion
Automated collection from financial news APIs and RSS feeds. Rate limiting and deduplication at the source level to minimize noise.
Tagging & Classification
NLP-based categorization by asset class, sector, event type, and sentiment. Entity extraction for companies, people, and financial instruments.
Summarization
AI-assisted summarization using a consistent “analysis template” that extracts key drivers, risks, and relevant finance concepts for each story.
Structured Output
Delivery through interactive dashboards with filtering, search, and guided analysis views that connect news to theoretical frameworks.
QA & Reliability Approach
Drawing from my QA engineering background, I applied systematic quality practices to ensure reliable outputs:
Edge Case Handling
- Malformed input validation
- Rate limit graceful degradation
- Fallback for API failures
Data Quality Checks
- Duplicate detection algorithms
- Consistency validation
- Schema enforcement
Regression Testing
- Automated test suites
- Output comparison baselines
- CI/CD integration
Monitoring
- Processing pipeline metrics
- Error rate tracking
- Alert thresholds
Outcomes & Next Steps
Key Achievements
- Built a pipeline for news ingestion, tagging, and summarisation with consistent "analysis template"
- Developed interactive modules (market overview dashboards, guided analysis views)
- Applied QA-style validation (edge cases, data checks, regression testing mindset)
- Supports learning in valuation, rates, and macro/markets
Future Improvements
- Enhanced sentiment analysis with fine-tuned models
- User personalization based on learning goals
- Integration with portfolio simulation tools
- Real-time collaboration features for study groups