Back to Projects Machine Learning

Financial Dashboard & Analytics

Financial data is notoriously dense. Rows upon rows of Excel sheets might be fine for an accountant, but for a high-net-worth individual trying to get a quick pulse on their portfolio, it is a nightmare. Our client, WealthGuard, wanted to change that.

Design Philosophy

We started with a simple question: "What does the user actually need to know right now?" We stripped away the noise. Instead of showing every single transaction on the home screen, we aggregated data into meaningful "Movement Cards"—cards that explain why a portfolio went up or down, not just by how much.

Technical Hurdles

The biggest challenge was data normalization. We were pulling data from APIs that spoke different languages—Crypto exchanges, traditional stock markets, and real estate valuation tools. We built a robust ETL (Extract, Transform, Load) pipeline using Python and AWS Lambda that standardizes this data into a uniform format before it ever hits our PostgreSQL database.

Impact

Since launch, WealthGuard has reported a 300% increase in daily active users. Clients are checking the app not just monthly for statements, but daily to track their progress.

Technologies

React Node.js PostgreSQL Chart.js AWS Lambda Python (Data Processing)

Project Info

  • Created a "Scenario Builder" that lets users simulate market crashes to test portfolio resilience.
  • Implemented biometric authentication (FaceID/TouchID) for mobile web users.
  • Developed a custom charting library on top of D3.js to support specialized financial indicators.

Gallery