Risk Radar

Inspiration

Many people receive suspicious emails and messages daily, often falling victim to scams. Risk Radar was inspired by the need to help users quickly assess message risk and protect themselves from financial fraud.

What it does

Risk Radar analyzes text messages or emails and classifies them into Low, Medium, or High Risk, providing a short explanation for the risk level. It leverages AI to detect subtle scam patterns beyond obvious keywords.

How we built it

Frontend: React + Vite for fast and interactive UI. Backend: Node.js + Express server that communicates with key words. AI Integration: Use key words to analyze messages and provide risk classification. Deployment: Local testing first, ready for GitHub deployment.

Challenges we ran into

Connect frontend programs with backend programs. Ensuring proper CORS configuration so the frontend could call the backend(different ports). Handling ambiguous messages where risk was not obvious.

Accomplishments that we're proud of

Successfully integrated key words to classify messages in real time. Created a clean, interactive frontend that shows risk results instantly. Implemented frames with varied colours for each risk level.

What we learned

How to use VS Code to edit programs in a Node.js project. Effective communication between React frontend and Express backend. How to design a simple, interesting, user-friendly interface for security tools.

What's next for Risk Radar

Improve detection accuracy using a larger dataset of scam messages. Add real-time highlighting of risky keywords in messages. Deploy the app online for public access. Enable multi-language support for scam detection.