AI Scam & Fraud Risk Analyzer

Inspiration

Online scams are increasing rapidly, targeting students, job seekers, and everyday users. Many people struggle to judge whether a message is legitimate until it’s too late. We wanted to build a tool that not only detects scams but also explains the risk clearly, empowering users to make safer decisions.

What it Does

AI Scam & Fraud Risk Analyzer analyzes suspicious messages in real time and generates:

A scam probability score

A clear risk level (Low, Medium, High)

Human-readable explanations of detected scam signals

Actionable safety recommendations

How We Built It

The project was built as a full-stack web application:

React (Vite) frontend for user interaction

Flask backend providing a real-time REST API

A hybrid AI model using TF-IDF NLP features and rule-based fraud indicators

Explainable AI logic to highlight urgency language, financial bait, suspicious phrases, and links

Challenges We Ran Into

Designing an explainable AI system instead of a black-box model

Handling ML model serialization and deployment for real-time inference

Managing CORS between frontend and backend

Balancing transparency with effective scam detection Accomplishments That We're Proud Of

Building a fully functional end-to-end AI system

Implementing explainable scam detection instead of simple classification

Achieving real-time analysis with a clean UI

Delivering a complete, hackathon-ready project

What We Learned

How to design and deploy hybrid AI systems

Practical ML pipeline and API integration

Importance of explainability and UX in cybersecurity tools

Real-world challenges in scam and fraud detection

What’s Next for AI Scam & Fraud Risk Analyzer

Browser extension for real-time scam detection

URL reputation and domain analysis

Multi-language support

Enterprise and educational security integrations

Built With

• analysis
• flask
• flask-cors
• html
• javascript
• joblib
• nlp)
• python)
• react
• rule-based
• scikit-learn
• tf-idf
• vite)