Real-Time Transaction Analysis

Experience how machine learning models detect fraudulent transactions using pattern recognition and risk analysis.

Python scikit-learn Flask JavaScript Bootstrap
Test Transaction
Try amounts like $999 or small amounts under $10
Transactions between 00:00-06:00 are considered high-risk
Try high-risk locations like RU, UK, BR, or CN
ATM transactions at night are considered risky
Note: Initial analysis may take up to 60 seconds while the service initializes. Subsequent analyses will be instantaneous.
Quick Test Scenarios
Tips:
  • Try late-night ATM transactions (high risk)
  • Test small amounts under $10 (potential card testing)
  • Use locations like RU, BR, CN (high-risk regions)
Analysis Result
Risk Factors:
Transaction History
About This Project

This fraud detection system uses machine learning to analyze transactions in real-time. Features include:

  • Real-time transaction analysis
  • Multiple risk factor consideration
  • Location-based risk assessment
  • Pattern recognition for fraud detection

Built with Python, Flask, scikit-learn, and modern web technologies.