ML-Based Fraud Detection for Online Transactions

Objectives to cover:

• Introduction
  ML-based fraud detection strengthens security in online transactions by identifying anomalies in real time.
• Overview of Online Transaction Security Challenges
  Growing digital payments face threats like phishing, identity theft, and transaction fraud.
• Traditional vs. Machine Learning Approaches
  Unlike static rule-based systems, ML adapts dynamically to evolving fraud patterns.
• Machine Learning Algorithms for Fraud Detection
  Supervised, unsupervised, and deep learning models help detect complex fraudulent behaviors.
• Feature Engineering and Data Preprocessing
  Key features like transaction history, location, and frequency improve model accuracy.
• System Architecture of the Framework
  The framework integrates data ingestion, ML models, and real-time decision layers.
• Model Training and Performance Metrics
  Metrics such as accuracy, precision, recall, and AUC evaluate fraud detection models.
• Integration with Real-Time Systems
  ML solutions connect seamlessly with payment gateways for instant fraud alerts.
• Conclusion
  ML-driven fraud detection ensures secure, adaptive, and reliable online transaction systems.

Reference:  IEEE Style