The objective of this project is to develop an AI-driven secure payment monitoring system that dynamically adjusts security controls based on transaction risk levels. The system analyzes behavioral and transactional data in real time to prevent fraud while maintaining seamless user experience.
Study adaptive authentication and risk-based security models in financial systems.
Identify transaction parameters influencing risk assessment.
Design a risk engine to calculate dynamic risk scores.
Implement machine learning algorithms for behavioral profiling.
Integrate adaptive security measures such as step-up authentication.
Develop real-time monitoring dashboards.
Log risk scores and transaction outcomes securely.
Simulate normal and high-risk transaction scenarios.
Evaluate fraud detection accuracy and user friction impact.
Optimize risk thresholds to reduce false positives.
Document scalability and real-world deployment considerations.
