Create a fraud detection system for ATMs that processes transaction patterns and camera data locally using edge computing. The system will detect suspicious activities in real time, reducing financial fraud risks while maintaining secure and low-latency operations.
Study ATM security mechanisms and fraud patterns.
Research transaction anomaly detection algorithms.
Design edge architecture for ATM-level data analytics.
Implement real-time transaction monitoring logic.
Integrate camera-based suspicious behavior detection.
Configure encrypted communication with central banking servers.
Optimize model performance for embedded ATM hardware.
Conduct simulated fraud scenarios for validation.
Compare detection latency with centralized monitoring systems.
Evaluate improvements in fraud prevention efficiency.
Document compliance with financial security standards.
