Design a fraud detection system that analyzes financial transaction data to identify suspicious activities. The platform will use anomaly detection and machine learning algorithms to improve fraud prevention accuracy.
Analyze financial transaction datasets.
Implement anomaly detection algorithms.
Train supervised models for fraud classification.
Engineer features such as transaction frequency and amount deviation.
Evaluate models using ROC curves and accuracy metrics.
Develop dashboards highlighting flagged transactions.
Integrate real-time monitoring capabilities.
Test false-positive reduction strategies.
Optimize system performance.
Document findings and recommendations
