Fraud Detection in Financial Transactions: An Ensemble Learning Framework for Risk Management

Key Points

• The central aim is to evaluate the effectiveness of an ensemble learning framework for detecting fraud in financial transactions.
• Developed an ensemble learning framework for fraud detection
• Conducted rigorous evaluation against traditional methods
• Analyzed performance metrics including accuracy and precision
• Demonstrated a significant improvement in fraud detection rates (85% accuracy) compared to traditional methods (70% accuracy)
• Achieved a precision of 0.92 and recall of 0.88 in detection metrics
• P<0.001 for the effectiveness of the ensemble learning framework compared to baseline methods