Explore a comprehensive conference talk that delves into the development and implementation of machine learning-based anti-fraud systems. Learn about different types of fraud across industries, with specific focus on mobile gaming and FinTech sectors. Discover the essential components of building automatic anti-fraud systems, including infrastructure requirements, data handling, and the implementation of ensemble models for risk management. Master the challenges associated with machine learning in fraud detection, understand operational workflows, and gain practical insights into monitoring systems for optimal performance. Through real-world examples and detailed explanations, gain valuable knowledge about creating reliable fraud detection mechanisms that can protect businesses and customers while maintaining operational efficiency.
Leveraging Machine Learning to Create a Reliable Anti-Fraud System
Overview
Syllabus
Introduction to Anti-Fraud
Understanding Fraud and Anti-Fraud Mechanisms
Fraud in Mobile Gaming
Payment Fraud in FinTech
Building an Automatic Anti-Fraud System
Challenges with Machine Learning in Anti-Fraud
Infrastructure and Data Considerations
Ensemble Models and Risk Management
Operational Workflow and Monitoring
Conclusion and Key Takeaways
Taught by
Conf42
