Главная
Study mode:
on
1
Introduction to Anti-Fraud
2
Understanding Fraud and Anti-Fraud Mechanisms
3
Fraud in Mobile Gaming
4
Payment Fraud in FinTech
5
Building an Automatic Anti-Fraud System
6
Challenges with Machine Learning in Anti-Fraud
7
Infrastructure and Data Considerations
8
Ensemble Models and Risk Management
9
Operational Workflow and Monitoring
10
Conclusion and Key Takeaways
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it 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

Conf42
Add to list