Udemy - Introduction Antifraud Systems Building

What you'll learn
Understand the most common types of fraud in fintech and digital systems
Build mental models for detecting fraud using signals and scoring logic
Learn key antifraud architecture patterns: microservices, queues, scoring engines
See how rule engines (like Drools) help in real-time fraud detection
Apply concepts like rate limiting, logging, and behavioral analysis in design
Requirements
General understanding of backend development (Java, Node.js, Python - any is fine)
No prior antifraud knowledge is required
This course is not for complete coding beginners - it's conceptual and system-level
Description
This course gives you a practical understanding of how scalable antifraud systems are structured and operated in real-world environments.You'll explore the architecture behind fraud prevention platforms - including components like real-time data pipelines, scoring logic, rule engines, user behavior signals, and alerting. Each lecture focuses on applied thinking, helping you form a strong mental model for designing or working with fraud detection systems.This is not a coding course. There are no Java or Python examples. Instead, the course delivers strategic and architectural knowledge - ideal for software engineers, technical leads, product managers, and security architects who want to understand how antifraud systems function at scale.You'll learn:The types of fraud that affect financial and digital platformsKey architecture patterns: microservices, event-driven design, scoring enginesHow rule engines (like Drools) are used in real-time decisionsWhat signals and behaviors are typically monitoredHow teams apply rate limiting, logging, audit trails, and moreDeployment and monitoring practices to ensure stability and scalabilityBy the end of this course, you'll have clarity on how professional-grade antifraud systems are built - and how you can speak confidently about them in your team or organization. Whether you're designing systems yourself or working alongside those who do, this course will give you a clear foundation in antifraud architecture and best practices.
Who this course is for
• Backend Java developers who want to learn antifraud concepts
Developers working on payment, identity, or KYC platforms
Architects and tech leads aiming to reason about fraud defense at system level
Anyone curious how fraud detection is actually built in practice - without math or ML
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