Introduction Antifraud Systems Building

Design and understand scalable antifraud systems for real-time risk detection.

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.

Overview

Section 1: Understanding the Fraud Problem

Lecture 1 What is Fraud? Why Developers Should Care

Lecture 2 Types of Fraud in Fintech (Real Cases)

Lecture 3 Fraud Impact Business Risk and User Trust

Section 2: Antifraud System Architecture

Lecture 4 Antifraud System Overview Components That Matter

Lecture 5 Java Microservices for Fraud Detection

Lecture 6 Event-Driven Messaging Kafka & Real-Time Streams

Lecture 7 Data Storage PostgreSQL, NoSQL, and OpenSearch

Section 3: Detection Logic and Behavior Profiling

Lecture 8 Detection Logic Rules vs ML Models

Lecture 9 Scoring Engine Thresholds, Weighting, and Confidence

Lecture 10 Behavioral Profiling Device Fingerprinting and Geo

Lecture 11 KYC & AML Integration into Backend Flow

Section 4: Defense Layer & Protection Logic

Lecture 12 Blacklists, Velocity Checks, and Pattern Matching

Lecture 13 Building the Fraud Detection API in Java

Lecture 14 Real-Time Fraud Evaluation with Drools

Lecture 15 Audit Trail, Logging, and Tamper Protection

Lecture 16 Rate Limiting and Blocking in Practice

Section 5: Deployment, Scaling, and Strategy

Lecture 17 System Deployment, Monitoring, and Scaling

Lecture 18 Presenting Antifraud Architecture to Stakeholders

Section 6: Final Summary & Key Takeaways

Lecture 19 Congratulations

• 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