Learning Path
Beginner
Understanding Transaction Data
Learn the structure of transaction data and key fields for fraud detection.
Handling Imbalanced Data
Techniques for dealing with highly imbalanced fraud datasets.
Your First Fraud Model
Build a simple Random Forest classifier for fraud detection.
Evaluating Fraud Models
Understanding precision, recall, and why accuracy isn't enough.
Intermediate
XGBoost for Fraud Detection
Train and tune XGBoost models for high-performance fraud scoring.
Feature Engineering for Fraud
Create powerful features: velocity, aggregations, and behavioral patterns.
Isolation Forest Anomaly Detection
Detect anomalous transactions without labeled fraud data.
Building a Rule Engine
Implement deterministic rules to complement ML models.
Advanced
Real-Time Streaming with Kafka
Build a streaming fraud detection pipeline with Apache Kafka.
Graph-Based Fraud Ring Detection
Use graph algorithms to detect connected fraud networks.
Building a Feature Store
Implement low-latency feature serving with Redis and Feast.
Ensemble Methods & Stacking
Combine multiple models for maximum fraud detection accuracy.
Expert
Deep Learning for Fraud Detection
LSTM and Transformer models for sequential transaction patterns.
Production Deployment on Kubernetes
Deploy scalable fraud detection with K8s, monitoring, and auto-scaling.