Real-Time Fraud Detection

High-Performance Streaming Pipeline with Kafka, Spark, and Isolation Forest

Apache Kafka PySpark Streaming Isolation Forest Redis Cache
50ms
Avg Latency
99.7%
Detection Rate
100K+
Events/Second
0.1%
False Positive Rate

System Architecture

Data Ingestion

  • Kafka multi-partition topics
  • Exactly-once semantics
  • Schema registry validation
  • 100K+ events/second throughput

Processing Engine

  • Spark Structured Streaming
  • Windowed aggregations
  • Stateful processing
  • Feature engineering pipeline

ML Scoring

  • Isolation Forest anomaly detection
  • XGBoost classification ensemble
  • Redis model caching
  • Online learning updates

Interactive Demos

Explore fraud detection capabilities

Transaction Stream

Live transaction feed with real-time fraud scoring

Anomaly Detector

Interactive anomaly scoring with feature analysis

Rule Engine

Configure and test fraud detection rules

Alert Dashboard

Real-time alerts and investigation queue

Network Analysis

Fraud ring detection with graph analytics

Model Trainer

Train and evaluate fraud detection models

Feature Store

Real-time feature engineering and storage

Latency Profiler

End-to-end latency analysis and optimization

Replay Engine

Historical data replay for backtesting

Tools & Utilities

Development and operations tools

Kafka Monitor

Topic metrics and consumer lag

Benchmarks

Performance metrics and comparisons

Data Generator

Synthetic fraud data for testing

Model Export

ONNX, PMML deployment packages

Documentation

Learn and integrate

Theory & Background

Streaming ML concepts

API Reference

Complete API documentation

Tutorials

Step-by-step guides

Examples

Code samples and use cases

Quick Start

# Start Kafka and Spark clusters
docker-compose up -d kafka spark

# Initialize fraud detection pipeline
from fraud_detector import FraudPipeline

pipeline = FraudPipeline(
    kafka_brokers='localhost:9092',
    topics=['transactions'],
    model_path='models/isolation_forest_v2.pkl'
)

# Start real-time scoring
pipeline.start(
    checkpoint_path='/tmp/fraud_checkpoint',
    output_mode='update',
    trigger_interval='1 second'
)

# Score single transaction
result = pipeline.score_transaction({
    'transaction_id': 'TXN-12345',
    'amount': 2500.00,
    'merchant_category': 'electronics',
    'distance_from_home': 150,
    'time_since_last_txn': 0.5
})

print(f"Fraud Score: {result['score']:.2f}")
print(f"Decision: {result['decision']}")