Fraud Detection System

Enterprise-grade fraud detection system with microservices architecture, implementing Java 23/Spring Boot 3.2+ backend and event-driven processing.

🚀 Features

• Clean Architecture: Domain-driven design with clear separation of concerns
• Asynchronous Processing: Redis Streams-based message queue with worker pools
• Advanced Rules Engine: Pluggable fraud detection rules (threshold, pattern, composite, ML-based)
• Circuit Breaker Pattern: Resilient external service calls
• Comprehensive Monitoring: Metrics collection with Prometheus/Grafana
• Structured Logging: JSON logging with correlation IDs
• Database Migrations: Liquibase-managed schema evolution

🏗️ Architecture

fraud-detection-system/
├── fraud-core/              # Domain entities and business logic
├── fraud-persistence/       # JPA entities and repositories
├── fraud-api/               # REST API controllers and DTOs
├── fraud-infrastructure/    # Application configuration
└── fraud-rules-engine/      # Fraud detection rules engine

🛠️ Technology Stack

• Backend: Java 23, Spring Boot 3.2+, Lombok, MapStruct
• Database: PostgreSQL 16 with JSONB support
• Queue: Redis 7 Streams for message persistence
• Build: Maven with multi-module structure
• Testing: JUnit 5, Testcontainers, RestAssured
• Monitoring: Micrometer, Prometheus, Grafana

🚀 Quick Start

Prerequisites

• Java 21+ (tested with OpenJDK 21)
• Docker and Docker Compose
• Maven 3.8+ (if not using Docker)

Using Docker Compose

1. Clone the repository

   git clone <repository-url>
   cd fraud-detection-system

2. Start all services

   docker-compose up -d

3. Check service health

   curl http://localhost:8080/actuator/health

Manual Setup

1. Start PostgreSQL and Redis

   docker run -d --name postgres -p 5432:5432 -e POSTGRES_DB=fraud_detection -e POSTGRES_USER=frauduser -e POSTGRES_PASSWORD=password postgres:16-alpine
   docker run -d --name redis -p 6379:6379 redis:7-alpine redis-server --appendonly yes

2. Build the application

   mvn clean package -DskipTests

3. Run the application

   java -jar fraud-api/target/fraud-api-1.0.0-SNAPSHOT.jar

📡 API Usage

Ingest Transaction

curl -X POST http://localhost:8080/api/v1/transactions \
  -H "Content-Type: application/json" \
  -d '{
    "transactionId": "txn-123456",
    "timestamp": "2025-01-17T10:30:00",
    "senderAccount": "ACCOUNT001",
    "receiverAccount": "ACCOUNT002",
    "amount": 150.50,
    "transactionType": "TRANSFER",
    "paymentChannel": "WEB",
    "correlationId": "550e8400-e29b-41d4-a716-446655440000"
  }'

Response

{
  "transactionId": "txn-123456",
  "correlationId": "550e8400-e29b-41d4-a716-446655440000",
  "status": "QUEUED",
  "queuedAt": "2025-01-17T10:30:00Z"
}

Health Check Endpoints

• Application Health: GET /actuator/health
• Queue Status: GET /api/v1/admin/queue/health
• Circuit Breakers: GET /api/v1/admin/circuit-breakers
• Metrics: GET /actuator/prometheus

🎯 Fraud Detection Rules

The system includes several built-in fraud detection rules:

Threshold Rules

• High Amount Rule: Triggers on transactions ≥ $50,000
• Suspicious Amount Rule: Triggers on transactions ≥ $100,000

Rule Evaluation Process

1. Ingestion: Transaction received via REST API
2. Queue: Transaction queued for async processing
3. Processing: Worker processes transaction through rules engine
4. Evaluation: All enabled rules evaluated in priority order
5. Result: Fraud decision stored with confidence scores

📊 Monitoring & Observability

Metrics Collected

• fraud.transactions.received.total - Total transactions ingested
• fraud.transactions.processed.total - Transactions processed
• fraud.transactions.alerted.total - Fraud alerts generated
• fraud.rule.evaluation.duration - Rule evaluation timing
• fraud.queue.depth - Current queue depth

Logging

Structured JSON logs with correlation IDs for request tracing:

{
  "timestamp": "2025-01-17T10:30:45.123Z",
  "level": "INFO",
  "logger": "com.fraud.detection.RulesEngine",
  "message": "Transaction evaluated",
  "correlationId": "550e8400-e29b-41d4-a716-446655440000",
  "transactionId": "txn-123456",
  "triggered_rules": ["high-amount-rule"],
  "result": "ALERT"
}

🔧 Configuration

Application Properties

spring:
  datasource:
    url: jdbc:postgresql://localhost:5432/fraud_detection
    username: frauduser
    password: ${DB_PASSWORD}

  redis:
    host: localhost
    port: 6379

fraud:
  queue:
    workers:
      count: 4
      batch-size: 10
    retry:
      max-attempts: 3
      backoff-multiplier: 2

  rules:
    hot-reload: true

🧪 Testing

Unit Tests

mvn test

Integration Tests

mvn verify -Pintegration-test

Load Testing

# Using JMeter (configure test plan for fraud-detection.jmx)
jmeter -n -t fraud-detection.jmx -l results.jtl

📈 Performance Benchmarks

• Throughput: 1000+ transactions/second
• Latency: p99 < 100ms for API, < 500ms for processing
• Concurrent Workers: Configurable pool (default: 4)
• Queue Backlog: Handles 10k+ pending messages

🔒 Security Considerations

• Input Validation: Comprehensive JSR-303 validation
• Rate Limiting: IP-based request throttling
• Authentication: JWT-based auth (extensible)
• Data Protection: PII masking in logs
• Container Security: Non-root execution

🚢 Deployment

Docker Compose (Development)

docker-compose up -d

Kubernetes (Production)

kubectl apply -f k8s/

CI/CD Pipeline

• GitHub Actions for automated testing
• Docker image builds
• Integration with artifact registries

📚 API Documentation

Complete OpenAPI 3.0 specification available at:

• Swagger UI: http://localhost:8080/swagger-ui.html
• OpenAPI JSON: http://localhost:8080/v3/api-docs

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🎯 Roadmap

• Phase 1: Foundation & Architecture
• Phase 2: Message Queue & Async Processing
• Phase 3: Advanced Rules Engine (Base Framework)
• Phase 3.2-3.5: Complete Rules Engine (Pattern, Composite, ML Rules)
• Phase 4: Notification System
• Phase 5: Admin Panel Frontend
• Phase 6: Monitoring & Logging
• Phase 7: Testing & Quality Assurance
• Phase 8: Production Deployment

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Built with ❤️ using Clean Architecture principles