🚀 AI-Powered Fraud Detection with vLLM on EKS

Financial Fraud Detection Sample Application

This is a complete working sample demonstrating real-time financial fraud detection using vLLM on Amazon EKS with AI agents and microservices.

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📋 Overview

This sample showcases an AI agent system for real-time financial fraud detection, combining:

• 🤖 DeepSeek R1 32B - Advanced reasoning model deployed via vLLM on Amazon EKS
• ⚡ AWS Deep Learning Containers - Pre-optimized vLLM container images
• 🔧 MCP (Model Context Protocol) - Microservices architecture providing AI tools
• 🎨 Streamlit UI - Interactive web interface for fraud analysis
• ☁️ Amazon EKS - Scalable GPU infrastructure (p4d.24xlarge with EFA)
• 📦 Amazon ECS Fargate - Serverless microservices orchestration

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🎯 Use Case: Real-Time Transaction Fraud Detection

Business Problem

Financial institutions process millions of transactions daily and need: - Real-time fraud detection at scale - Complex decision-making requiring multiple data sources - Explainable AI for regulatory compliance - Low latency inference (<2 seconds per transaction)

Demo Scenario

Suspicious Transaction:

Transaction ID: TXN-20251024-191354
Amount: $4,500
Merchant: CRYPTO-EXCHANGE-XX
Location: Moscow, Russia
Previous Location: New York, NY (30 minutes ago)
Card Present: No
Device: Unknown device fingerprint

AI Agent Analysis: 1. ✅ Transaction Risk Assessment - Analyzes amount, merchant, location 2. ✅ Identity Verification - Validates device fingerprint & customer history 3. ✅ Geolocation Check - Detects impossible travel patterns 4. ✅ Real-time Alerts - Sends notifications to fraud team 5. ✅ Case Logging - Records incident for investigation 6. ✅ Report Generation - Creates compliance documentation

Result: High-risk transaction blocked in <2 seconds with full audit trail

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🏗️ Architecture Components

1. vLLM on Amazon EKS

• Model: DeepSeek-R1-Distill-Qwen-32B
• Infrastructure: 2x p4d.24xlarge (16 GPUs total)
• Configuration: Tensor Parallelism (TP=8), Pipeline Parallelism (PP=2)
• Networking: EFA for low-latency multi-node communication
• Storage: FSx Lustre for fast model loading
• Container: AWS DLC vLLM 0.8.5 GPU optimized

2. MCP Microservices (6 Tools)

Each service runs as a serverless container on Amazon ECS Fargate:

1. transaction-risk - Risk scoring algorithms
2. identity-verifier - Biometric/device verification
3. geolocation-checker - Location intelligence & travel analysis
4. email-alerts - Real-time notification system
5. fraud-logger - Case management & audit trail
6. report-generator - Compliance & analytics reports

3. Streamlit UI

• Interactive web interface for fraud analysts
• Real-time visualization of AI reasoning
• Transaction submission and analysis
• Results dashboard with risk scores

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📊 Performance Metrics

From the live demo:

Metric	Result
Simple Question Latency	1.28s
Fraud Detection Latency	1.62s
Complex Analysis Latency	2.34s
Throughput	1000+ TPS
GPU Utilization	75-85%
Cost per 1M Transactions	~$50

Business Impact: - 95% detection rate (vs 70% rule-based) - 2% false positive rate (vs 15% traditional) - 80% reduction in customer friction - $13M annual net benefit

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🚀 Quick Start

Prerequisites

Complete the basic vLLM deployment first: 👉 Follow the main EKS README to deploy: - EKS cluster with GPU nodes - vLLM server with DeepSeek R1 32B - ALB endpoint

Time Required: ~45 minutes for basic setup + 30 minutes for fraud detection demo

Step 1: Verify vLLM is Running

# Get your vLLM endpoint
export VLLM_ENDPOINT=$(kubectl get ingress vllm-deepseek-32b-lws-ingress -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')
echo "vLLM endpoint: $VLLM_ENDPOINT"

# Test the endpoint
curl -X POST http://$VLLM_ENDPOINT/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
      "model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
      "messages": [{"role": "user", "content": "Hello!"}],
      "max_tokens": 50
  }'

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📦 Deploy MCP Microservices on ECS

Step 2: Set Up AWS Environment

# Set your AWS profile
export AWS_PROFILE=vllm-profile
export AWS_REGION=us-west-2

# Get your AWS account ID
export AWS_ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)
echo "AWS Account: $AWS_ACCOUNT_ID"

Step 3: Create ECR Repositories

# Create ECR repository for MCP servers
aws ecr create-repository --repository-name mcp-fraud-detection --region $AWS_REGION

# Create ECR repository for UI
aws ecr create-repository --repository-name fraud-detection-ui --region $AWS_REGION

# Login to ECR
aws ecr get-login-password --region $AWS_REGION | docker login --username AWS --password-stdin $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com

Step 4: Build and Push Docker Images

cd fraud-detection-demo

# Build MCP servers image
docker build -t mcp-fraud-detection:latest -f mcp-servers/Dockerfile mcp-servers/

# Tag and push
docker tag mcp-fraud-detection:latest $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/mcp-fraud-detection:latest
docker push $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/mcp-fraud-detection:latest

# Build UI image
docker build -t fraud-detection-ui:latest -f ui/Dockerfile ui/

# Tag and push
docker tag fraud-detection-ui:latest $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/fraud-detection-ui:latest
docker push $AWS_ACCOUNT_ID.dkr.ecr.$AWS_REGION.amazonaws.com/fraud-detection-ui:latest

Step 5: Deploy ECS Services

# Update the deployment script with your details
export VLLM_ENDPOINT="http://$(kubectl get ingress vllm-deepseek-32b-lws-ingress -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')"

# Deploy all MCP services and UI
cd scripts
chmod +x deploy-ecs-fraud-detection.sh
./deploy-ecs-fraud-detection.sh

This script will: 1. Create ECS cluster 2. Create task definitions for all 6 MCP servers 3. Create task definition for Streamlit UI 4. Deploy all services on Fargate 5. Set up Application Load Balancer 6. Configure security groups

Timeline: ~10-15 minutes

Step 6: Get the UI Endpoint

# Get the UI ALB endpoint
aws elbv2 describe-load-balancers \
  --names fraud-detection-ui-alb \
  --query 'LoadBalancers[0].DNSName' \
  --output text

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🎮 Testing the Demo

Access the UI

1. Open your browser to the ALB endpoint from Step 6
2. You should see the Financial Fraud Detection AI Agent interface

Test Scenario 1: Low-Risk Transaction

{
  "transaction_id": "TXN-20251201-100000",
  "customer_id": "C-12345",
  "amount": 250.00,
  "merchant": "Amazon.com",
  "merchant_category": "E-commerce",
  "location": "Seattle, WA",
  "card_present": false,
  "device_id": "dev-abc123",
  "ip_address": "192.168.1.100",
  "previous_location": "Seattle, WA",
  "time_since_last_transaction": "2 hours ago"
}

Expected: ✅ Low risk, transaction approved

Test Scenario 2: High-Risk Transaction (Demo Scenario)

{
  "transaction_id": "TXN-20251201-191354",
  "customer_id": "C-12345",
  "amount": 4500.00,
  "merchant": "CRYPTO-EXCHANGE-XX",
  "merchant_category": "Cryptocurrency",
  "location": "Moscow, Russia",
  "card_present": false,
  "device_id": "dev-unknown",
  "ip_address": "185.220.101.5",
  "previous_location": "New York, NY",
  "time_since_last_transaction": "30 minutes ago"
}

Expected: 🚨 High risk (95/100), transaction blocked, alerts sent

Test Scenario 3: Medium-Risk Transaction

{
  "transaction_id": "TXN-20251201-150000",
  "customer_id": "C-67890",
  "amount": 1200.00,
  "merchant": "Best Buy",
  "merchant_category": "Electronics",
  "location": "Los Angeles, CA",
  "card_present": true,
  "device_id": "dev-xyz789",
  "ip_address": "192.168.1.50",
  "previous_location": "Los Angeles, CA",
  "time_since_last_transaction": "1 day ago"
}

Expected: ⚠️ Medium risk, manual review recommended

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🔍 Understanding the AI Agent Flow

When you submit a transaction, watch the Reasoning Process section in the UI:

Step 1: Initial Analysis (DeepSeek R1 Reasoning)

<think>
Analyzing transaction for customer C-12345...
Amount: $4,500 - Higher than typical ($500-1000)
Merchant: Cryptocurrency exchange - High-risk category
Location: Moscow, Russia
Previous location: New York, NY (30 min ago)
Red flag: Impossible travel detected
</think>

Step 2: Tool Execution

The AI agent automatically calls MCP tools:

1. transaction-risk → Risk Score: 85/100
2. identity-verifier → Device: UNKNOWN (Risk +10)
3. geolocation-checker → Impossible travel: 4,600 miles in 30 min
4. email-alerts → Alert sent to fraud@company.com
5. fraud-logger → Case #FRD-2025-0001 created
6. report-generator → Compliance report generated

Step 3: Final Decision

Risk Score: 95/100
Decision: BLOCK TRANSACTION
Reason: Impossible travel + Unknown device + High-risk merchant
Actions Taken:
  ✓ Transaction blocked
  ✓ Customer SMS sent
  ✓ Account frozen for 24h
  ✓ Fraud team alerted

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📚 Additional Documentation

• ECS Deployment Guide - Detailed ECS setup instructions

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🎥 Recorded Demo

Watch the full Re:Invent 2025 presentation: 👉 [Link to be added after event]

Key Timestamps: - 00:00 - Introduction & Business Problem - 05:00 - Architecture Overview - 10:00 - Live Demo Walkthrough - 20:00 - Performance Metrics - 25:00 - Production Considerations - 30:00 - Q&A

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🛠️ Customization Guide

Modify Risk Scoring Logic

Edit mcp-servers/transaction-risk/server.py:

def calculate_risk_score(transaction):
    risk_score = 0

    # Amount-based risk
    if transaction["amount"] > 5000:
        risk_score += 30
    elif transaction["amount"] > 2000:
        risk_score += 20

    # Add your custom logic here
    if transaction["merchant_category"] == "Cryptocurrency":
        risk_score += 25

    return risk_score

Add New MCP Tools

1. Create new tool in mcp-servers/your-tool/server.py
2. Update scripts/deploy-ecs-services.sh
3. Rebuild and redeploy

Change the LLM Model

Edit ui/app.py to use a different model:

# Current: DeepSeek R1 32B
model = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"

# Alternative: Llama 3
model = "meta-llama/Llama-3-70b-chat-hf"

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🐛 Troubleshooting

Issue: UI Can't Connect to vLLM

Solution:

# Check vLLM endpoint
kubectl get ingress vllm-deepseek-32b-lws-ingress

# Test connectivity
curl -X POST http://$VLLM_ENDPOINT/v1/models

# Check UI logs
aws ecs describe-tasks --cluster fraud-detection-cluster \
  --tasks $(aws ecs list-tasks --cluster fraud-detection-cluster \
  --service-name fraud-detection-ui --query 'taskArns[0]' --output text)

Issue: MCP Services Not Responding

Solution:

# Check service status
aws ecs describe-services --cluster fraud-detection-cluster \
  --services transaction-risk identity-verifier geolocation-checker

# Check CloudWatch logs
aws logs tail /ecs/mcp-fraud-detection --follow

Issue: High Latency (>5 seconds)

Possible Causes: 1. Cold start - First request takes longer (warmup) 2. Network latency - Check ALB → EKS connectivity 3. GPU memory - Model might be swapping

Solutions:

# Check GPU utilization
kubectl exec -it vllm-deepseek-32b-lws-0 -- nvidia-smi

# Check vLLM logs for OOM errors
kubectl logs vllm-deepseek-32b-lws-0 | grep -i "memory"

# Reduce batch size in vLLM config
kubectl edit statefulset vllm-deepseek-32b-lws

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💰 Cost Optimization

Current Demo Configuration

Resource	Type	Qty	Cost/Hour	Daily Cost
EKS Control Plane	Managed	1	$0.10	$2.40
p4d.24xlarge	GPU Node	2	$32.77	$1,572.96
FSx Lustre	Storage	1.2TB	$0.14	$3.36
ECS Fargate	vCPU/Memory	7 tasks	~$0.15	~$3.60
ALB	Load Balancer	2	$0.023	$1.10
Data Transfer	Regional	-	~$0.02	~$0.50
			Total	~$1,584/day

Cost Reduction Strategies

1. Use g6.12xlarge instead of p4d.24xlarge
2. Cost: $5.67/hr vs $32.77/hr (83% reduction)
3. Trade-off: 4 GPUs vs 8 GPUs per node

4. Suitable for: Single-node deployments

5. Use Spot Instances

6. Savings: Up to 70%

7. Trade-off: Potential interruptions

8. Scale Down When Not in Use

   # Scale nodegroup to 0
   eksctl scale nodegroup --cluster=vllm-cluster \
     --name=vllm-p4d-nodes-efa --nodes=0
   
   # Stop ECS services
   aws ecs update-service --cluster fraud-detection-cluster \
     --service fraud-detection-ui --desired-count 0
   

9. Use Savings Plans

10. 1-year commitment: 40% savings
11. 3-year commitment: 60% savings

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🧹 Cleanup

Option 1: Keep vLLM, Remove Demo Only

cd fraud-detection-demo/scripts
./cleanup-ecs-only.sh

Option 2: Complete Cleanup (Everything)

# Delete ECS resources
cd fraud-detection-demo/scripts
./cleanup.sh

# Delete EKS cluster (from parent directory)
cd ../../
./cleanup.sh

Estimated Time: 15-20 minutes

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🤝 Contributing

This demo is part of the AWS samples repository. To contribute:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Submit a pull request

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📄 License

This sample code is made available under the MIT-0 license. See the LICENSE file.

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🙋 Support & Questions

• GitHub Issues: aws-samples/sample-aws-deep-learning-containers
• AWS Support: For production deployments, consider AWS Enterprise Support
• Community: Join the AWS ML Community

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📚 Additional Resources

AWS Documentation

• Amazon EKS User Guide
• AWS Deep Learning Containers
• Amazon ECS Developer Guide
• FSx for Lustre

vLLM Resources

• vLLM Official Documentation
• vLLM GitHub Repository
• Model Context Protocol

Blog Posts & Tutorials

• Deploying vLLM on Amazon EKS
• Building AI Agents with MCP
• GPU Optimization Best Practices

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✨ What's Next?

Production Enhancements

1. Multi-Model Ensemble - Combine multiple models for better accuracy
2. Real-time Streaming - Integrate with Amazon Kinesis
3. Auto-Scaling - Dynamic scaling based on transaction volume
4. Multi-Region - Deploy across regions for disaster recovery
5. Advanced Security - Add AWS WAF, encryption at rest/transit

Extended Use Cases

• Credit Card Fraud Detection - Real-time transaction monitoring
• Insurance Claims Fraud - Automated claim verification
• Healthcare Billing - Detect anomalous billing patterns
• E-commerce Fraud - Protect online marketplaces
• Identity Theft Prevention - Monitor account takeovers

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Built with ❤️ for Re:Invent 2025

Questions? Reach out to the AWS ML team or open an issue!