FraudGuard — AI Fraud Detection (Microservices on AWS)

A high-performance fraud detection system built with a microservices architecture, AWS integration, and real-time AI-powered scoring.

Live Demo: https://d49m8z8w0jzwy.cloudfront.net

Architecture

Screenshots

Services

Service Name	Tech Stack	Port	Purpose
Auth Service	Node, Express, MongoDB	3001	User registration, login, JWT auth
Transaction Service	Node, Express, MongoDB	3002	CRUD for transactions, pushes to SQS
AI Scoring Service	Node, Gemini API	3003	Async worker, scores transactions via AI
Notification Service	Node, Express, MongoDB	3004	Audit logs and real-time notifications

AWS Architecture

• Amazon ECS (Fargate): Container orchestration for the 4 microservices
• Amazon SQS: Message queuing for asynchronous communication between services
• Application Load Balancer (ALB): Routes traffic to appropriate ECS tasks
• Amazon ECR: Private container registry for Docker images
• Amazon S3 & CloudFront: Hosting and CDN for the React frontend
• AWS Secrets Manager: Secure storage of environment variables

Tech Stack

Category	Technologies
Frontend	React.js, Tailwind CSS, Redux Toolkit, Recharts
Backend	Node.js, Express.js
Database	MongoDB Atlas
Cloud	AWS (ECS, SQS, S3, CloudFront, ALB), Docker
AI	Google Gemini API
CI/CD	GitHub Actions

Local Setup

1. Clone the repository

   git clone https://github.com/yourusername/FraudGuard.git
   cd FraudGuard

2. Set up Environment Variables Create a .env file in each service directory (auth-service, transaction-service, ai-scoring-service, notification-service) with the required keys (see below).

3. Run with Docker Compose

   docker-compose up --build

4. Access the application

   • Frontend: http://localhost:5173
   • APIs: http://localhost:3001 to 3004

Environment Variables Needed

Auth Service: PORT, MONGO_URI, JWT_SECRET Transaction Service: PORT, MONGO_URI, SQS_QUEUE_URL, RAZORPAY_KEY_ID, RAZORPAY_KEY_SECRET AI Scoring Service: PORT, MONGO_URI, SQS_QUEUE_URL, GEMINI_API_KEY Notification Service: PORT, MONGO_URI, SQS_QUEUE_URL

CI/CD Pipeline

The project uses GitHub Actions for automated deployments:

• Backend Services: Any push modifying the services/ folder triggers a build and push to AWS ECR, followed by an ECS task update.
• Frontend: Pushes to the client/ folder trigger an optimized build, syncs with the S3 bucket, and invalidates the CloudFront cache.

Future Improvements

• Add WebSocket support for real-time dashboard updates
• Implement a caching layer with Redis to optimize frequent queries
• Add multi-factor authentication (MFA) for analyst accounts
• Introduce an automated retry mechanism for failed AI scoring attempts

Author

[Your Name] LinkedIn | GitHub