FASTAPI Banking API with AI&ML Transaction Fraud Detection

Build a complete, production-ready banking API using FastAPI and SQLModel in this comprehensive course. Go beyond the basics and learn to architect a secure and scalable financial system with integrated AI-powered fraud detection.

What Makes This Course Unique:

• Develop a real-world banking system from the ground up.
• Implement AI/ML-powered fraud detection using MLflow and scikit-learn.
• Master Docker for containerization and Traefik for reverse proxying and load balancing.
• Handle high-volume transactions with Celery, Redis, and RabbitMQ.
• Secure your API with industry-standard authentication practices.

You'll Learn How To:

• Design a robust banking API architecture using domain-driven design.
• Implement secure user authentication with JWT, OTP verification, and rate limiting.
• Create comprehensive transaction processing including currency conversions and fraud analysis.
• Build a machine learning pipeline for real-time transaction risk analysis.
• Deploy your application with Docker Compose and manage traffic with Traefik.
• Scale your application using asynchronous Celery workers.
• Monitor your system with comprehensive logging using Loguru.
• Train, evaluate, and deploy ML models with MLflow.
• Work with PostgreSQL using SQLModel and Alembic for database migrations.

Key Features of This Project:

• Core Banking Functionality: Implement account creation, transfers, deposits, withdrawals, and statement generation.
• Virtual Card Management: Develop features for card creation, activation, blocking, and top-ups.
• User Management: Build user profiles, manage next-of-kin information, and implement KYC (Know Your Customer) procedures.
• AI/ML-Powered Fraud Detection: Integrate ML-based transaction analysis for proactive fraud detection.
• Background Processing: Implement email notifications, PDF generation, and automated ML model training.
• Advanced Deployment: Utilize container orchestration, reverse proxying, and high availability strategies.
• ML Ops: Learn the complete machine learning lifecycle, from model training to deployment and monitoring.

This course is perfect for:

• Backend developers with at least 1 year of experience seeking to build secure FinTech solutions.
• Tech leads responsible for architecting FinTech solutions.

By the end of this course, you'll have built a production-ready banking system with AI capabilities, ready to showcase in your portfolio or implement in real-world projects.

Technologies You'll Master:

• FastAPI & SQLModel: Build high-performance, type-safe APIs.
• Docker & Traefik: Containerize your application and manage intelligent request routing.
• Celery & RabbitMQ: Implement distributed task processing.
• PostgreSQL & Alembic: Utilize robust data storage and schema migrations.
• Scikit-learn: Apply machine learning algorithms.
• MLflow: Manage the entire machine learning lifecycle.
• Pydantic V2: Validate data and manage settings.
• JWT & OTP: Implement secure authentication flows.
• Cloudinary: Handle image uploads.
• Rate Limiting: Protect your API from abuse.

Stop with basic tutorials and start building something real!