Building a real Banking API with with AI/ML Fraud Detection. (FastAPI, Docker, Celery, RabbitMQ etc)

Description

Take a full architectural tour of NextGenBank — a production-style banking API built with FastAPI, Docker, Celery, Redis, and RabbitMQ, with a real AI/ML transaction analysis and fraud detection pipeline layered on top. In this video I walk through every major component of the system: how requests flow from the API layer into the async task workers, how transactions are scored for fraud in near real-time using a gradient-boosting model, how the message broker and cache fit together, and how the whole thing is containerized for local development and deployment. If you've ever wondered how the backend of a modern fintech app is actually wired together — auth, accounts, transactions, async processing, ML inference, observability — this is the breakdown for you. 🎓 Full hands-on course on Udemy: 👉 [https://www.udemy.com/course/fastapi-banking-with-ai-ml-fraud-detection/?referralCode=6AF6C11C157E9FE26090] 💻 Source code on GitHub: 👉 https://github.com/API-Imperfect/nextgenbank-fastapi 🧰 Tech stack covered FastAPI (async Python web framework) PostgreSQL + SQLAlchemy + Alembic Celery for background tasks Redis as cache & Celery backend RabbitMQ as the message broker Docker & docker-compose scikit-learn / gradient boosting for fraud detection Pydantic, JWT auth, and more 👨‍💻 Who this is for Backend developers, Python engineers, and fintech-curious builders who want to see how a real banking-style API is structured end-to-end — not just a CRUD demo. #FastAPI #Python #BankingAPI #FraudDetection #MachineLearning #SystemDesign #Backend #Docker #Celery #RabbitMQ #Fintech #Microservices #AI