FASTAPI Banking API with AI&ML Transaction Fraud Detection

Learn FastAPI, MLFlow, AI/ML, Docker, Celery etc, to build a banking API with transaction fraud detection

Welcome to this comprehensive course on building a  banking API with FastAPI with an AI-powered/machine learning transaction analysis and fraud detection system. This course goes beyond basic API development to show you how to architect a complete banking system that's production-ready, secure, and scalable.

What Makes This Course Unique:

• Learn to build a real-world banking system with FastAPI and SQLModel

• Implement AI/ML-powered fraud detection using MLflow and scikit-learn

• Master containerization with Docker

• Master reverse proxying and load balancing with Traefik

• 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 with domain-driven design principles
✓ Implement secure user authentication with JWT, OTP verification, and rate limiting
✓ Create transaction processing with currency conversions and fraud detection
✓ Build a machine learning pipeline for real-time transaction risk analysis
✓ Deploy 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 migrations

Key Features in This Project:

• Core Banking Functionality: Account creation, transfers, deposits, withdrawals, statements

• Virtual Card Management: Card creation, activation, blocking, and top-ups

• User Management: Profiles, Next of Kin information, KYC implementation

• AI/ML-Powered Fraud Detection: ML-based transaction analysis and fraud detection

• Background Processing: Email notifications, PDF generation, and ML training

• Advanced Deployment: Container orchestration, reverse proxying, and high availability

• ML Ops: Model training, evaluation, deployment, and monitoring

This course is perfect For:

• Backend developers with at least 1 year of experience, looking to build secure fintech solutions.
• Tech leads planning to architect fintech solutions.

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

Technologies You'll Master:

• FastAPI & SQLModel: For building high-performance, type-safe APIs

• Docker & Traefik: For containerization and intelligent request routing

• Celery & RabbitMQ: For distributed task processing

• PostgreSQL & Alembic: For robust data storage and schema migrations

• Scikit-learn: For machine learning.

• MLflow: For managing the machine learning lifecycle

• Pydantic V2: For data validation and settings management

• JWT & OTP: For secure authentication flows

• Cloudinary: For handling image uploads

• Rate Limiting: For API protection against abuse

No more basic tutorials - let's build something real!