FastAPI & Machine Learning: Build a Banking Fraud Detection

About this course

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 TraefikHandle high-volume transactions with Celery, Redis, and RabbitMQSecure 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 implementationAI/ML-Powered Fraud Detection: ML-based transaction analysis and fraud detection

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