FASTAPI Banking API with AI&ML Transaction Fraud Detection

FASTAPI Banking API with AI&ML Transaction Fraud Detection | Udemy [Update 09/2025]
English | Size: 24.8 GB
Genre: eLearning

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

What you’ll learn
You will learn how to integrate Docker with Celery, Redis,RabbitMQ, FlowermMLFlow and FastAPI
You will learn how to use scikit learn,numpy and pandas for machine learning, to create a transaction analysis and Fraud detection system
You will learn how to use mlflow to create machine learning training pipelines and lifecycle management
You will learn how to use Reverse Proxies and load balancing with TRAEFIK
You will learn how manage multiple Docker containers with Portainer in development and in Production
You will learn how to use Loguru for comprehensive Logging
You will learn how to use Redis,RabbitMQ and celery for background machine learning task processing.

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!

Who this course is for:
Python Developers,curious about building a Fintech API’s
Intermediate Python Developers with at least 1 year of experience, more is better
Intermediate Python Develpers curious about machine learning applications in real world projects.

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