🚀 Just Launched: End-to-End Real-Time Fraud Detection System! 🚀

I'm excited to share my latest project—a full-stack, real-time fraud detection platform built from scratch! This project demonstrates my expertise in data engineering, machine learning, backend API development, and modern frontend engineering.

What I built:

🔹 Data Engineering & Feature Engineering:

• Designed a robust PostgreSQL schema to store and manage transaction data.
• Engineered features such as transaction statistics, user behavior metrics, and categorical encodings to maximize fraud detection accuracy.

🔹 Machine Learning:

• Developed and trained an unsupervised Isolation Forest model for anomaly detection using scikit-learn.
• Built a preprocessing pipeline with scaling and one-hot encoding to ensure consistent, production-ready inference.
• Automated model serialization and deployment using joblib.

🔹 Backend API (FastAPI):

• Built RESTful endpoints for transaction ingestion and real-time fraud scoring.
• Integrated the ML model and preprocessing pipeline for seamless, low-latency predictions.
• Implemented robust error handling, CORS support, and database integration.

🔹 Frontend (React):

• Developed an intuitive dashboard to visualize transactions, fraud alerts, and scoring results in real time.
• Built a transaction scoring form for instant feedback and model transparency.
• Leveraged Axios for smooth API communication and React hooks for state management.

🔹 Testing & DevOps:

• Wrote comprehensive unit tests for both backend and frontend.
• Used environment variables and .env files for secure, configurable deployments.
• Ensured full system alignment with automated integration tests.

Tech Stack:
Python, FastAPI, scikit-learn, PostgreSQL, React, Axios, Docker, pgAdmin

Key Skills Demonstrated:

• End-to-end ML system design
• API development & integration
• Database modeling
• Frontend engineering
• Automated testing & CI/CD best practices

This project is an end-to-end real-time fraud detection platform featuring a machine learning backend (Python/FastAPI), a PostgreSQL database, and a modern React frontend dashboard.

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🚀 Features

• Real-time transaction ingestion and scoring
• Isolation Forest anomaly detection model
• Feature engineering and preprocessing pipeline
• RESTful API with FastAPI
• PostgreSQL for persistent storage
• Interactive React dashboard for monitoring and scoring
• Unit and integration tests

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🛠️ Prerequisites

• Python 3.8+
• Node.js & npm
• PostgreSQL
• (Optional) Redis (if using caching)
• pgAdmin or psql for database management

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⚡ Quick Start

1. Clone the Repository

git clone https://github.com/uzumstanley/AI-Powered-Real-Time-Fraud-Detection-System.git
cd AI-Powered-Real-Time-Fraud-Detection-System

2. Set Up the Database

• Create a PostgreSQL database (e.g., frauddb).
• Create a user (e.g., mac) with access.
• Run the schema:

psql -U mac -d frauddb -f app/Database/schema.sql

3. Configure Environment Variables

Edit app/.env:

DATABASE_URL=postgresql://mac@localhost:5432/frauddb
JWT_SECRET=your_jwt_secret_here

4. Install Python Dependencies

cd app
pip install -r ../requirements.txt

5. Start the Backend

uvicorn main:app --reload

The API will be available at http://127.0.0.1:8000.

6. Run Tests (Optional)

pytest ../tests/test_main.py

7. Start the Frontend

cd ../frontend
npm install
npm start

The dashboard will be available at http://localhost:3000.

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🖥️ Usage

• Score a transaction: Use the form on the dashboard or send a POST request to /transactions/score with JSON data.
• View transactions: The dashboard lists recent transactions and fraud alerts.
• API docs: Visit http://127.0.0.1:8000/docs for interactive API documentation.

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📄 Sample Transaction JSON

{
  "account_id": "test_account_123",
  "TransactionAmount": 420.50,
  "CustomerAge": 35,
  "TransactionDuration": 6.2,
  "LoginAttempts": 1,
  "AccountBalance": 15000.0,
  "user_transaction_count": 8,
  "user_avg_transaction_amount": 400.0,
  "deviation_from_user_avg": 20.5,
  "transaction_hour": 10,
  "transaction_day_of_week": 1,
  "TransactionType": "Debit",
  "Location": "Chicago",
  "Channel": "Online",
  "CustomerOccupation": "Analyst",
  "user_primary_location": "Chicago",
  "is_unusual_location": "False"
}

you can score the transactions with task specific data

🧑‍💻 Project Structure

app/                # FastAPI backend and ML model
frontend/           # React frontend
tests/              # Unit and integration tests
app/Database/       # Database schema
app/model/          # Model training and conversion scripts

Here is the youtube video link where i publish the demo of the system: https://www.youtube.com/watch?v=YroGtcu9mrM

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Feel free to DM me for a walkthrough or collaboration opportunities!

Project Roadmap: https://github.com/users/uzumstanley/projects/2/views/1

🙋‍♂️ Contact

For questions or collaboration, open an issue or contact uzumstanley.