AI-Powered Financial Fraud Detection System

This project is a full-stack application that demonstrates how to use AI and machine learning for detecting fraudulent financial transactions. It consists of a React frontend and a Python Flask backend with a machine learning model.

Features

• Machine learning-based fraud detection
• Real-time transaction analysis
• Batch processing of transaction data
• Training mode for model improvement
• Interactive dashboard for visualization

Project Structure

• project/src/ - React frontend application
• project/backend/ - Python Flask API for ML model

Prerequisites

• Node.js and npm
• Python 3.7+
• Required Python packages: flask, flask-cors, pandas, numpy, scikit-learn, joblib

Setup Instructions

Backend (Flask API)

1. Install Python dependencies:

   cd project/backend
   pip install -r requirements.txt

2. Start the Flask server:

   python app.py

   The API server will run on http://localhost:5000

Frontend (React)

1. Install Node.js dependencies:

   cd project
   npm install

2. Start the React development server:

   npm run dev

   Or build for production:

   npm run build

3. For a production build, serve the static files:

   cd dist
   python -m http.server 8000

   The React app will run on http://localhost:8000 (or port 5173 in development mode)

Using the Application

Training Mode

1. Switch to "Training Mode" in the top right corner
2. Upload a CSV file containing transaction data with fraud labels
3. The model will train and display metrics

Prediction Mode

1. You can analyze transactions in two ways:

   • Individual transaction analysis: Fill out the form with transaction details
   • Batch analysis: Upload a CSV file with multiple transactions

2. View the results showing fraud probabilities and confidence scores

About the Machine Learning Model

The application uses a Random Forest classifier to detect fraudulent transactions. It analyzes patterns in transaction amounts, account balances, transaction types, and balance discrepancies.