FraudShield AI - Fraud Detection System

FraudShield AI is a comprehensive fraud detection system built using the MERN stack (MongoDB, Express, React, Node.js) with integrated machine learning capabilities. This system helps businesses detect and prevent fraudulent transactions in real-time.

Features

• Real-time transaction monitoring and risk assessment
• AI-powered fraud detection using machine learning models
• User-friendly dashboard with data visualization
• Alert management system for suspicious activities
• Detailed transaction analysis and reporting
• Role-based access control (admin, analyst, user)
• Audit trail for all system activities

Project Structure

The project is organized into three main components:

fraud-detection-ai/
├── frontend/          # React frontend
├── backend/           # Express server
└── ai/                # AI/ML components

Frontend (React)

The frontend provides a user interface for viewing and managing fraud alerts, user authentication, dashboards, and reports.

Backend (Express + MongoDB)

The backend provides API endpoints, business logic, and database interaction using MongoDB.

AI Component

The AI component contains the machine learning models for fraud detection, data processing, and analysis.

Getting Started

Prerequisites

• Node.js (v14 or higher)
• MongoDB
• npm or yarn

Installation

1. Clone the repository

   git clone https://github.com/yourusername/fraudshield-ai.git
   cd fraudshield-ai
   

2. Install backend dependencies

   cd backend
   npm install
   

3. Install frontend dependencies

   cd ../frontend
   npm install
   

4. Configure environment variables

   • Copy the .env.example file to .env in the backend directory
   • Update the variables as needed

5. Start the development servers

   Backend:

   cd backend
   npm run dev
   

   Frontend:

   cd frontend
   npm start
   

6. Open your browser and navigate to http://localhost:3000

API Documentation

The backend provides the following API endpoints:

• Auth API: User registration, login, profile management
• Transaction API: Transaction monitoring and risk assessment
• Alert API: Alert management and processing
• Report API: Data analysis and reporting
• Dashboard API: Summary statistics and visualizations

AI Models

The fraud detection system uses several machine learning models:

1. Transaction Risk Assessment: Evaluates the risk level of each transaction
2. User Behavior Analysis: Detects unusual patterns in user behavior
3. Anomaly Detection: Identifies outliers in transaction data