kebnecode/Ai_fruad

This proje is an AI_fruad System to dictect ghost worker in Nigeria Government # AI Fraud Detection System for Government Payments A comprehensive web application that uses Artificial Intelligence and Machine Learning to detect fraud in government payments. The system identifies fake beneficiaries, duplicate salaries, and suspicious transactions. ## 🚀 Features ### Core Capabilities - **Fake Beneficiary Detection**: Identify fraudulent beneficiaries using AI-powered validation - **Duplicate Salary Detection**: Find duplicate salary payments using fuzzy matching algorithms - **Transaction Anomaly Detection**: Detect suspicious transactions using machine learning - **Real-time Alerts**: Instant notifications for high-risk activities - **Comprehensive Reports**: Generate detailed fraud reports with actionable insights - **Audit Trail**: Complete tracking of all system activities ### Technology Stack - **Frontend**: HTML5, CSS3, JavaScript, Bootstrap 5, Chart.js - **Backend**: PHP 7.4+ with PDO - **Database**: MySQL 5.7+ - **ML Service**: Python 3.8+, Flask, Scikit-learn, XGBoost - **Integration**: REST API architecture ## 📋 System Requirements - PHP 7.4 or higher - MySQL 5.7 or higher - Python 3.8 or higher - Apache/Nginx web server (XAMPP/WAMP for Windows) - cURL extension enabled in PHP - Modern web browser (Chrome, Firefox, Edge) ## 🛠️ Installation Guide ### Step 1: Setup Database 1. Start your MySQL server (via XAMPP/WAMP) 2. Open phpMyAdmin or MySQL command line 3. Import the database schema: ```bash mysql -u root -p < database/schema.sql ``` 4. Import sample data (optional): ```bash mysql -u root -p < database/sample_data.sql ``` ### Step 2: Configure Database Connection Edit `includes/database.php` and update database credentials if needed: ```php define('DB_HOST', 'localhost'); define('DB_NAME', 'ai_fraud_detection'); define('DB_USER', 'root'); define('DB_PASS', ''); ``` ### Step 3: Setup Python ML Service 1. Navigate to the ml_service directory: ```bash cd ml_service ``` 2. Create a virtual environment (recommended): ```bas …