Fraud Detection in Financial Transactions

This project leverages machine learning techniques to detect fraudulent transactions in financial data. By identifying unusual patterns and suspicious activities, it helps to mitigate financial losses and enhance transaction security.

Features

• Real-time detection of fraudulent transactions in financial data.
• Identifies patterns indicative of fraud, such as unusual spending behavior.
• Uses machine learning models like Random Forest, XGBoost, and Neural Networks for accuracy.
• Provides risk scores and alerts for flagged transactions.

Installation

To set up the Fraud Detection system on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/fraud-detection.git
   cd fraud-detection

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To start detecting fraudulent activities in transaction data, run the main script:

python app.py

Monitor transactions in real-time and receive alerts for suspicious activities.

Results

The system accurately identifies fraudulent transactions, providing detailed reports and actionable insights to prevent financial loss.