FadeFraud is an ML model that evaluates transactions for fraud using a specific set of rules and a Random Forest Regression algorithm to determine a fraud score for each transaction and the reasons why. This model is integrated into 2 projects:

This application tracks credit card transactions in real-time using the Plaid API and evaluates them for fraud using the model. The model is hosted as a RESTful API using Flask. If a transaction is flagged as fraudulent, the system immediately notifies the user via email.

This application uploads a CSV of secure transactions made by users to the dashboard and uses the model, also hosted as a RESTful API using Flask, to evaluate them all at once. The transactions are then separated by high, medium, and low risk, and the reasons why each transaction is flagged.

• Real-time transaction monitoring via Plaid API
• Machine learning-based fraud detection using a trained model
• RESTful API for seamless integration
• Email alerts for fraudulent transactions

• Backend: Python, Flask
• Machine Learning: scikit-learn
• Data Processing: pandas, NumPy
• API Integration: Plaid API
• Email Notification: SMTP

• frontend: Branch for frontend of corporate facing product.
• backend: Branch for backend of corporate facing product.
• storefront: Branch for frontend of the test storefront and the ML model for both products.

1. Transaction Retrieval: The system retrieves real-time credit card transactions from Plaid.
2. Fraud Analysis: Transactions are sent to the machine learning model via a Flask-based REST API.
3. Fraud Detection Decision: If the transaction is classified as fraudulent, an alert is generated.
4. User Notification: The system sends an email to the user with transaction details and fraud alert information.

This project is licensed under the MIT License. See LICENSE for more details.