Final_fraud_detection

This project aims to detect fraudulent credit card transactions using machine learning algorithms. The system incorporates various features such as biometric authentication, data visualizations, synthetic data testing, location-based fraud detection, and secure databases to ensure robust fraud detection capabilities.

Features

1. Biometric Authentication

   • Ensures secure access to the fraud detection page.

2. Data Visualizations

   • Confusion Matrix: Displays true positives, false positives, true negatives, and false negatives.
   • Bar Plots: Shows the distribution of fraudulent and non-fraudulent transactions.
   • Line Charts: Tracks transaction trends and anomalies over time.

3. Model Accuracy

   • The machine learning model achieves an accuracy of 0.99, indicating high reliability in detecting fraudulent transactions.

4. Synthetic Data Testing

   • Synthetic data with 10 transactions is generated for testing. Normal transactions are highlighted in green, and fraudulent transactions in red. Transaction IDs detected as fraud are also displayed.

5. Location-Based Fraud Detection

   • Detects fraud based on the user's current location. Any transaction initiated from a location other than Kolkata is flagged as potentially fraudulent.

6. Company-Based Fraud Detection

   • Uses a dataset of company names commonly reported for involvement in scams. Transactions associated with companies from this dataset are considered fraudulent.

7. Real-Time Alerts

   • If fraud is detected, an alert message is sent to the user's registered phone number with relevant transaction details.

8. Multiple Transactions Detection

   • Detects fraud when multiple transactions of the same amount (less than the amount required for OTP) are made back-to-back.

9. Secure Database

   • Ensures that all transaction data is stored securely.