Credit card fraud detect is a major concern for financial institutions and consumers worldwide. With the rapid growth of online transactions and digital payments, fraudulent activities have become more sophisticated, causing significant financial losses. The Credit Card Fraud Detection System aims to automatically detect potentially fraudulent transactions before they cause harm.
The system uses a machine learning-based approach to identify patterns of fraudulent behavior. It takes into account various factors such as transaction amount, country, time, and merchant category. These factors are analyzed to predict whether a transaction is legitimate or fraudulent. The project also includes a Django web interface where users can log in, test transactions, and administrators can monitor flagged activities.
Key Features of the System
1. Administrator Features
Administrators hold the highest level of access in the system. To maintain integrity, only registered and verified admin users can log in and manage the application.
Key Features:
- User Registration & Management: Admins can view all registered users, assign or update their roles (e.g., promoting a user to Analyst or Reviewer), and manage profiles.
- Access Control: Admins ensure that only authorized individuals can access sensitive features like fraud review or model retraining.
- Model Management: Admins can upload CSV files to train or retrain the fraud detection model.
- System Monitoring: Admins can view all flagged transactions, system logs, and reports for analysis.
This ensures complete control over both users and data security within the system.
2. Analyst Features
Analysts are registered users who focus on data processing and model operations.
Key Features:
- Secure Login: Analysts must log in using their credentials to gain access to data analytics tools and model training pages.
- Model Training and Evaluation: Analysts can upload new transaction data in CSV format to retrain or test the model.
- Fraud Prediction Testing: Analysts can use the “Check Transaction” page to analyze transaction details and review fraud probability.
- Data Insights: Analysts observe fraud trends and performance metrics to refine prediction accuracy.
By enforcing login, all analyst activities are tracked, ensuring accountability and secure data handling.
3. Reviewer Features
Reviewers play a critical role in validating flagged transactions. Only registered reviewers can log in to access flagged data.
Key Features:
- Fraud Verification: Reviewers can view all transactions flagged by the system’s machine learning model.
- Decision Logging: After analyzing a transaction, reviewers can mark it as legitimate or fraudulent.
- Coordination with Analysts: Their feedback helps improve future model accuracy.
Registration and login ensure that only authorized reviewers can access sensitive fraud data, preventing misuse.
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