FraudShield AI — Intelligent Credit Card Fraud Detection & Transaction Monitoring
AI-powered web application that detects credit card fraud using a trained XGBoost machine learning model.
Project overview
What this project is about.
FraudShield AI is a web application that helps identify potentially fraudulent credit card transactions using machine learning.
The application uses a trained Weighted XGBoost model to analyze transaction data and generate a fraud probability along with a Fraud or Legitimate result. Each analyzed transaction can be saved and viewed later for monitoring and analysis.
The backend is built with Django and Django REST Framework, with SQLite used for storing transaction records. The frontend is built using HTML, CSS, and JavaScript with a responsive and user-friendly interface.
The project includes a transaction analysis page, monitoring dashboard, transaction history, REST API, sample transaction loader, and detailed developer documentation.
The machine learning model was trained using the ULB Credit Card Fraud Detection dataset, which contains 284,807 transactions, including 492 fraudulent transactions.
The final model achieved:
- ROC-AUC: 0.980
- PR-AUC: 0.883
- Precision: ~0.90
- Recall: ~0.847
- F1-score: ~0.874
The model uses a fixed decision threshold of 0.65 to classify transactions as Fraud or Legitimate.
Who it is for
Usage and use case
- 1. Open the Analyze page.
- 2. Choose whether to load a sample transaction from the dataset or enter the transaction features manually.
- 3. If entering a transaction manually, provide the required 30 model features:
- Time, V1-V28, and Amount.
- 4. Click the Analyze Transaction button.
- 5. FraudShield AI sends the transaction to the backend and passes it through the trained XGBoost model.
- 6. The system returns a fraud probability and classifies the transaction as Fraud or Legitimate.
- 7. The analyzed transaction is automatically saved for future reference.
- 8. Open the Transactions page to search, sort, filter, and view previously analyzed transactions.
- 9. Open the Dashboard to view transaction statistics, fraud rate, recent activity, and probability charts.
- 10. Open the Documentation page to learn about the system architecture, machine learning model, and available REST API endpoints.
Built with
Technology stack
- Python
- Django
- Django REST Framework
- SQLite
- XGBoost
- scikit-learn
- Pandas
- NumPy
- Joblib
- HTML5
- CSS3
- Vanilla JavaScript
What you get
Key features
- AI-based credit card fraud prediction using a Weighted XGBoost model
- Fraud probability and risk-level scoring
- Persistent transaction history
- Searchable transaction records
- Sortable and filterable transactions
- Live analytics dashboard
- Fraud-rate monitoring
- Fraud probability distribution charts
- High-risk transaction highlighting
- REST API for fraud prediction
- REST API for transaction history
- REST API for dashboard analytics
- Sample transaction loader using real dataset rows
- Developer documentation with API reference
- System architecture documentation
- Responsive custom-built interface
- Light and dark mode support
Before you start
Requirements
Python 3.10+
Django
Django REST Framework
XGBoost
scikit-learn
Pandas
NumPy
Joblib
SQLite
HTML5
CSS3
JavaScript
Modern Web Browser
Optional extras
Boost your project
Optional extras to help you document, present and understand your work. Mention the ones you like when you enquire.
-
Popular Documentation support Submission-ready project report. - Full project report (PDF and Word)
- Setup guide and README
- Diagrams and screenshots
-
Presentation support Slides ready for your review. - Slide deck of 12 to 15 slides
- Speaker notes
- Demo walkthrough script
-
Publication support Guidance to turn it into a paper. - Paper structure and abstract
- Journal and format guidance
- Reference and plagiarism-check help
-
1:1 mentorship Live help from a mentor. - 45-minute video call
- Code walkthrough
- Viva question practice
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Can a project be changed to fit my requirements?
Yes. Describe the changes you need when you enquire, and we confirm the scope and the price before any work starts.
Are add-ons available?
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Is there a demo?
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Can I get a refund?
Projects are digital, so payments are generally not refundable. If something went wrong, send a refund request with your order reference and we review it fairly. The Refund Policy explains the details.
