| # Fraud Detection Synthetic Dataset | |
| ## Dataset Description | |
| This is a synthetic dataset for fraud detection created for the XNL LLM Task 3 challenge. It contains transaction data with labeled fraud cases. | |
| ### Dataset Summary | |
| - Number of transactions: 34767 | |
| - Fraud rate: 10.19% | |
| - Generated using the Synthetic Data Generator tool | |
| ### Data Fields | |
| - `transaction_id`: Unique identifier for each transaction | |
| - `user_id`: User who made the transaction | |
| - `timestamp`: When the transaction occurred | |
| - `amount`: Transaction amount | |
| - `merchant`: Where the transaction occurred | |
| - `description`: Text description of the transaction | |
| - `transaction_type`: Type of transaction (purchase, subscription, etc.) | |
| - `device`: Device used for the transaction | |
| - `ip_address`: IP address (for online transactions) | |
| - `location`: Geographic location | |
| - `is_fraud`: Target variable - indicates if the transaction is fraudulent (1) or legitimate (0) | |
| ## Additional Information | |
| This dataset was generated using a synthetic data generator that creates realistic transaction patterns with embedded fraud signals. The data can be used for training and testing fraud detection models. | |
| ## Argilla Integration | |
| This dataset is also available on Argilla as `fraud-detection-transactions` for interactive exploration and labeling. | |