license:mit size_categories:n<1K format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us infinite-dataset-hub synthetic
---
license: mit
tags:
- infinite-dataset-hub
- synthetic
---

# PaymentAnomalyScan

tags: finance, fraud detection, machine learning

_Note: This is an AI-generated dataset so its content may be inaccurate or false_

**Dataset Description:**

The 'PaymentAnomalyScan' dataset is a curated collection of financial transactions with labels indicating the likelihood of fraudulent activity. This dataset is tailored for training machine learning models to detect anomalies in payment patterns. Each record in the dataset represents a unique transaction and includes a variety of features that are commonly used in fraud detection, such as transaction amount, merchant category, location, and user behavior patterns. The dataset includes both normal transactions and transactions flagged as potential fraud. Labels in the dataset are 'Normal' for typical transactions and 'Fraud' for transactions that exhibit unusual characteristics that could indicate fraudulent activity.

**CSV Content Preview:**

```csv
transaction_id,user_id,transaction_amount,merchant_category,location,transaction_timestamp,is_fraudulent,risk_score
TX10001,USR001,150.00,Electronics,Online,2023-01-15T08:30:00Z,False,0.1
TX10002,USR002,3000.00,Travel,Hotel,2023-01-15T09:15:00Z,True,0.9
TX10003,USR003,50.00,Groceries,Local,2023-01-15T10:05:00Z,False,0.05
TX10004,USR004,1200.00,Electronics,Online,2023-01-15T11:45:00Z,True,0.85
TX10005,USR005,200.00,Clothing,Local,2023-01-15T12:30:00Z,False,0.15
```

This dataset features 5 rows with each row representing a unique transaction, including whether the transaction is fraudulent. The 'is_fraudulent' column is a binary label indicating if the transaction is fraudulent ('True') or not ('False'), and the 'risk_score' is a continuous value that quantifies the likelihood of fraud, with higher values indicating a higher risk. This is a simplified example, and in a full dataset, there would likely be many more features and a much larger number of transactions.

**Source of the data:**

The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'PaymentAnomalyScan':

- **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=PaymentAnomalyScan&dataset=PaymentAnomalyScan&tags=finance,+fraud+detection,+machine+learning
- **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
- **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub
