Dark Mode
Advanced Transaction Fraud Analysis Data
Finance & Banking Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This high-fidelity dataset captures credit card transactions from 2023, designed to support and elevate fraud detection research. It contains a range of anonymised features related to individual transactions, including time, location, and type, alongside the transaction amount. A key component is the 'Class' attribute, which categorises each transaction as either legitimate or fraudulent. This classification is instrumental for training and evaluating models aimed at identifying suspicious activities and helping financial institutions mitigate the risks associated with credit card fraud.
Columns
- ID: A unique identifier for each specific credit card transaction, used for precise record-keeping.
- V1-V28: A set of anonymised features associated with each transaction. These may represent various details like time, location, and other parameters crucial for fraud detection analysis.
- Amount: The monetary value of the transaction, indicating the charge or credit to the card.
- Class: A pivotal attribute that classifies transactions into distinct groups, such as 'legitimate' or 'fraudulent', which is crucial for identifying suspicious activity.
Distribution
The dataset is distributed as a single CSV file named
creditcard_2023.csv with a size of 324.81 MB. It contains approximately 569,000 records, with 31 columns in total. The data is reported to be 100% valid with no missing or mismatched values.Usage
This dataset is ideal for a variety of data-driven applications, including:
- Developing and testing machine learning models for fraud detection.
- Conducting exploratory data analysis (EDA) to understand transaction patterns.
- Creating data visualisations to identify anomalies and trends.
- Academic and commercial research within finance and computer science.
Coverage
The dataset focuses on credit card transactions from the year 2023. Specific geographic or demographic scope is not detailed in the provided information.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Machine Learning Engineers: For building and refining fraud detection algorithms.
- Financial Analysts: To study transaction patterns and identify potential risk factors.
- Academic Researchers: For studies in finance, data science, and cybersecurity.
- Students: As a practical dataset for projects related to data analysis, visualisation, and machine learning.
Dataset Name Suggestions
- 2023 High-Fidelity Fraudulent Transaction Data
- Credit Card Fraud Detection Dataset 2023
- Advanced Transaction Fraud Analysis Data
- Financial Transactions Anomaly Dataset 2023
Attributes
Original Data Source: Advanced Transaction Fraud Analysis Data
QUALITY