📌 Dataset Description

**Credit Card Fraud Detection Dataset **

This dataset contains 10,000 credit card transactions designed to support research and experimentation in fraud detection using machine learning. The data realistically simulates both legitimate and fraudulent transactions, while maintaining a naturally imbalanced class distribution, which reflects real-world financial systems.

The dataset is suitable for binary classification tasks, where the objective is to predict whether a transaction is fraudulent (1) or legitimate (0) based on transaction behavior, risk indicators, and cardholder information.

🎯 Objective

To build and evaluate machine learning models that can identify fraudulent credit card transactions using transaction-level features such as amount, time, location mismatch, device trust, and transaction velocity.

📊 Dataset Characteristics

• Total Records: 10,000
• Total Features: 10
• Target Variable: is_fraud
• Class Distribution: Highly imbalanced (fraud ≈ 4–5%)
• Data Type: Synthetic (privacy-safe)

🧾 Feature Description

🔍 Potential Use Cases

• Credit card fraud detection systems
• Imbalanced classification problems
• Feature engineering and risk analysis
• Model comparison (Logistic Regression, Random Forest, XGBoost, etc.)
• Anomaly detection research

📈 Recommended Evaluation Metrics

Due to class imbalance, the following metrics are recommended:

• Precision
• Recall
• F1-score
• ROC-AUC
• Confusion Matrix

⚠️ Notes

• This dataset is synthetically generated and does not contain any real customer data.
• It is intended for educational, research, and practice purposes only.

Below is a draft DMP–style description of your credit‐card fraud detection experiment, modeled on the antiquities example:

1. Dataset Description

Research Domain
This work resides in the domain of financial fraud detection and applied machine learning. We focus on detecting anomalous credit‐card transactions in real time to reduce financial losses and improve trust in digital payment systems.

Purpose
The goal is to train and evaluate a binary classification model that flags potentially fraudulent transactions. By publishing both the code and data splits via FAIR repositories, we enable reproducible benchmarking of fraud‐detection algorithms and support future research on anomaly detection in transaction data.

Data Sources
We used the publicly available credit‐card transaction dataset from Kaggle (original source: https://www.kaggle.com/mlg-ulb/creditcardfraud), which contains anonymized transactions made by European cardholders over two days in September 2013. The dataset includes 284 807 transactions, of which 492 are fraudulent.

Method of Dataset Preparation

1. Schema validation: Renamed columns to snake_case (e.g. transaction_amount, is_declined) so they conform to DBRepo’s requirements.

2. Data import: Uploaded the full CSV into DBRepo, assigned persistent identifiers (PIDs).

3. Splitting: Programmatically derived three subsets—training (70%), validation (15%), test (15%)—using range‐based filters on the primary key actionnr. Each subset was materialized in DBRepo and assigned its own PID for precise citation.

4. Cleaning: Converted the categorical flags (is_declined, isforeigntransaction, ishighriskcountry, isfradulent) from “Y”/“N” to 1/0 and dropped non‐feature identifiers (actionnr, merchant_id).

5. Modeling: Trained a RandomForest classifier on the training split, tuned on validation, and evaluated on the held‐out test set.

2. Technical Details

Dataset Structure

• The raw data is a single CSV with columns:

  • actionnr (integer transaction ID)

  • merchant_id (string)

  • average_amount_transaction_day (float)

  • transaction_amount (float)

  • is_declined, isforeigntransaction, ishighriskcountry, isfradulent (binary flags)

  • total_number_of_declines_day, daily_chargeback_avg_amt, sixmonth_avg_chbk_amt, sixmonth_chbk_freq (numeric features)

Naming Conventions

• All columns use lowercase snake_case.

• Subsets are named creditcard_training, creditcard_validation, creditcard_test in DBRepo.

• Files in the code repo follow a clear structure:

  ├── data/         # local copies only; raw data lives in DBRepo 
  ├── notebooks/Task.ipynb 
  ├── models/rf_model_v1.joblib 
  ├── outputs/        # confusion_matrix.png, roc_curve.png, predictions.csv 
  ├── README.md 
  ├── requirements.txt 
  └── codemeta.json 
  

Required Software

• Python 3.9+

• pandas, numpy (data handling)

• scikit-learn (modeling, metrics)

• matplotlib (visualizations)

• dbrepo‐client.py (DBRepo API)

• requests (TU WRD API)

Additional Resources

• Original dataset: https://www.kaggle.com/mlg-ulb/creditcardfraud

• Scikit-learn docs: https://scikit-learn.org/stable

• DBRepo API guide: via the starter notebook’s dbrepo_client.py template

• TU WRD REST API spec: https://test.researchdata.tuwien.ac.at/api/docs

3. Further Details

Data Limitations

• Highly imbalanced: only ~0.17% of transactions are fraudulent.

• Anonymized PCA features (V1–V28) hidden; we extended with domain features but cannot reverse engineer raw variables.

• Time‐bounded: only covers two days of transactions, may not capture seasonal patterns.

Licensing and Attribution

• Raw data: CC-0 (per Kaggle terms)

• Code & notebooks: MIT License

• Model artifacts & outputs: CC-BY 4.0

• DUWRD records include ORCID identifiers for the author.

Recommended Uses

• Benchmarking new fraud‐detection algorithms on a standard imbalanced dataset.

• Educational purposes: demonstrating model‐training pipelines, FAIR data practices.

• Extension: adding time‐series or deep‐learning models.

Known Issues

• Possible temporal leakage if date/time features not handled correctly.

• Model performance may degrade on live data due to concept drift.

• Binary flags may oversimplify nuanced transaction outcomes.

Credit Card Fraud Detection Dataset (European Cardholders, September 2013)

As a data contributor, I'm sharing this crucial dataset focused on the detection of fraudulent credit card transactions. Recognizing these illicit activities is paramount for protecting customers and the integrity of financial systems.

About the Dataset:

This dataset encompasses credit card transactions made by European cardholders during a two-day period in September 2013. It presents a real-world scenario with a significant class imbalance, where fraudulent transactions are considerably less frequent than legitimate ones. Out of a total of 284,807 transactions, only 492 are instances of fraud, representing a mere 0.172% of the entire dataset.

Content of the Data:

Due to confidentiality concerns, the majority of the input features in this dataset have undergone a Principal Component Analysis (PCA) transformation. This means the original meaning and context of features V1, V2, ..., V28 are not directly provided. However, these principal components capture the variance in the underlying transaction data.

The only features that have not been transformed by PCA are:

• Time: Numerical. Represents the number of seconds elapsed between each transaction and the very first transaction recorded in the dataset.
• Amount: Numerical. The transaction amount in Euros (€). This feature could be valuable for cost-sensitive learning approaches.

The target variable for this classification task is:

• Class: Integer. Takes the value 1 in the case of a fraudulent transaction and 0 otherwise.

Important Note on Evaluation:

Given the substantial class imbalance (far more legitimate transactions than fraudulent ones), traditional accuracy metrics based on the confusion matrix can be misleading. It is strongly recommended to evaluate models using the Area Under the Precision-Recall Curve (AUPRC), as this metric is more sensitive to the performance on the minority class (fraudulent transactions).

How to Use This Dataset:

1. Download the dataset file (likely in CSV format).
2. Load the data using libraries like Pandas.
3. Understand the class imbalance: Be aware that fraudulent transactions are rare.
4. Explore the features: Analyze the distributions of 'Time', 'Amount', and the PCA-transformed features (V1-V28).
5. Address the class imbalance: Consider using techniques like oversampling the minority class, undersampling the majority class, or using specialized algorithms designed for imbalanced datasets.
6. Build and train binary classification models to predict the 'Class' variable.
7. Evaluate your models using AUPRC to get a meaningful assessment of performance in detecting fraud.

Acknowledgements and Citation:

This dataset has been collected and analyzed through a research collaboration between Worldline and the Machine Learning Group (MLG) of ULB (Université Libre de Bruxelles).

When using this dataset in your research or projects, please cite the following works as appropriate:

• Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015.
• Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon.
• Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE.
• Andrea Dal Pozzolo. Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi).
• Fabrizio Carcillo, Andrea Dal Pozzolo, Yann-Aël Le Borgne, Olivier Caelen, Yannis Mazzer, Gianluca Bontempi. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier.
• Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Gianluca Bontempi. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing.
• Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019.
• Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi *Combining Unsupervised and Supervised...

Credit Card Fraud: Analysis and Prevention Overview Credit card fraud represents a significant threat to the integrity of financial transactions and consumer trust in digital commerce. As the reliance on credit cards for everyday purchases continues to grow, so does the sophistication of fraudsters exploiting vulnerabilities in the system. This project aims to analyze patterns of credit card fraud, understand the factors contributing to fraudulent activities, and explore effective methods for detection and prevention.

Dataset Description The dataset comprises 100,000 transactions generated to simulate real-world credit card activity. Each entry includes the following features:

TransactionID: A unique identifier for each transaction, ensuring traceability. TransactionDate: The date and time when the transaction occurred, allowing for temporal analysis. Amount: The monetary value of the transaction, which can help identify unusually large transactions that may indicate fraud. MerchantID: An identifier for the merchant involved in the transaction, useful for assessing merchant-related fraud patterns. TransactionType: Indicates whether the transaction was a purchase or a refund, providing context for the activity. Location: The geographic location of the transaction, facilitating analysis of fraud trends by region. IsFraud: A binary target variable indicating whether the transaction is fraudulent (1) or legitimate (0), essential for supervised learning models. Analysis Objectives Exploratory Data Analysis (EDA):

Examine the distribution of transaction amounts and types. Identify trends in transaction dates and locations. Analyze the ratio of fraudulent to legitimate transactions. Pattern Recognition:

Use clustering techniques to group transactions and identify unusual patterns. Explore correlations between transaction features and the occurrence of fraud. Fraud Detection Modeling:

Implement machine learning algorithms (e.g., logistic regression, decision trees, random forests) to build predictive models that can classify transactions as fraudulent or legitimate. Evaluate model performance using metrics such as accuracy, precision, recall, and the F1 score. Feature Importance Analysis:

Determine which features contribute most significantly to the detection of fraud, aiding in the refinement of fraud detection systems. Potential Solutions Real-time Monitoring Systems: Develop systems capable of analyzing transactions in real-time, flagging suspicious activities based on learned patterns and thresholds. Consumer Education: Promote awareness among consumers about the signs of credit card fraud and best practices for safeguarding personal information. Collaboration with Merchants: Work closely with merchants to implement better security measures, such as enhanced verification processes for high-risk transactions. Regulatory Compliance: Ensure compliance with regulations and standards (e.g., PCI DSS) to enhance security protocols across the payment ecosystem. Conclusion Understanding and addressing credit card fraud is vital for maintaining consumer confidence and the overall health of the financial system. Through rigorous analysis and the application of advanced machine learning techniques, this project aims to contribute valuable insights and practical solutions for combating credit card fraud effectively.

kgauvin603/creditcard-fraud-detection dataset hosted on Hugging Face and contributed by the HF Datasets community

284,807 European credit card transactions from September 2013 with PCA-anonymized features. Contains 492 frauds (0.172% fraud rate) - the gold standard benchmark for imbalanced classification and anomaly detection ML research.

An anonymized Credit Card Fraud Detection dataset designed for building, training, and evaluating machine learning models for financial fraud prevention.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

About Data

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.

Acknowledgements

The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project

Please cite the following works:

Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon

Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE

Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)

Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier

Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing

Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019

Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019

Yann-Aël Le Borgne, Gianluca Bontempi Reproducible machine Learning for Credit Card Fraud Detection - Practical Handbook

Bertrand Lebichot, Gianmarco Paldino, Wissam Siblini, Liyun He, Frederic Oblé, Gianluca Bontempi Incremental learning strategies for credit cards fraud detection, IInternational Journal of Data Science and Analytics

The dataset from https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud

The dataset contains transactions made by credit cards in September 2013 by European cardholders.
This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

The dataset is generated using the Faker library to simulate transaction data. It contains several columns that represent both user and transaction information, including features for detecting fraudulent activities. The data includes a mix of categorical, numerical, and datetime values, which need to be processed for machine learning.

Context This dataset contains simulated credit card transactions featuring both legitimate and fraudulent activities from January 1st, 2019, through December 31st, 2020. It captures the transaction behaviors of 1,000 synthetic customers interacting with a pool of 800 merchants. With roughly 1.85 million transactions in total, the dataset reflects the highly imbalanced nature of real-world financial fraud, providing features such as transaction time, amount, merchant category, and geographical coordinates.

Sources The data was synthetically generated using the Sparkov Data Generation tool created by Brandon Harris. It simulates normal user transaction habits, merchant locations, and specific fraud scenarios to create a highly realistic environment for predictive modeling. The data is available via HuggingFace (NeerajCodz/creditCardFraudDetection).

Inspiration The primary inspiration behind this dataset is to provide a robust, large-scale, and accessible resource for evaluating anomaly detection and machine learning algorithms. Credit card fraud is a massive challenge in financial cybersecurity; this dataset allows researchers, students, and data scientists to tackle extreme class imbalances, test novel transaction-monitoring strategies, and ultimately advance the state-of-the-art in automated fraud detection without compromising sensitive personal data.

Access the Credit Card Transactions Dataset featuring structured transaction records suitable for fraud detection, risk modeling, anomaly detection, and financial analytics. Designed for machine learning and AI-driven fintech applications.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The data sets contains transactions made by credit cards by cardholders. This dataset we have found 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the frauds account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA is Time and Amount. Feature Time contains the seconds between each transaction and the first transaction in the dataset. The feature Amount is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature Class is the response variable and it takes value 1 in case of fraud and 0 otherwise. Time Number of seconds elapsed between this transaction and the first transaction in the dataset V1….V28 may be result of a PCA Dimensionality reduction to protect user identities and sensitive features(v1-v28) Amount Transaction amount Class The value 1 is for fraudulent transactions, value 0 is for nonfraudulent transactions

This dataset provides detailed, labeled records of simulated credit card transactions, including transaction amounts, merchant and cardholder information, and fraud indicators. It is ideal for developing and benchmarking machine learning models aimed at detecting fraudulent activity and reducing financial risk in payment systems. The inclusion of transaction context and cardholder demographics supports advanced analytics and feature engineering.

Dataset

This dataset was created by Rupeswara Babu Sangoju

Released under Apache 2.0

Contents

Credit Card Fraud Detection – Processed Dataset

This dataset contains preprocessed credit card transaction data prepared for fraud detection tasks.

  Data Description

The dataset is derived from anonymized transaction records and includes numerical features (V1–V28), transaction amount, and time-based information.

  Preprocessing Steps

Feature scaling and normalization Handling class imbalance Feature selection based on correlation analysis Removal of irrelevant… See the full description on the dataset page: https://huggingface.co/datasets/jyunyilin/credit-card-fraud-detection.

The size of the Credit Card Fraud Detection Platform market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

Credit Card Fraud Detection Analysis and Preprocessing -

Introduction, Data Source, and Project Goal This project presents an Exploratory Data Analysis (EDA) and strategic data preparation for a credit card fraud detection dataset. The dataset, sourced from Kaggle, contains over 280,000 records. The primary challenge identified is extreme class imbalance, as less than 0.2% of transactions are fraudulent. The goal is to prepare the data for a classification model capable of predicting whether… See the full description on the dataset page: https://huggingface.co/datasets/Barvero/Credit_Card_Fraud_Analysis_Project.

The Global Credit Card Fraud Detection Platform Market was valued at USD 4,332.80 million in 2024 and is expected to grow at a CAGR of around 13.86% during 2025-2033.

Dataset

This dataset was created by Arshiya Kishore

Released under MIT

Contents