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.

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

🔍 Dataset Description: Credit Card Fraud Detection This dataset is designed for building and evaluating machine learning models for credit card fraud detection. It contains anonymized transaction records where the goal is to classify transactions as fraudulent (1) or non-fraudulent (0) based on several features.

📁 Dataset Overview: Each row represents a single credit card transaction.

Features include a mix of numerical and transformed variables (e.g., V1 to V28) derived from PCA for confidentiality.

The Amount and Hour_of_Day features represent the transaction value and time, respectively.

The Class column is the target variable:

0 → Legitimate transaction

1 → Fraudulent transaction

✅ Key Highlights: The dataset contains both classes (0 and 1) to ensure balanced evaluation for binary classification.

Suitable for testing anomaly detection, binary classification, and imbalanced dataset handling techniques like SMOTE or under-sampling.

Ideal for learners, researchers, and practitioners working on fraud detection in real-world scenarios.

🧠 Suggested Use Cases: Model evaluation with metrics like precision, recall, F1-score (due to class imbalance).

Experimentation with algorithms such as Logistic Regression, Random Forest, XGBoost, and Neural Networks.

Feature engineering and explainability techniques (e.g., SHAP values).

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 0.15 GB large.

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.

The global credit card fraud detection platform market is experiencing robust growth, driven by the escalating volume of digital transactions and the increasing sophistication of fraudulent activities. While precise figures for market size and CAGR are not provided, based on industry reports and observed trends, a reasonable estimation places the 2025 market size at approximately $15 billion. Considering the rapid adoption of advanced technologies like AI and machine learning in fraud detection, a conservative Compound Annual Growth Rate (CAGR) of 15% is projected for the forecast period (2025-2033). This growth is fueled by several factors, including the rising prevalence of e-commerce, the expanding adoption of mobile payments, and the increasing demand for robust security solutions from both personal and enterprise users. The market is segmented by screening type (manual and automatic) and application (personal and enterprise), with the automatic screening and enterprise segments expected to witness faster growth due to their efficiency and scalability. The competitive landscape is highly dynamic, with a mix of established players like Visa, Mastercard, and FICO, alongside innovative technology companies like Kount, Riskified, and Feedzai. These companies are continuously developing and deploying advanced algorithms and analytics to stay ahead of evolving fraud techniques. Regional growth varies, with North America and Europe currently holding significant market share, though Asia-Pacific is projected to exhibit rapid expansion due to increasing internet penetration and e-commerce adoption in developing economies. Challenges to market growth include the high cost of implementation and maintenance of these platforms, along with the need for continuous updates to counter evolving fraud tactics. However, the increasing financial losses incurred due to fraud are incentivizing businesses and consumers to invest in more sophisticated fraud detection solutions, thereby sustaining the market's upward trajectory.

Performance comparison with other credit card fraud detection dataset.

The global credit card fraud detection platform market is projected to reach a value of USD 10.3 billion by 2033, exhibiting a CAGR of 12.5% during the forecast period (2023-2033). The increasing adoption of digital payment methods, the growing number of online transactions, and the rising incidences of fraudulent activities are driving the market's growth. However, factors such as data privacy concerns and the high cost of implementing these platforms may hinder the market's growth. The market is segmented based on application, type, and region. The application segments include e-commerce, banking and financial institutions, and other applications. The e-commerce segment holds the largest market share due to the increasing popularity of online shopping. The types segment includes rule-based systems, statistical techniques, machine learning algorithms, and others. Machine learning algorithms are expected to witness the highest growth rate during the forecast period due to their ability to learn from data and identify fraudulent patterns accurately. The regional segments include North America, Europe, Asia Pacific, and the Rest of the World. North America currently dominates the market and is expected to maintain its dominance throughout the forecast period.

1) Data Introduction • The Credit Card Transactions Dataset includes more than 20 million credit card transactions over the decades of 2,000 U.S. resident consumers created by IBM's simulations, providing details of each transaction and fraudulent labels.

2) Data Utilization (1) Credit Card Transactions Dataset has characteristics that: • This dataset provides a variety of properties that are similar to real credit card transactions, including transaction amount, time, card information, purchase location, and store category (MCC). (2) Credit Card Transactions Dataset can be used to: • Development of Credit Card Fraud Detection Model: Using transaction history and properties, you can build a fraud (abnormal transaction) detection model based on machine learning. • Analysis of consumption patterns and risks: Long-term and diverse transaction data can be used to analyze customer consumption behavior and identify risk factors.

Dataset

This dataset was created by Sriseshagiri

Contents

The dataset has been released by [1], which had been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of Université Libre de Bruxelles (ULB) on big data mining and fraud detection. [1] Pozzolo, A. D., Caelan, O., Johnson, R. A., and Bontempi, G. (2015). Calibrating Probability with Undersampling for Unbalanced Classification. 2015 IEEE Symposium Series on Computational, pp. 159-166, doi: 10.1109/SSCI.2015.33 open source kaggle : https://www.kaggle.com/mlg-ulb/creditcardfraud

Context

The context of this dataset is to find fraudulent credit cards by analyzing the features. The detection of fraudulent credit card can be done using ML or DL.

Acknowledgements

The data actually collected from Weka Repository: https://weka.8497.n7.nabble.com/file/n23121/credit_fruad.arff

Dataset

This dataset was created by AMAN GOEL

Contents

Market Research Intellect's Credit Card Fraud Detection Platform Market Report highlights a valuation of USD 3.5 billion in 2024 and anticipates growth to USD 8.2 billion by 2033, with a CAGR of 10.5% from 2026-2033.Explore insights on demand dynamics, innovation pipelines, and competitive landscapes.

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.

The global Credit Card Fraud Detection Platform Market size was valued at USD 3.59 billion in 2024 and is expected to grow at a CAGR of 15.3% from 2025 to 2034.

Dataset

This dataset was created by Manish Kumar

Contents

Card fraud losses across the world increased by more than ** percent between 2020 and 2021, the largest increase since 2018. It was estimated that merchants and card acquirers lost well over ** billion U.S. dollars, with - so the source adds - roughly ** billion U.S. dollar coming from the United States alone. Note that the figures provided here included both credit card fraud and debit card fraud. The source does not separate between the two, and also did not provide figures on the United States - a country known for its reliance on credit cards.

This dataset contains simulated credit card transaction records, including detailed information on transaction amounts, merchant details, geolocation, device usage, and fraud labels. It is designed for training and evaluating fraud detection models, supporting the identification of both typical and anomalous transaction patterns. The dataset is ideal for fintech AI development, security analytics, and research into payment fraud behaviors.

Data

We provide you with a data set in CSV format. The data set contains 2 lakhh+ record train instances and 56 thousand test instance There are 31 input features, labeled V1 to V28 and Amount .

The target variable is labeled Class.

Task

Create a Classification model to predict the target variable Class.

1. A report - A Power point presentation
2. Any custom code you used
3. Instructions for me to run your model on a separate data set

What should be in the report?

1. List of any assumptions that you made
2. Description of your methodology and solution path
3. List of algorithms and techniques you used
4. List of tools and frameworks you used
5. Results and evaluation of your models

How to evaluate the model

1. Use the F1 Score for metrics
2. Any other evaluation measure that you believe is appropriate other than Accuracy.

Credit Card Fraud Detection Platform Market size was valued at USD 3.4 Billion in 2024 and is projected to reach USD 12.44 Billion by 2032, growing at a CAGR of 17.6% during the forecast period 2026 to 2032.Global Credit Card Fraud Detection Platform Market Drivers:The market drivers for the credit card fraud detection platform market can be influenced by various factors. These may include:Rising Incidence of Online Payment Fraud: The increasing number of fraud attempts during online transactions pushes financial institutions to adopt platforms that monitor and detect unauthorized credit card activity in real time.Growth in E-Commerce Transactions: With more consumers shopping online, the volume of card-not-present transactions rises, creating higher exposure to fraud and driving demand for detection platforms to secure digital payments.