Payment card fraud - including both credit cards and debit cards - is forecast to grow by over ** billion U.S. dollars between 2022 and 2028. Especially outside the United States, the amount of fraudulent payments almost doubled from 2014 to 2021. In total, fraudulent card payments reached ** billion U.S. dollars in 2021. Card fraud losses across the world increased by more than ** percent between 2020 and 2021, the largest increase since 2018.

• Digital payments are evolving, but so are cyber criminals.

• According to the Data Breach Index, more than 5 million records are being stolen on a daily basis, a concerning statistic that shows - fraud is still very common both for Card-Present and Card-not Present type of payments.

• In today’s digital world where trillions of Card transaction happens per day, detection of fraud is challenging.

This Dataset sourced by some unnamed institute.

Feature Explanation:

distance_from_home - the distance from home where the transaction happened.

distance_from_last_transaction - the distance from last transaction happened.

ratio_to_median_purchase_price - Ratio of purchased price transaction to median purchase price.

repeat_retailer - Is the transaction happened from same retailer.

used_chip - Is the transaction through chip (credit card).

used_pin_number - Is the transaction happened by using PIN number.

online_order - Is the transaction an online order.

fraud - Is the transaction fraudulent.

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.

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.

The Credit Card Transactions Dataset provides detailed records of credit card transactions, including information about transaction times, amounts, and associated personal and merchant details. This dataset has over 1.85M rows.

How This Dataset Can Be Used:

Fraud Detection : Use machine learning models to identify fraudulent transactions by examining patterns in transaction amounts, locations, and user profiles. Enhancing fraud detection systems becomes feasible by analyzing behavioral patterns.

Customer Segmentation : Segment customers based on spending patterns, location, and demographics. Tailor marketing strategies and personalized offers to these different customer segments for better engagement.

Transaction Classification : Classify transactions into categories such as grocery or entertainment to understand spending behaviors. This helps in improving recommendation systems by identifying transaction categories and preferences.

Geospatial Analysis : Analyze transaction data geographically to map spending patterns and detect regional trends or anomalies based on latitude and longitude.

Predictive Modeling : Build models to forecast future spending behavior using historical transaction data. Predict potential fraudulent activities and financial trends.

Behavioral Analysis : Examine how factors like transaction amount, merchant type, and time influence spending behavior. Study the relationships between user demographics and transaction patterns.

Anomaly Detection : Identify unusual transaction patterns that deviate from normal behavior to detect potential fraud early. Employ anomaly detection techniques to spot outliers and suspicious activities.

This graph illustrates the distribution of the domestic fraud rate of bank card transactions in France between 2011 and 2018, by type of payment. In 2018, the fraud rate for bank withdrawals in France amounted to 0.02 percent.

Card fraud losses across the world increased by more than 10 percent between 2020 and 2021, the largest increase since 2018. It was estimated that merchants and card acquirers lost well over 30 billion U.S. dollars, with - so the source adds - roughly 12 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.

Imagine this: you're sitting at a coffee shop, enjoying a latte, and casually checking your email. Suddenly, a notification pops up, your credit card has been charged $500 for something you didn’t buy. Scenarios like this are becoming alarmingly common. Credit card fraud is a modern menace, evolving with every...

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 fraud techniques. The market, valued at approximately $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. This substantial growth is fueled by several key factors. The rising adoption of e-commerce and mobile payments creates a larger attack surface for fraudsters, necessitating advanced detection solutions. Furthermore, the increasing prevalence of sophisticated fraud schemes, such as synthetic identity theft and account takeover, demands more intelligent and adaptive fraud detection systems. The market is segmented by screening type (manual and automatic) and application (personal and enterprise), with automatic screening and enterprise applications driving the majority of growth due to their scalability and efficiency. The competitive landscape is dynamic, with established players like FICO, Mastercard, and Visa competing alongside innovative startups such as Forter and Feedzai. These companies continuously develop AI-powered solutions leveraging machine learning and big data analytics to identify and prevent fraudulent transactions effectively. Regional growth varies, with North America and Europe currently holding significant market share, but Asia-Pacific is expected to experience rapid expansion in the coming years due to rising digital adoption and economic growth in countries like India and China. The continued growth of the credit card fraud detection platform market hinges on several factors. The increasing demand for real-time fraud detection capabilities is driving the adoption of cloud-based solutions and the integration of advanced analytics. Regulatory compliance requirements, particularly around data privacy and security, also contribute to market growth. However, challenges remain. The cost of implementing and maintaining these sophisticated systems can be prohibitive for smaller businesses. Moreover, the constant evolution of fraud techniques necessitates ongoing investment in research and development to stay ahead of emerging threats. The market’s future trajectory will depend on the continued innovation in fraud detection technologies, the ability to adapt to evolving fraud tactics, and the successful integration of these solutions across various industries and geographies.

This statistic presents the value of losses due to synthetic credit card fraud in the United States from 2015 to 2017, with projections extending to 2020. Such fraud led to *** million U.S. dollars in damages in 2017, an amount which was expected to increase to nearly **** trillion U.S. dollars in 2020.

This dataset, commonly known as creditcard.csv, contains anonymized credit card transactions made by European cardholders in September 2013. It includes 284,807 transactions, with 492 labeled as fraudulent. Due to confidentiality constraints, features have been transformed using PCA, except for 'Time' and 'Amount'.

This dataset was used in the research article titled "A Hybrid Anomaly Detection Framework Combining Supervised and Unsupervised Learning for Credit Card Fraud Detection". The study proposes an ensemble model integrating techniques such as Autoencoders, Isolation Forest, Local Outlier Factor, and supervised classifiers including XGBoost and Random Forest, aiming to improve the detection of rare fraudulent patterns while maintaining efficiency and scalability.

Key Features:

30 numerical input features (V1–V28, Time, Amount) Class label indicating fraud (1) or normal (0) Imbalanced class distribution typical in real-world fraud detection Use Case: Ideal for benchmarking and evaluating anomaly detection and classification algorithms in highly imbalanced data scenarios.

Source: Originally published by the Machine Learning Group at Université Libre de Bruxelles.

Dataset

This dataset was created by Dileep

Contents

In 2024, damage caused by credit card fraud reported by Japanese companies amounted to **** billion Japanese yen, reaching a new decade high. Losses caused by illegal credit card use increased from about **** billion yen in the previous year.

Dataset

This dataset was created by Shubham Lipare

Contents

Unlike many other countries, the United States did not see a surge in the “card-not-present” fraud rate immediately after migrating to chip-card technology. Instead, the U.S. card-not-present fraud rate of non-prepaid debit cards has increased gradually over the past decade. Merchants’ and cardholders’ card-not-present fraud loss rates have increased for both dual- and single-message networks, while issuers’ card-not-present fraud loss rate has increased for single-message networks.

Financial transaction data, which is typically imbalanced. While most transactions are not fraudulent, there exist a small percentage that are fraudulent. These few fraudulent transactions usually incur costly monetary repercussions.

Context

Limited credit card transaction data is available for training fraud detection models and other uses, such as analyzing similar purchase patterns. Credit card data that is available often has significant obfuscation, relatively few transactions, and short time duration. For example, this Kaggle dataset has 284,000 transactions over two days, of which less than 500 are fraudulent. In addition, all but two columns have had a principal components transformation, which obfuscates true values and makes the column values uncorrelated.

Content

The data here has almost no obfuscation and is provided in a CSV file whose schema is described in the first row. This data has more than 20 million transactions generated from a multi-agent virtual world simulation performed by IBM. The data covers 2000 (synthetic) consumers resident in the United States, but who travel the world. The data also covers decades of purchases, and includes multiple cards from many of the consumers.

Further details about the generation are here. Analyses of the data suggest that it is a reasonable match for real data in many dimensions, e.g. fraud rates, purchase amounts, Merchant Category Codes (MCCs), and other metrics. In addition, all columns except merchant name have their "natural" value. Such natural values can be helpful in feature engineering of models.

F1 provides a useful score for models predicting whether a particular transaction is fraudulent. In addition, comparison can be made to the results other fraud detection models, e.g.

• TabFormer (BERT-based Model)
• LSTM and GRU Models for AI on IBM Z Mainframe

A broader set of synthetic financial transactions labeled for money laundering is also available on Kaggle:

• AML: Anti Money Laundering Data

Feedback

We look forward to models and other analysis of this data. We also look forward to discussion, comments, and questions.

LICENSE

               Apache License
            Version 2.0, January 2004
          http://www.apache.org/licenses/

TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

1. Definitions.

   "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.

   "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.

   "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.

   "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.

   "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.

   "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.

   "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).

   "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.

   "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the c...

In 2024, damage caused by credit card fraud reported by Japanese companies amounted to **** billion Japanese yen. With about **** billion yen, fraudulent use of credit card numbers accounted for the largest amount of losses.

This dataset provides detailed credit card transaction records enriched with fraud suspicion flags, risk scores, and contextual information such as merchant, location, and transaction method. It is ideal for developing, training, and evaluating fraud detection models, as well as for analyzing transaction patterns and identifying emerging fraud tactics in the financial sector.

Snapshot img

Credit Card Payments Market Size 2025-2029

The credit card payments market size is forecast to increase by USD 181.9 billion, at a CAGR of 8.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing prevalence of online transactions. The digital shift in consumer behavior, fueled by the convenience and accessibility of e-commerce platforms, is leading to a surge in credit card payments. Another key trend shaping the market is the adoption of mobile biometrics for payment processing. This advanced technology offers enhanced security and ease of use, making it an attractive option for both consumers and merchants. However, the market also faces challenges. In developing economies, a lack of awareness and infrastructure for online payments presents a significant obstacle. Bridging the digital divide and educating consumers about the benefits and security of online transactions will be crucial for market expansion in these regions. Effective strategies, such as partnerships with local financial institutions and targeted marketing campaigns, can help overcome this challenge and unlock new opportunities for growth.

What will be the Size of the Credit Card Payments Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by advancements in technology and shifting consumer preferences. Payment optimization through EMV chip technology and payment authorization systems enhances security and streamlines transactions. Cross-border payments and chargeback prevention are crucial for businesses expanding globally. Ecommerce payment solutions, BNPL solutions, and mobile payments cater to the digital age, offering flexibility and convenience. Payment experience is paramount, with user interface design and alternative payment methods enhancing customer satisfaction. Merchant account services and payment gateway integration enable seamless transaction processing. Payment analytics and loyalty programs help businesses understand customer behavior and boost retention. Interchange fees, chargeback management, and dispute resolution are essential components of credit card processing. Data encryption and fraud detection ensure payment security. Multi-currency support and digital wallets cater to diverse customer needs. Customer support and subscription management are vital for maintaining positive relationships and managing recurring billing. Processing rates, settlement cycles, and PCI compliance are key considerations for businesses seeking efficient and cost-effective payment solutions. The ongoing integration of these elements shapes the dynamic and evolving credit card payments landscape.

How is this Credit Card Payments Industry segmented?

The credit card payments industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userConsumer or individualCommercialProduct TypeGeneral purpose credit cardsSpecialty credit cardsOthersApplicationFood and groceriesHealth and pharmacyRestaurants and barsConsumer electronicsOthersGeographyNorth AmericaUSCanadaEuropeGermanyUKAPACChinaIndiaJapanSouth KoreaSouth AmericaArgentinaBrazilRest of World (ROW).

By End-user Insights

The consumer or individual segment is estimated to witness significant growth during the forecast period.The market is a dynamic and evolving landscape that caters to businesses and consumers alike. Recurring billing enables merchants to automatically charge customers for goods or services on a regular basis, streamlining the payment process for both parties. EMV chip technology enhances payment security, reducing the risk of fraud. Payment optimization techniques help businesses minimize transaction costs and improve authorization rates. Cross-border payments facilitate international business, while chargeback prevention measures protect merchants from revenue loss due to disputed transactions. Ecommerce payment solutions provide convenience for consumers and merchants, with payment gateway integration ensuring seamless transactions. Rewards programs and buy now, pay later (BNPL) solutions incentivize consumer spending. Mobile payments and digital wallets offer flexibility and convenience. Merchants can accept various payment methods, including cryptocurrencies, and benefit from payment analytics and conversion rate optimization. Payment volume continues to grow, necessitating robust fraud detection systems and multi-currency support. Customer support is crucial for resolving disputes and addressing payment issues. Alternative payment methods cater to diverse consumer preferences. The payment experience is key to customer retention and a