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.

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.

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...

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.

Credit Card Fraud Detection FAIR Exercise
This project implements an end-to-end, FAIR-compliant pipeline for detecting fraudulent credit-card transactions. It includes:

• Publicly available data splits (70 % train, 15 % validation, 15 % test) in TU Wien’s DBRepo, each with a persistent identifier.

• A RandomForest model trained on the data, serialized and deposited with metadata in TUWRD.

• Evaluation outputs (confusion matrix, ROC curve, predictions) and a comprehensive model card.

• Fully documented Jupyter notebooks and code under MIT, with environment and metadata files to enable reproducible reuse.

The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset present 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-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (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 http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML.

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.

Credit Card Generator Market Outlook

The global credit card generator market is projected to experience robust growth with a market size of approximately USD 580 million in 2023, and it is anticipated to reach USD 1.2 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 8.5%. The rising need for secure and efficient credit card testing tools, driven by the expansion of e-commerce and digital transactions, forms a significant growth catalyst for this market. As online retail and digital financial services burgeon, the demand for reliable credit card generators continues to escalate, underscoring the importance of this market segment.

One of the pivotal growth drivers for the credit card generator market is the increasing complexity and sophistication of online payment systems. As e-commerce platforms and digital payment solutions proliferate worldwide, there is a growing need for comprehensive testing tools to ensure the reliability and security of these systems. Credit card generators play a crucial role in this context by providing developers and testers with the means to simulate various credit card scenarios, thereby enhancing the robustness of payment processing systems. Additionally, the rise in cyber threats and fraud necessitates stringent testing, further propelling market growth.

Another significant factor contributing to the market's expansion is the growing emphasis on fraud prevention and security. Financial institutions and businesses are increasingly investing in sophisticated tools to combat fraud and secure financial transactions. Credit card generators offer a practical solution for testing the efficacy of anti-fraud measures and ensuring that security protocols are adequately robust. By enabling the simulation of fraudulent activities and various transaction scenarios, these tools help organizations better prepare for and mitigate potential security breaches.

Furthermore, the marketing and promotional applications of credit card generators are also driving market growth. Companies leveraging digital marketing strategies use these tools to create dummy credit card numbers for various promotional activities, such as offering free trials or discounts, without exposing real customer data. This capability not only aids in marketing efforts but also ensures compliance with data privacy regulations, thereby enhancing consumer trust and brand reputation. The versatility of credit card generators in supporting both operational and marketing functions underscores their growing importance in the digital age.

Regionally, North America holds a significant share of the credit card generator market, driven by the high penetration of digital payment systems and advanced cybersecurity measures in the region. The presence of numerous financial institutions and technology companies further bolsters the market in North America. Meanwhile, Asia Pacific is expected to witness the fastest growth, fueled by the rapid digitalization of economies, increasing internet penetration, and burgeoning e-commerce activities. Europe also presents substantial opportunities due to stringent data protection regulations and the widespread adoption of digital transaction systems.

Type Analysis

The credit card generator market can be segmented by type into software and online services. Software-based credit card generators are widely used by developers and testers within organizations to simulate credit card transactions and validate payment processing systems. These tools are typically integrated into the development and testing environments, providing a controlled and secure platform for generating valid credit card numbers. The demand for software-based generators is driven by their ability to offer customizable options and advanced features, such as bulk generation and API integration, which enhance the efficiency of testing processes.

Online services, on the other hand, cater to a broader audience, including individual users, small businesses, and marketers. These services are accessible via web platforms and provide an easy-to-use interface for generating credit card numbers for various purposes, such as testing, fraud prevention, and marketing promotions. The growing popularity of online credit card generators can be attributed to their convenience, accessibility, and the increasing need for temporary and disposable credit card numbers in the digital economy. These services are particularly useful for busin

A September 2023 survey of American adults found that the most frequently experienced type of financial cybercrime was credit card fraud, reported by roughly 64 percent of respondents. The breach of financial data was ranked second, followed by account hacking.

In 2022, about ******* complaints filed with the Federal Trade Commission (FTC) were due to credit card fraud in the United States. An additional ****** complaints were filed with the FTC due to government documents/benefits fraud.

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. 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.

Market Analysis for Credit Card Fraud Detection Platform The global credit card fraud detection platform market is estimated to reach USD 25.2 billion by 2033, growing at a CAGR of 14.3% from 2025 to 2033. The increasing adoption of digital payment methods, rising incidences of cybercrime, and stringent regulations on data security drive the market growth. The adoption of advanced technologies like machine learning and artificial intelligence in fraud detection solutions further fuels market expansion. The market is segmented into application (personal, enterprise) and type (manual screening, automatic screening). The enterprise segment dominates the market due to the growing demand for fraud protection in corporate environments. Automatic screening solutions are gaining popularity as they automate the fraud detection process, reducing operational costs and improving efficiency. Key market players include Kount, ClearSale, Stripe Radar, Riskified, Sift, SEON, Visa Advanced Authorization, Mastercard, Akkio, and Grid Dynamics. North America holds the largest market share due to the high adoption of advanced fraud detection technologies and the presence of major financial institutions in the region.

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

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.

Dataset

This dataset was created by Emily Smith

Released under Data files © Original Authors

Contents

Dataset

This dataset was created by Shubham Joshi

Released under Data files © Original Authors

Contents

Dataset

This dataset was created by @data is life

Released under Apache 2.0

Contents

In the late 1970s, the Rand Corporation pioneered a method of collecting crime rate statistics. They obtained reports of offending behavior--types and frequencies of crimes committed--directly from offenders serving prison sentences. The current study extends this research by exploring the extent to which variation in the methodological approach affects prisoners' self-reports of criminal activity. If the crime rates reported in this survey remained constant across methods, perhaps one of the new techniques developed would be easier and/or less expensive to administer. Also, the self-reported offending rate data for female offenders in this collection represents the first time such data has been collected for females. Male and female prisoners recently admitted to the Diagnostic Unit of the Colorado Department of Corrections were selected for participation in the study. Prisoners were given one of two different survey instruments, referred to as the long form and short form. Both questionnaires dealt with the number of times respondents committed each of eight types of crimes during a 12-month measurement period. The crimes of interest were burglary, robbery, assault, theft, auto theft, forgery/credit card and check-writing crimes, fraud, and drug dealing. The long form of the instrument focused on juvenile and adult criminal activity and covered the offender's childhood and family. It also contained questions about the offender's rap sheet as one of the bases for validating the self-reported data. The crime count sections of the long form contained questions about motivation, initiative, whether the offender usually acted alone or with others, and if the crimes recorded included crimes against people he or she knew. Long-form data are given in Part 1. The short form of the survey had fewer or no questions compared with the long form on areas such as the respondent's rap sheet, the number of crimes committed as a juvenile, the number of times the respondent was on probation or parole, the respondent's childhood experiences, and the respondent's perception of his criminal career. These data are contained in Part 2. In addition, the surveys were administered under different conditions of confidentiality. Prisoners given what were called "confidential" interviews had their names identified with the survey. Those interviewed under conditions of anonymity did not have their names associated with the survey. The short forms were all administered anonymously, while the long forms were either anonymous or confidential. In addition to the surveys, data were collected from official records, which are presented in Part 3. The official record data collection form was designed to collect detailed criminal history information, particularly during the measurement period identified in the questionnaires, plus a number of demographic and drug-use items. This information, when compared with the self-reported offense data from the measurement period in both the short and long forms, allows a validity analysis to be performed.

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.

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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