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.

The Credit Card Statistics provide data in relation to monthly credit card transactions. A breakdown of the number of credit cards issued to Irish residents is also provided.

Visa's number of transactions for their credit cards were used only slightly more often at the end of 2022 compared to the same period in 2021. Although the absolute number of payment transactions kept increasing, the number of payments per account remained relatively unchanged. In Q1 2025, Visa credit cards are used in roughly **** billion transactions. It is believed that credit cards have increasingly become popular to counter cost of living in countries like the United States. Note that the figures shown here are different from transaction number estimates on global general purpose cards as this ranking only shows credit card numbers.

The market size for credit cards in the United States grew by over *** percent between 2022 and 2023, a continuation of previous years. This according to estimates from on the value of transactions conducted with cards with a credit function. Credit cards are the most popular payment method available in the country for several years in a row, with a market share that slightly increased during the first year of COVID-19. The United States' credit card penetration is forecast to reach more than ** percent come 2025.

Anomaly detection for credit card transactions of state employees. Import data and train model for anomaly detection in Power BI desktop or Notebook. To label outliers in Power BI, you'll need to run a Python script in the Power Query Editor and use the get_outliers() method. Create dashboard to visualize with the help of Line Charts, Bubble Charts, TreeMaps, etc.

Source: Delaware Open Data

The monthly card payment statistics provide data in relation to credit and debit card transactions undertaken by Irish resident households. The data includes the monthly value and volume of transactions across both credit and debit cards by Irish households. The data is collected from issuers of credit and debit cards and specifically from reporting agents that are resident in Ireland (including established foreign branches). The aggregate data is further broken down into, remote and non-remote card spending; contactless and mobile wallet card spending; sectoral card spending; domestic and non-domestic card spending; regional card spending in Ireland; and cash withdrawals. A breakdown of the number of credit & debit cards currently issued to Irish residents is also provided. Note, only Personal Cards are in scope for this reporting, business cards and cards issued to non-Irish residents are not included. Additionally, data files uploaded here follow the SDMX –ML format where Series Key are the primary identifier for a reporting period (Date for which the data is reported is represented in the Reporting Period field). For example : PCI.M.IE.W2.PCS_ALL.11.PN is the series key and each element/dimension between the delimiter “.” is expanded with a description in subsequent columns ending with the subscript “DESC” to understand the meaning of each element/dimension. The Observation_free column represents the value (€ EUR) or Volume (PN) of transactions depending on the last element/dimension, EUR or PN. For further information on the Payment Statistics Monthly, the reporting instructions in the Landing page link has additional details about the table and the column names used in this data collection.

The credit card penetration in Thailand was forecast to continuously increase between 2024 and 2029 by in total 36.8 percentage points. After the fifteenth consecutive increasing year, the credit card penetration is estimated to reach 67.53 percent and therefore a new peak in 2029. Notably, the credit card penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like Malaysia and Philippines.

Credit Card Transaction: Quarterly: Volume data was reported at 9,502.716 Unit mn in Jun 2022. This records an increase from the previous number of 9,301.651 Unit mn for Mar 2022. Credit Card Transaction: Quarterly: Volume data is updated quarterly, averaging 6,627.430 Unit mn from Mar 2019 (Median) to Jun 2022, with 14 observations. The data reached an all-time high of 9,502.716 Unit mn in Jun 2022 and a record low of 4,705.638 Unit mn in Jun 2020. Credit Card Transaction: Quarterly: Volume data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Global Database’s Brazil – Table BR.KAA001: Credit Card Statistics.

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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.

This dataset contains insights into a collection of credit card transactions made in India, offering a comprehensive look at the spending habits of Indians across the nation. From the Gender and Card type used to carry out each transaction, to which city saw the highest amount of spending and even what kind of expenses were made, this dataset paints an overall picture about how money is being spent in India today. With its variety in variables, researchers have an opportunity to uncover deeper trends in customer spending as well as interesting correlations between data points that can serve as invaluable business intelligence. Whether you're interested in learning more about customer preferences or simply exploring unbiased data analysis techniques, this data is sure to provide insight beyond what one could anticipate

Indonesia Electronic Card: Transaction: Credit Card: Volume: Purchase data was reported at 29,583,676.000 Unit in Jul 2019. This records an increase from the previous number of 26,495,911.000 Unit for Jun 2019. Indonesia Electronic Card: Transaction: Credit Card: Volume: Purchase data is updated monthly, averaging 18,427,526.860 Unit from Jan 2006 (Median) to Jul 2019, with 163 observations. The data reached an all-time high of 29,940,025.000 Unit in Dec 2018 and a record low of 7,946,883.000 Unit in Feb 2006. Indonesia Electronic Card: Transaction: Credit Card: Volume: Purchase data remains active status in CEIC and is reported by Bank of Indonesia. The data is categorized under Global Database’s Indonesia – Table ID.KAG001: Electronic Card Statistics.

The credit card penetration in Brazil was forecast to continuously increase between 2024 and 2029 by in total 16.6 percentage points. After the twelfth consecutive increasing year, the credit card penetration is estimated to reach 62.27 percent and therefore a new peak in 2029. The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Turkey Credit Card Transaction: Domestic: Vol: Domestic Cards data was reported at 314,825,545.000 Unit in Jun 2018. This records a decrease from the previous number of 325,009,181.000 Unit for May 2018. Turkey Credit Card Transaction: Domestic: Vol: Domestic Cards data is updated monthly, averaging 164,316,749.500 Unit from Jan 2002 (Median) to Jun 2018, with 198 observations. The data reached an all-time high of 325,009,181.000 Unit in May 2018 and a record low of 42,731,360.000 Unit in Jan 2002. Turkey Credit Card Transaction: Domestic: Vol: Domestic Cards data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

Israel Credit Cards Transactions: Volume data was reported at 374,618,878.000 Unit in Jun 2018. This records an increase from the previous number of 365,545,129.000 Unit for Mar 2018. Israel Credit Cards Transactions: Volume data is updated quarterly, averaging 190,402,035.500 Unit from Mar 2003 (Median) to Jun 2018, with 62 observations. The data reached an all-time high of 374,618,878.000 Unit in Jun 2018 and a record low of 94,459,827.000 Unit in Mar 2003. Israel Credit Cards Transactions: Volume data remains active status in CEIC and is reported by Bank of Israel. The data is categorized under Global Database’s Israel – Table IL.KA007: Credit Card Statistics.

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.

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This Dataset contains year, month, bank-type and bank-wise total value and volume of card payments and cash withdrawal transactions of credit and debit Cards at point of sale (PoS), ATMs and online during each month

Dataset

This dataset was created by Patrick Slocum

Released under Apache 2.0

Contents

Daily, weekly and monthly data showing seasonally adjusted and non-seasonally adjusted UK spending using debit and credit cards. These are official statistics in development. Source: CHAPS, Bank of England.

Both in absolute and relative figures, the use of credit cards in the United States continued to grow in 2021. The per capita use of credit cards, for example, is estimated to have reached a figure of nearly *** transactions per person. Interestingly, this number is slightly below that of Canada. Credit cards remained the most popular in-store payment method in the U.S. since the coronavirus pandemic, but lost terrain to mobile wallets when it comes to the most used e-commerce payment methods available in the country.