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.

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).

The credit card penetration in Canada was forecast to continuously increase between 2024 and 2029 by in total 1.4 percentage points. After the seventh consecutive increasing year, the credit card penetration is estimated to reach 84.55 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).Find more key insights for the credit card penetration in countries like United States and Mexico.

India Cards: Volume: Credit Card: Usage at POS data was reported at 230.266 Unit mn in Mar 2025. This records an increase from the previous number of 199.996 Unit mn for Feb 2025. India Cards: Volume: Credit Card: Usage at POS data is updated monthly, averaging 48.909 Unit mn from Apr 2004 (Median) to Mar 2025, with 252 observations. The data reached an all-time high of 230.266 Unit mn in Mar 2025 and a record low of 9.570 Unit mn in Apr 2004. India Cards: Volume: Credit Card: Usage at POS data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI012: Card Payments.

From the selected regions, the ranking by number of credit cards in use is led by the United States with 1.1 billion cards and is followed by Japan (295.11 million cards). In contrast, the ranking is trailed by Saudi Arabia with 2.73 million cards, recording a difference of 1.1 billion cards to the United States. Shown is the estimated number of credit cards currently in use.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).

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.

Predicting Credit Card Customer Segmentation

Exploring Key Customer Characteristics

By [source]

About this dataset

This dataset contains a wealth of customer information collected from within a consumer credit card portfolio, with the aim of helping analysts predict customer attrition. It includes comprehensive demographic details such as age, gender, marital status and income category, as well as insight into each customer’s relationship with the credit card provider such as the card type, number of months on book and inactive periods. Additionally it holds key data about customers’ spending behavior drawing closer to their churn decision such as total revolving balance, credit limit, average open to buy rate and analyzable metrics like total amount of change from quarter 4 to quarter 1, average utilization ratio and Naive Bayes classifier attrition flag (Card category is combined with contacts count in 12months period alongside dependent count plus education level & months inactive). Faced with this set of useful predicted data points across multiple variables capture up-to-date information that can determine long term account stability or an impending departure therefore offering us an equipped understanding when seeking to manage a portfolio or serve individual customers

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How to use the dataset

This dataset can be used to analyze the key factors that influence customer attrition. Analysts can use this dataset to understand customer demographics, spending patterns, and relationship with the credit card provider to better predict customer attrition.

Research Ideas

• Using the customer demographics, such as gender, marital status, education level and income category to determine which customer demographic is more likely to churn.
• Analyzing the customer’s spending behavior leading up to churning and using this data to better predict the likelihood of a customer of churning in the future.
• Creating a classifier that can predict potential customers who are more susceptible to attrition based on their credit score, credit limit, utilization ratio and other spending behavior metrics over time; this could be used as an early warning system for predicting potential attrition before it happens

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: BankChurners.csv | Column name | Description | |:---------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------| | CLIENTNUM | Unique identifier for each customer. (Integer) | | Attrition_Flag | Flag indicating whether or not the customer has churned out. (Boolean) | | Customer_Age | Age of customer. (Integer) | | Gender | Gender of customer. (String) | | Dependent_count | Number of dependents that customer has. (Integer) | | Education_Level ...

India Cards: Value: Credit Card: Usage at POS data was reported at 714,730.077 INR mn in Mar 2025. This records an increase from the previous number of 621,249.131 INR mn for Feb 2025. India Cards: Value: Credit Card: Usage at POS data is updated monthly, averaging 150,620.000 INR mn from Apr 2004 (Median) to Mar 2025, with 252 observations. The data reached an all-time high of 792,928.079 INR mn in Oct 2024 and a record low of 18,290.000 INR mn in Apr 2004. India Cards: Value: Credit Card: Usage at POS data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Monetary – Table IN.KAI012: Card Payments.

Israel Number of Credit Cards not in Use data was reported at 2,402,317.000 Unit in Mar 2018. This records an increase from the previous number of 2,284,837.000 Unit for Dec 2017. Israel Number of Credit Cards not in Use data is updated quarterly, averaging 2,122,150.000 Unit from Sep 2014 (Median) to Mar 2018, with 15 observations. The data reached an all-time high of 2,402,317.000 Unit in Mar 2018 and a record low of 1,730,188.000 Unit in Sep 2014. Israel Number of Credit Cards not in Use 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.

Credit Card Accounts in the United States increased to 636.03 Million in the second quarter of 2025 from 631.39 Million in the first quarter of 2025. This dataset includes a chart with historical data for the United States Credit Card Accounts.

Canada was one of three countries worldwide in 2021, where credit card ownership among consumers 15 years and up was over ** percent. This according to a major survey held once every three years in over 140 different countries. The results highlight the major differences in how countries prefer to pay: In Europe, for instance, the Nordics, Luxembourg, and the United Kingdom are regarded as top credit card countries, whereas the Netherlands ranked significantly lower than all these countries. Credit card usage Cardholders use their credit cards for billions of purchase transactions per year. Some do this to avoid carrying cash around, while others carry out transactions. Many also use credit cards because they do not have to pay immediately. While this can help with monthly cash flow issues, it can also lead to credit card debt that can take years to pay off. Regional differences in credit cards Some counties have a culture of credit card usage. For example, the leading credit card companies in the United States have issued hundreds of millions of credit cards, more than the number of U.S. citizens. Other countries do not have the culture of non-cash transactions. Overcoming this requires both an investment in payment infrastructure and putting people in the habit of using cards instead of cash.

Thailand Credit Card: Spending Value: Domestic Usage (DU) data was reported at 132,834.070 THB mn in May 2018. This records an increase from the previous number of 129,784.690 THB mn for Apr 2018. Thailand Credit Card: Spending Value: Domestic Usage (DU) data is updated monthly, averaging 57,961.545 THB mn from Jan 1998 (Median) to May 2018, with 218 observations. The data reached an all-time high of 172,293.040 THB mn in Dec 2017 and a record low of 10,356.347 THB mn in May 1999. Thailand Credit Card: Spending Value: Domestic Usage (DU) data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KA012: Credit Card Statistics.

Thailand Credit Card: Spending Value: Abroad Usage: Commercial Banks data was reported at 9,943.610 THB mn in Oct 2018. This records an increase from the previous number of 7,642.330 THB mn for Sep 2018. Thailand Credit Card: Spending Value: Abroad Usage: Commercial Banks data is updated monthly, averaging 6,463.465 THB mn from Jan 2012 (Median) to Oct 2018, with 82 observations. The data reached an all-time high of 10,157.640 THB mn in Apr 2018 and a record low of 2,219.460 THB mn in Jan 2012. Thailand Credit Card: Spending Value: Abroad Usage: Commercial Banks data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KA012: Credit Card Statistics.

Thailand Credit Card: Spending Value: Abroad Usage: CB: Bank Card data was reported at 0.000 THB mn in May 2018. This stayed constant from the previous number of 0.000 THB mn for Apr 2018. Thailand Credit Card: Spending Value: Abroad Usage: CB: Bank Card data is updated monthly, averaging 0.000 THB mn from Jan 2012 (Median) to May 2018, with 77 observations. Thailand Credit Card: Spending Value: Abroad Usage: CB: Bank Card data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KA012: Credit Card Statistics.

Envestnet®| Yodlee®'s Credit Card Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Graph and download economic data for Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks (CCLACBW027SBOG) from 2000-06-28 to 2025-09-10 about revolving, credit cards, loans, consumer, banks, depository institutions, and USA.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Utilization: Active Accounts Only: 75th Percentile (RCCCBACTIVEUTILPCT75) from Q3 2012 to Q1 2025 about FR Y-14M, utilities, consumer credit, large, balance, percentile, loans, consumer, banks, depository institutions, and USA.

Israel Number of Credit Cards in Use: ow Banks Guarantee data was reported at 5,814,819.000 Unit in Jun 2018. This records an increase from the previous number of 5,783,400.000 Unit for Mar 2018. Israel Number of Credit Cards in Use: ow Banks Guarantee data is updated quarterly, averaging 5,497,478.000 Unit from Sep 2014 (Median) to Jun 2018, with 16 observations. The data reached an all-time high of 5,814,819.000 Unit in Jun 2018 and a record low of 5,036,306.000 Unit in Sep 2014. Israel Number of Credit Cards in Use: ow Banks Guarantee 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.

Thailand Credit Card: Spending Value: Abroad Usage: CB: Affinity Card data was reported at 8,710.750 THB mn in May 2018. This records a decrease from the previous number of 10,157.640 THB mn for Apr 2018. Thailand Credit Card: Spending Value: Abroad Usage: CB: Affinity Card data is updated monthly, averaging 6,361.540 THB mn from Jan 2012 (Median) to May 2018, with 77 observations. The data reached an all-time high of 10,157.640 THB mn in Apr 2018 and a record low of 2,219.460 THB mn in Jan 2012. Thailand Credit Card: Spending Value: Abroad Usage: CB: Affinity Card data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KA012: Credit Card Statistics.

Thailand Credit Card: Spending Value: Abroad Usage: Non Bank data was reported at 3,741.860 THB mn in May 2018. This records a decrease from the previous number of 4,211.700 THB mn for Apr 2018. Thailand Credit Card: Spending Value: Abroad Usage: Non Bank data is updated monthly, averaging 1,647.900 THB mn from Jan 2005 (Median) to May 2018, with 161 observations. The data reached an all-time high of 4,211.700 THB mn in Apr 2018 and a record low of 667.770 THB mn in Feb 2005. Thailand Credit Card: Spending Value: Abroad Usage: Non Bank data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KA012: Credit Card Statistics.