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.

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).

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).

When it comes to credit card users in the United States, ** percent of 18 - 29 year olds do so in the U.S. This is according to exclusive insights from the Consumer Insights Global survey which shows that ** percent of 30 - 49 year old consumers also fall into this category.Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than ********* interviews.

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.

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 ...

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.

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.

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 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.

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

Statistics Table on the Proportion of Revolving Credit Card Users in Different Age Groups to the Total Number of Cardholders (Financial Joint Credit Information Center)

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.

Credit cards were ***** as less likely to be used by younger consumer in 2024 than their older counterparts. This is according to a survey held in 14 different countries across North America, Europe, and Latin America. The share of users among Gen Z respondents in the survey - ages 18 to 27 years old - were significantly lower than the other age groups, with Millennials being slightly below average. Credit cards did, however, rank as the second-most used payment method among Gen Z in travel, behind debit cards.

1) Data Introduction • The Credit Card Classification - clean data Dataset is a tabular dataset refined for classifying the risk level (high/low) of credit card users based on various demographic and income-related features. It includes attributes such as gender, car and property ownership, work/personal contact information, employment status, and the number of children and family members.

2) Data Utilization (1) Characteristics of the Credit Card Classification - clean data Dataset: • With integrated multidimensional demographic and financial features, this dataset is well-suited for predicting credit card user risk levels and recommending financial products.

(2) Applications of the Credit Card Classification - clean data Dataset: • Credit Risk Classification: The dataset can be used to develop models that classify credit card users into high or low risk categories and to evaluate credit scores using input variables. • Financial Marketing and Strategy: The data supports the development of customized credit card marketing strategies for customer segments, as well as the design of lending limits and interest rate policies.

Credit Card: Amount of Transactions: Value: POS: Ratnakar Bank Limited data was reported at 16,483.458 INR mn in Aug 2018. This records an increase from the previous number of 12,197.768 INR mn for Jul 2018. Credit Card: Amount of Transactions: Value: POS: Ratnakar Bank Limited data is updated monthly, averaging 457.051 INR mn from Apr 2011 (Median) to Aug 2018, with 88 observations. The data reached an all-time high of 16,483.458 INR mn in Aug 2018 and a record low of 0.000 INR mn in Dec 2013. Credit Card: Amount of Transactions: Value: POS: Ratnakar Bank Limited 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.KAI016: Credit Card Statistics: by Bankwise.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Share of Accounts Making the Minimum Payment (RCCCBSHRMIN) from Q3 2012 to Q1 2025 about shares, accounts, payments, FR Y-14M, consumer credit, large, balance, loans, consumer, banks, depository institutions, and USA.

Israel Number of Credit Cards in Use data was reported at 8,443,559.000 Unit in Mar 2018. This records an increase from the previous number of 8,271,394.000 Unit for Dec 2017. Israel Number of Credit Cards in Use data is updated quarterly, averaging 7,747,530.000 Unit from Sep 2014 (Median) to Mar 2018, with 15 observations. The data reached an all-time high of 8,443,559.000 Unit in Mar 2018 and a record low of 7,070,462.000 Unit in Sep 2014. Israel Number of Credit Cards 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.

The Philippines: Percent of people aged 15+ who have a credit card: The latest value from 2021 is 8.09 percent, an increase from 1.94 percent in 2017. In comparison, the world average is 22.26 percent, based on data from 121 countries. Historically, the average for the Philippines from 2011 to 2021 is 4.1 percent. The minimum value, 1.94 percent, was reached in 2017 while the maximum of 8.09 percent was recorded in 2021.