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 average for 2021 based on 121 countries was 22.26 percent. The highest value was in Canada: 82.74 percent and the lowest value was in Afghanistan: 0 percent. The indicator is available from 2011 to 2021. Below is a chart for all countries where data are available.

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).

http://debtpro.co/credit-card-debt-consolidation-bad-credit/ The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the Statement of Loans. The World Bank complies with all sanctions applicable to World Bank transactions.

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.

The percentage of respondents who report having a credit card

credit card relief - (source) - The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the Statement of Loans. The World Bank complies with all sanctions applicable to World Bank transactions.

Thanks redgage

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 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.

Credit card (% age 15+) in Bolivia was reported at 12.64 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Bolivia - Credit card (% age 15+) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

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.

Credit card (% age 15+) in Uruguay was reported at 36.55 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Uruguay - Credit card (% age 15+) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

As of February 2025, the number of credit cards in Indonesia amounted to ***** million. The number of credit cards in Indonesia decreased by ***** percent when the COVID-19 crisis happened in 2020. Currently, there are ** credit card issuers in Indonesia. Credit card ownership in Indonesia To own a credit card in Indonesia, the primary cardholder must be at least 21 years old or married and have a monthly income of at least three million Indonesian rupiah, while an additional credit card can be used by anybody who is at least 17 years old. According to a survey by the World Bank, about *** percent of the wealthiest ** percent in Indonesia held a credit card. In comparison, only around *** percent of the poorest ** percent of the population owned a credit card. Visa and Mastercard are the most frequently owned credit cards by Indonesians. Credit card transactions in Indonesia Even though there were fewer credit card transactions in Indonesia, the value of those transactions increased throughout the COVID-19 crisis in 2020. To boost Indonesia's economic recovery from the crisis, Indonesia's central bank has cut the maximum credit card interest rate from **** to **** percent per month per July 2021 and decreased the minimum credit card payment from ** percent to * percent of its total balance each month per July 2022.

Credit card (% age 15+) in Argentina was reported at 28.89 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Argentina - Credit card (% age 15+) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

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.

Credit card (% age 15+) in Lithuania was reported at 11.89 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Lithuania - Credit card (% age 15+) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Credit card (% age 15+) in Poland was reported at 24.44 % in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Poland - Credit card (% age 15+) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Credit Card Offer Acceptance Trends in Banking

Exploring Credit Card Offer Response and Customer Behaviors

By Anshuman Gautam [source]

About this dataset

This dataset contains credit card offer acceptance information for customers of a bank. We seek to use this data to build models that will provide insights into why some bank customers choose to accept these offers. The data contains various customer attributes such as customer number, offer accepted, reward type, mailer type, income level and more. Additionally, the dataset includes quarterly balances across all accounts and whether or not the customer owns their home or holds any overdraft protection. By exploring this data we hope to gain better understanding of the factors that influence offer acceptances in order to improve future marketing campaigns and increase customer satisfaction levels

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How to use the dataset

This dataset provides a convenient way to explore customer acceptance trends of credit card offers from Banking. It contains customer information such as their income level, number of bank accounts open, overdraft protection, number of credit cards held, number of homes owned and household size. Additionally the dataset also tracks average balance across all accounts, quarterly balances on each account and rewards offered with the offer.

Using this dataset you can analyse consumer behaviour when presented with credit card offers from your bank and gain insight into customer preferences for rewards or other incentives among various market segments. You can use the data to better predict customer acceptance rates based on past responses and use marketing strategies tailored to specific customer segments in order to improve offer acceptance rates.

To analyse this data using the Unlocking Credit Card Offer Acceptance Trends in Banking dataset you will need basic knowledge in topics like Python programming language or Microsoft Excel spreadsheets etc. You may also need specialized statistical software packages such as R or SPSS depending on what kind of analysis you wish to perform. After obtaining this necessary skillset it's important that you familiarize yourself with exploration techniques like descriptive statistics as well as methods like linear & logistic regression if needed for more advanced models that can be used establish relationships between factors that affect whether customers accept an offer or not (income level vs reward type). While analysing it is important remember that variables should be treated consistently regardless of profile type because inconsistent variable treatment might lead skewed results & unreliable conclusions drawn from datasets created through collecting responses from people who come from different socioeconomic backgrounds& ages etc; which could mask any genuine trends found within equally segmented populations answering similar questions about products/ services - making them a biased source for informed decisions about population behaviour! Finally once you have explored your data & identified any notable characteristics worth drawing attention too; consider presenting your findings through visually engaging/ informative methods like graphs/ compelling narratives etc so that stakeholders may understand just how useful predictive modelling using machine learning could really be by developing valuable insights into customers’ preferences when they apply for new product offerings at banks!

Research Ideas

• Determining the optimum offer and reward structure for a bank's credit card offers based on customer income level, number of bank accounts open, and other factors.
• Predicting customer acceptance behavior using machine learning techniques and insights from the dataset such as household size, average balance, etc.
• Segmenting customers into different groups to better target offers based on their financial profile including Credit rating, Number of Credit cards held or Own Your Home and customize marketing message appropriate to each segment

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: creditcardmarketing-bbm.csv | Column name | Description ...

The table shows the level of bank credit to the private sector around the world, i.e. the total value of debt outstanding at a point in time. The first column of numbers presents the amount in local currency. The second column shows the percent change in that amount from three months ago and the last column shows the percent change since the same month last year. All amounts are in nominal terms. Note that total private credit consists of business credit and household credit which, in turn, has two components: mortgage credit that finances the acquisition of real estate and consumer credit that includes credit cards, auto loans, personal lines of credit, student loans, and other types of personal credit. Bank credit is by far the main source of financing for the business sector in almost all countries. Two notable exceptions are the U.S. and the UK where companies can also tap the well developed stock and bond markets to finance projects. In the rest of the world, bank credit is the main source of firm financing.