Credit card debt in the United States has been growing at a fast pace between 2021 and 2025. In the fourth quarter of 2024, the overall amount of credit card debt reached its highest value throughout the timeline considered here. COVID-19 had a big impact on the indebtedness of Americans, as credit card debt decreased from *** billion U.S. dollars in the last quarter of 2019 to *** billion U.S. dollars in the first quarter of 2021. What portion of Americans use credit cards? A substantial portion of Americans had at least one credit card in 2025. That year, the penetration rate of credit cards in the United States was ** percent. This number increased by nearly seven percentage points since 2014. The primary factors behind the high utilization of credit cards in the United States are a prevalent culture of convenience, a wide range of reward schemes, and consumer preferences for postponed payments. Which companies dominate the credit card issuing market? In 2024, the leading credit card issuers in the U.S. by volume were JPMorgan Chase & Co. and American Express. Both firms recorded transactions worth over one trillion U.S. dollars that year. Citi and Capital One were the next banks in that ranking, with the transactions made with their credit cards amounting to over half a trillion U.S. dollars that year. Those industry giants, along with other prominent brand names in the industry such as Bank of America, Synchrony Financial, Wells Fargo, and others, dominate the credit card market. Due to their extensive customer base, appealing rewards, and competitive offerings, they have gained a significant market share, making them the preferred choice for consumers.

Debt Balance Credit Cards in the United States increased to 1.23 Trillion USD in the third quarter of 2025 from 1.21 Trillion USD in the second quarter of 2025. This dataset includes a chart with historical data for the United States Debt Balance Credit Cards.

Households Debt in the United States decreased to 68.30 percent of GDP in the first quarter of 2025 from 69.40 percent of GDP in the fourth quarter of 2024. This dataset provides - United States Households Debt To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Consumer Credit in Canada increased to 804999 CAD Million in September from 798319 CAD Million in August of 2025. This dataset provides - Canada Consumer Credit - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_b40deadbdc470e97b9e16de99c5e6ee2/view

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_5c8e5801c2a64e2e6b16608296ef3e02/view

Co-Branded Credit Card Defaults

Analyzing Application and Credit History Data

By Amit Kishore [source]

About this dataset

This dataset provides insights into the predictability of co-branded credit card default in a retail network of a company. With over [x] columns of data, this dataset contains information ranging from applicants' demographics and credit scores to their limits and payment history. This comprehensive dataset was constructed with the goal of understanding how demographic factors influence credit risk and ultimately, co-branded credit card default rates. From age to income, marital status to educational background, each variable is used to create an understanding of the risks associated with applicants taking out co-branded cards in the retail network. Additionally, get an inside look at current trends in loan application behavior — see how often customers use loan or have applied for new cards over set time intervals — as well as monthly payments and query history. Use this unique dataset to develop an improved model for predicting credit card default that could help financial institutions assess potential cusotmers more accuracyly!

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How to use the dataset

This dataset aims to help predict co-branded credit card defaults in retail networks by providing a variety of information about the applicants. The dataset includes information such as age, gender, marital status, employment status, education level, monthly income and expenses, credit history length, number of loans and credit cards owned by the applicant, number of times they applied for loan/credit card inquiries and how many times they used each loan/credit card in the last months.
- In order to use this dataset effectively to predict co-branded credit card default rates in a retail network it is important to understand the data and how it's related each other. It is also important to consider any external factors that can influence an individual's likelihood of defaulting on a loan.
- The first step is to look at the descriptive statistics for each column so that we can get some idea as to what kind of values are seen most often among our data points and if there are any outliers present. This will give us an idea about which features may be most relevant when predicting defaults or if our model may need more contextual information from outside sources like socio-economic or political factors.
- Once we have identified any relevant features from our descriptive statistics analysis we'll then want to start exploring different ways these variables are related with one another and what kind of relationship these variables have with regards to defaults (both positively correlated/directly increase default risk plus negatively correlated/directly decrease default risk). This can be done through simple pair plots which show distribution and correlations between two given columns or triangular heatmaps which allow us explore correlations among multiple columns at once. Building upon these relationships further allows us then determine possible causes behind the observed correlations between different variable groups – allowing us get even more insight into why certain individuals are more likely than others be defaulters on their co-branded cards (whether it because they simply had bad luck or because there were larger systematic factors playing out).
- Having identified all relevant features from this data exploration process along with any external “background” data points - we finally move into constructing our machine learning models using appropriate algorithms suitable for predicting probability outcomes such as SVM or XGBoost tree ensembles etc.. When building out your ML model you’ll want ensure that all parameters necessary for accurate predictions have been included before deploying them on production systems so as not compromise neither customer privacy nor product quality standards set by regulatory authorities governing such models across countries globally

Research Ideas

• Using the given dataset to create a predictive model that can be used to identify customers at risk of defaulting on their co-branded credit cards. This could help determine which customers should be offered special incentives or strategies in order to reduce their risk of defaulting.
• Using the given dataset to create a financial health recommendation engine that analyzes customer’s existing credit cards and recommends other ways they can improve their financial situation (e.g., balance transfers, better rewards programs, etc.).
• Extracting insights from the data by...

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.

Gorbachev, Olga, and Luengo-Prado, Maria, (2019) "The Credit Card Debt Puzzle: The Role of Preferences, Credit Access Risk, and Financial Literacy." Review of Economics and Statistics 101:2, 294-309.

Review of Economics and Statistics: Forthcoming.. Visit https://dataone.org/datasets/sha256%3A5b772e98338aa1037ce48ea8fe598b34e13d528a6c923067cb8fcb4c85fa8282 for complete metadata about this dataset.

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.

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.

Consumer Credit in the United States increased to 13.09 USD Billion in September from 3.13 USD Billion in August of 2025. This dataset provides the latest reported value for - United States Consumer Credit Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Sources of Revenue: Credit Card Income from Consumers for Credit Intermediation and Related Activities, All Establishments, Employer Firms (REVCICEF522ALLEST) from 2013 to 2022 about intermediate, employer firms, finance companies, credit cards, consumer credit, accounting, companies, revenue, establishments, finance, financial, loans, consumer, income, services, and USA.

Taiwan Credit Card: Domestic Banks: Bad Debts Written Off data was reported at 308.375 NTD mn in Oct 2018. This records a decrease from the previous number of 381.918 NTD mn for Sep 2018. Taiwan Credit Card: Domestic Banks: Bad Debts Written Off data is updated monthly, averaging 477.444 NTD mn from Jun 2004 (Median) to Oct 2018, with 173 observations. The data reached an all-time high of 10,858.606 NTD mn in Apr 2006 and a record low of 283.436 NTD mn in Feb 2015. Taiwan Credit Card: Domestic Banks: Bad Debts Written Off data remains active status in CEIC and is reported by Banking Bureau, Financial Supervisory Commission. The data is categorized under Global Database’s Taiwan – Table TW.KA027: Credit Card Statistics.

Taiwan Credit Card: CCC: AE: Bad Debts Written Off data was reported at 2.612 NTD mn in Oct 2018. This records an increase from the previous number of 2.463 NTD mn for Sep 2018. Taiwan Credit Card: CCC: AE: Bad Debts Written Off data is updated monthly, averaging 4.465 NTD mn from Jun 2004 (Median) to Oct 2018, with 173 observations. The data reached an all-time high of 633.440 NTD mn in Apr 2006 and a record low of 0.659 NTD mn in Mar 2015. Taiwan Credit Card: CCC: AE: Bad Debts Written Off data remains active status in CEIC and is reported by Banking Bureau, Financial Supervisory Commission. The data is categorized under Global Database’s Taiwan – Table TW.KA027: Credit Card Statistics.

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.

Context

The card payments data is published by the Reserve Bank of India on a monthly basis. The statistics cover the methods of payment used in retail transactions and ATM transactions in India. It constitutes payments via debit cards, credit cards, ATMs etc, . It can can be used to check trend of card based payment in India.

Content

The data contains monthly statistics of the following information from Apr'2011 to Aug'2019 1. Number of ATM deployed on site by the bank. 1. Number of ATM deployed off site by the bank. 1. Number of POS deployed online by the bank 1. Number of POS deployed offline by the bank 1. Total number of credit cards issued outstanding (after adjusting the number of cards withdrawan/cancelled). 1. Total number of financial transactions done by the credit card issued by the bank at ATMs 1. Total number of financial transactions done by the credit card issued by the bank at POS terminals 1. Total value of financial transactions done by the credit card issued by the bank at ATMs 1. Total value of financial transactions done by the credit card issued by the bank at POS terminals. 1. Total number of debit cards issued outstanding (after adjusting the number of cards withdrawan/cancelled). 1. Total number of financial transactions done by the debit card issued by the bank at ATMs 1. Total number of financial transactions done by the debit card issued by the bank at POS terminals 1. Total value of financial transactions done by the debit card issued by the bank at ATMs 1. Total value of financial transactions done by the debit card issued by the bank at POS terminals.

Acknowledgements

The data is scraped from RBI monthly statistics https://www.rbi.org.in/scripts/ATMView.aspx More details on how this data is collected and cleaned is documented in this kernel https://www.kaggle.com/karvalo/indian-card-payment-data-gathering-and-analysis

Quarterly financial flows and stocks of household credit market debt, consumer credit, non-mortgage loans, and mortgage loans, on a seasonally adjusted basis.