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 decreased to 1.18 Trillion USD in the first quarter of 2025 from 1.21 Trillion USD in the fourth quarter of 2024. This dataset includes a chart with historical data for the United States Debt Balance Credit Cards.

The tables and interactive maps below allow users to explore the ratio of debt to income by state, metropolitan statistical area, and county for each year since 1999. Household debt is calculated from Federal Reserve Bank of New York (FRBNY) Consumer Credit Panel/Equifax Data, and household income is reported by the Bureau of Labor Statistics.

The G.19 Statistical Release, Consumer Credit, reports outstanding credit extended to individuals for household, family, and other personal expenditures, excluding loans secured by real estate. Total consumer credit comprises two major types: revolving and nonrevolving. Revolving credit plans may be unsecured or secured by collateral and allow a consumer to borrow up to a prearranged limit and repay the debt in one or more installments. Credit card loans comprise most of revolving consumer credit measured in the G.19, but other types, such as prearranged overdraft plans, are also included. Nonrevolving credit is closed-end credit extended to consumers that is repaid on a prearranged repayment schedule and may be secured or unsecured. To borrow additional funds, the consumer must enter into an additional contract with the lender. Consumer motor vehicle and education loans comprise the majority of nonrevolving credit, but other loan types, such as boat loans, recreational vehicle loans, and personal loans, are also included. This statistical release is designated by OMB as a Principal Federal Economic Indicator (PFEI).

Consumer Credit in the United States decreased to 5.10 USD Billion in May from 16.87 USD Billion in April 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.

Analysis of ‘💳 CFPB Credit Card History’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/cfpb-credit-card-historye on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Lending levels
Monitoring developments in overall activity helps us identify new developments in the markets we regulate. These graphs show the number and aggregate dollar volume of new credit cards opened each month. Aggregated monthly originations are displayed along with a seasonally-adjusted series, which adjust for expected seasonal variation in lending activity.

Year-over-year changes
These graphs show the percentage change in the number of new credit cards originated in the month, compared to lending activity from one year ago. Positive changes indicate that lending activity is higher than it was last year and negative values indicate that lending has declined.

Geographic changes
This map shows the percentage change in the volume of new credit cards originated in each state, compared to lending activity from one year ago. Positive changes mean that the volume of credit cards originated in the state during the month are higher than they were one year ago and negative values indicate that the volume of credit cards has declined.

Source: https://www.consumerfinance.gov/data-research/consumer-credit-trends/credit-cards/origination-activity/#anchor_geographic-changes

This dataset was created by Adam Helsinger and contains around 300 samples along with Group, Month, technical information and other features such as: - Group - Month - and more.

How to use this dataset

• Analyze Group in relation to Month
• Study the influence of Group on Month
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Helsinger

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--- Original source retains full ownership of the source dataset ---

Households Debt in the United States decreased to 69.20 percent of GDP in the fourth quarter of 2024 from 70.50 percent of GDP in the third quarter of 2024. This dataset provides - United States Households Debt To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Key information about United States Household Debt

• United States Household Debt reached 18,036.0 USD bn in Dec 2024, compared with the reported number of 17,943.0 USD bn in the previous quarter
• US Household Debt: USD mn data is updated quarterly, available from Mar 1999 to Dec 2024
• The data reached an all-time high of 18,036.0 USD bn in Dec 2024 and a record low of 4,540.0 USD bn in Mar 1999

• In the latest reports, United States Household Debt accounted for 61.7 % of the country's Nominal GDP in Dec 2024
• Money Supply M2 in United States increased 21,533.8 USD bn YoY in Dec 2024
• United States Foreign Exchange Reserves was measured at 34.9 USD bn in Dec 2024
• The Foreign Exchange Reserves equaled 0.1 Months of Import in Dec 2024
• United States Domestic Credit reached 30,648.3 USD bn in Mar 2024, representing an drop of 0.3 % YoY

Consumer Credit in Canada increased to 788638 CAD Million in May from 787087 CAD Million in April of 2025. This dataset provides - Canada Consumer Credit - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Malawi: Percent of people aged 15+ who have a credit card: The latest value from 2021 is 0.97 percent, a decline from 1.29 percent in 2017. In comparison, the world average is 22.26 percent, based on data from 121 countries. Historically, the average for Malawi from 2011 to 2021 is 1.29 percent. The minimum value, 0.97 percent, was reached in 2021 while the maximum of 1.53 percent was recorded in 2014.

This dataset provides values for CREDIT CARD SPENDING reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Consumer Credit in the United Kingdom increased to 1417 GBP Million in June from 920 GBP Million in May of 2025. This dataset provides the latest reported value for - United Kingdom Consumer Credit - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Analysis of ‘🪧 U.S. Presidents and Debt’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/u-s-presidents-and-debte on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

All U.S. presidents since WWII and their impact on the U.S. debt

This dataset lists each president's impact on the U.S. debt since World War II to the present (2016), starting with Harry S. Truman to Barack Obama. The data is presented in two different ways:

• By President - inclusive of all terms served
  • Lists the term debt increase percentage
• Annual breakdown - from 1949 to 2016
  • Lists the annual debt increase percentage

The data also includes some meta information such as the term periods and lifespan, age, and party of each president.

Source

This dataset was created by Kevin Nayar and contains around 0 samples along with Party, Date Died, technical information and other features such as: - Age - Party - and more.

How to use this dataset

• Analyze Date Died in relation to Age
• Study the influence of Party on Date Died
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Kevin Nayar

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

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

Statistics Netherlands collects data on consumer credit granted to individuals and the resulting debt. The following monthly amounts are given for the various types of credit institutions and types of credit: - amount of credit granted; - amount of interest; - amount of repayments; - amount of outstanding balance; - amount of limits granted. Also stated is the number of: - outstanding contracts; - limits granted; - new loans supplied; - new limits granted.

Data available from January 1998 to December 2013.

Status of the figures: The figures in this table are provisional when published. As this table has been discontinued, they will not become definite. In July data of the previous year are made definite based on the results of a yearly survey. The data for January-May of the current year are also adjusted, but remains provisional. Other adjustments may be made when new or additional information from respondents becomes available.

Changes as of 24 February 2014: Data for December 2013 have been added and the table has been discontinued.

As a result of a number of ambiguities, on 23 February 2012 the figures on Credit card credit were removed as a matter of precaution. The figures will be replaced as soon as possible.

When will new figures be published? As a result of budget restrictions affecting Statistics Netherlands, from 2014 onwards a number of statistics will be either discontinued, published less frequently or published in less detail. Following consultation of our main users, one of the statistics to be discontinued is the series on Consumer Credit. This table is therefore the last in the series. For more information about reduction in the statistical work programme, see Statistics Netherlands Strategic multi-annual programme 2014-2018.

Private Debt to GDP in the United States decreased to 142 percent in 2024 from 147.50 percent in 2023. United States Private Debt to GDP - values, historical data, forecasts and news - updated on July of 2025.

Credit Card Lead Prediction

Happy Customer Bank is a mid-sized private bank that deals in all kinds of banking products, like Savings accounts, Current accounts, investment products, credit products, among other offerings.

The bank also cross-sells products to its existing customers and to do so they use different kinds of communication like telecasting, e-mails, recommendations on net banking, mobile banking, etc.

In this case, the Happy Customer Bank wants to cross-sell its credit cards to its existing customers. The bank has identified a set of customers that are eligible for taking these credit cards.

Now, the bank is looking for your help in identifying customers that could show higher intent towards a recommended credit card, given:

This dataset was part of May 2021 Jobathon conducted my analytics vidhya, for more info check:https://datahack.analyticsvidhya.com/contest/job-a-thon-2/

Credit risk is the probability of a financial loss resulting from a borrower's failure to repay a loan. Essentially, credit risk refers to the risk that a lender may not receive the owed principal and interest, which results in an interruption of cash flows and increased costs for collection.

Dataset features

1. Debt (Задолженность): The total outstanding debt amount owed by the client.
2. Overdue Days (Просрочка, дни): The number of days a payment is overdue.
3. Initial Limit (Первоначальный лимит): The initial credit limit assigned to the client.
4. Birth Date (BIRTHDATE): The client's date of birth.
5. Sex (SEX): The client's gender (e.g., male, female).
6. Education (EDU): The client's level of education (e.g., high school, university).
7. Income (INCOME): The client's monthly or annual income.
8. Loan Term (TERM): The duration of the loan or credit agreement in months.
9. Credit History Rating (Рейтинг кредитной истории): A score or rating reflecting the client's credit history.
10. Living Area (LV_AREA): The geographical area or region where the client resides.
11. Settlement Name (LV_SETTLEMENTNAME): The name of the city, town, or settlement where the client lives.
12. Industry Name (INDUSTRYNAME): The industry or sector in which the client is employed.
13. Probability of Default (PDN): The estimated likelihood that the client will default on the loan.
14. Client ID (CLIENTID): A unique identifier assigned to the client.
15. Scoring Mark (SCORINGMARK): A credit score or risk assessment mark assigned to the client.
16. Underage Children Count (UNDERAGECHILDRENCOUNT): The number of underage children the client has.
17. Velcom Scoring (VELCOMSCORING): A specific scoring metric (possibly telecom-related) used in risk assessment.
18. Family Status (FAMILYSTATUS): The client's marital or family status (e.g., single, married, divorced).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2Fe3f381f2b16279a11f8a88975c643fb3%2FIndias-largest-bank-HDFC-Bank-has-climbed-back-to-the-top-ten-banks-in-the-world-in-terms-of-market-capitalization.jpg?generation=1746093705818402&alt=media" alt="">

Abstract copyright UK Data Service and data collection copyright owner.Understanding Society (UK Household Longitudinal Study), which began in 2009, is conducted by the Institute for Social and Economic Research (ISER) at the University of Essex, and the survey research organisations Verian Group and NatCen. It builds on and incorporates the British Household Panel Survey (BHPS), which began in 1991. The Understanding Society: Main Survey, Linked Credit Reference Agency (CRA) Dataset, 2009-2021: Secure Access study contains, for study participants who provided consent, a file containing credit histories collected by a Credit Reference Agency (CRA) before being linked to their study records by the Financial Conduct Authority (FCA).The data in this study can be linked through the pidp variable to one of the main Understanding Society datasets: SN 6614 (End User Licence), SN 6931 (Special Licence) or SN 6676 (Secure Access). Further details on those studies can be found on the Understanding Society series webpage.For all individuals with a valid consent to CRA linkage collected in Wave 10 of Understanding Society, the data file contains detailed information on credit items held by individuals. The monthly dataset that has been linked to the main survey sample covers a period of twelve years (2009-2021). A credit item is a (typically financial) product taken out by an individual. Credit items contained in the CRA data include current accounts, mortgages, credit items (e.g. personal loans, motor finance, credit cards, retail credit and subprime credit such as payday loans) as well as some household bills (e.g. mobile phones, gas and electric bills but not council taxes).See documentation for further details. Main Topics: The main topic covered in the data files is information on credit items and their histories, including: current accounts; mortgages; running cards (credit cards and store cards); personal loans; motor finance; retail finance; ‘other running’ (card accounts such as mail order and charge cards); home collected high cost credit (rent to own, logbook loans, home credit, guarantor’s loans); household bill accounts (credit accounts pertaining to household expenses such as electricity, gas, water); telecommunications company credit accounts. See the User Guide for further details. Multi-stage stratified random sample Compilation/Synthesis