The generation X was the group of people with the highest average credit card balance in the United States in the 3rd quarter 2024. That year, the average credit card debt of the generation Z amounted to approximately ***** U.S. dollars. People in the silent generation had a credit card balance of roughly ***** U.S. dollars.

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.

The generation X was the group of people with the highest average credit card balance in the United States in 2023. That year, the average credit card debt of the generation Z amounted to approximately 3,260 U.S. dollars. People in the silent generation had a credit card balance of roughly 3,410 U.S. dollars.

Graph and download economic data for Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks (CCLACBW027SBOG) from 2000-06-28 to 2025-06-18 about revolving, credit cards, loans, consumer, banks, depository institutions, and USA.

The average amount of non-mortgage debt held by consumers in the United States has been falling steadily during the past years, amounting to ****** U.S. dollars in 2023. While respondents had ****** U.S. dollars of debt in 2018, that volume decreased to ****** U.S. dollars in 2019, which constituted the largest year-over-year decrease.What age groups are more indebted in the U.S.?The age group with the highest level of consumer debt in the U.S. was belonging to the Generation X with approximately ******* U.S. dollars of debt in 2022. The next generations with high consumer debt levels were baby boomers and millennials, whose debt levels were similar. In comparison, credit card debt is more equally distributed across all ages. There is an exception among people under 35 years old, who are significantly less burdened with credit card debt. However, most consumers expect to get rid of their debt in the short term. College expenses as a source of debtEducational expenses were not among the leading sources of debt among consumers in the U.S. in 2022. Instead, they made up about ** percent of the total. However, around ** percent of undergraduates from lower-income families had student loans, while over a fifth of undergraduates from higher-income families had student loans. Independently of how they cover these expenses, the confidence of students and parents about being able to pay these college costs was high in most cases.

Graph and download economic data for Household Debt Service Payments as a Percent of Disposable Personal Income (TDSP) from Q1 1980 to Q1 2025 about disposable, payments, debt, personal income, percent, personal, households, services, income, and USA.

The debt settlement service market is experiencing robust growth, driven by increasing consumer debt levels and a rising awareness of debt relief solutions. While precise market size figures for the base year (2025) are unavailable, considering industry reports and trends indicating a substantial market, a reasonable estimate for the 2025 market size could be $5 billion USD. Assuming a Compound Annual Growth Rate (CAGR) of 8% (a conservative estimate based on historical growth and future projections accounting for economic fluctuations and regulatory changes), the market is projected to reach approximately $8 billion USD by 2033. Key drivers include the rising prevalence of unsecured debt, such as credit card debt and medical bills, coupled with stagnant wage growth for many. Furthermore, the increasing availability of online debt settlement services and improved marketing strategies are contributing to market expansion. The market is segmented by service type (negotiation, counseling, etc.), customer demographics (age, income), and geographic regions. Competition is intense, with established players like Freedom Debt Relief, Rescue One Financial, and National Debt Relief competing against smaller, regional firms. Market restraints include stringent regulations, consumer skepticism, and the potential for negative impacts on credit scores. This growth trajectory is expected to continue, although the pace might vary depending on macroeconomic conditions. The increasing sophistication of debt settlement techniques and the evolving regulatory landscape are shaping the competitive dynamics. Companies are focusing on technology integration to improve efficiency and customer experience. The successful companies will be those that can navigate the regulatory environment, build trust with consumers, and offer transparent, effective solutions. Growth opportunities exist in expanding into underserved markets, developing innovative solutions, and enhancing client engagement through technological advances and personalized services. Further research into specific regional data would provide a more precise understanding of the market's nuances.

Graph and download economic data for Delinquency Rate on Credit Card Loans, All Commercial Banks (DRCCLACBS) from Q1 1991 to Q1 2025 about credit cards, delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

The survey asked respondents to compare their expenditure and consumer behaviour (concerning e.g. food, housing, leisure activities, alcohol, travel) to those of the average consumer. The respondents were asked which things and household items they considered necessary and what they would do if they had more money. The survey carried a set of attitudinal statements about consumption and lifestyle (e.g. "I like to drink wine when eating" or "Quality is more important to me than price"). Some questions covered on what grounds respondents made decisions on economical, family or work matters. The extent to which the deep recession of the early 1990s had affected the household was examined. One theme pertained to community identification: whether the respondents felt they were part of their family, workplace, community, Finnish society, and how much their way of spending or borrowing money, etc. was similar to that of other people. The respondents were asked to define different generations and to assess whether there was any conflict between them. They rated the importance of various things (e.g. self-respect, world peace, prosperity, independence) to themselves and the safety of their own life, community, society and the world. Views were probed on how much insecurity e.g. pollution, cuts to certain public services and increasing the national debt would cause. Some questions covered personal feelings of insecurity concerning e.g. livelihood, finances, relationships. The respondents evaluated risks in the present-day society and rated the risk involved in different actions (e.g. contracting a loan, travelling, speeding, flying, using drugs, casual sex). The survey contained questions about the income, expenditure, savings and debts of the respondents and the household. Credit card use, defaults on payments/debts and the resulting bad credit were charted. The respondents were asked what their methods of coping were when short of money, that is, whether they would borrow, reduce expenditure, gamble, etc. Background variables included respondents' sex, tenure, marital status, household size, number of children, basic and vocational education, economic activity, occupation of the respondent, the spouse and parents, experiences of unemployment, financial circumstances, social class, voting in elections and party preference.

As of the last quarter of 2022, Alaska and Hawaii were the states in the U.S. with the highest credit card debt. While the average credit card debt in Alaska amounted to 4,430 U.S. dollars, people from Mississippi only had on average 2,450 U.S. dollars of credit card debt.

Household debt in Thailand has become a serious problem, particularly for the elderly, because they have less ability to pay debts compared to the working population. Therefore, better understanding of the elderly’s debt and delinquency is crucial in policy formulation for the aged society. Previous works have focused on the elderly’s debt, either at the macro or individual levels. Little is known about geographic differences at the regional level. Knowing where the elderly debt and delinquency tend to cluster could guide area-specific policies to tackle the elderly financial stability. This research aims to examine the spatio-temporal distribution of the debt and delinquency of the near-elderly population (age 50–59) and the elderly (age 60 and above) using data from the National Credit Bureau of Thailand from 2008 to 2019. The analysis focuses on various types of loans. Spatial clusters are identified by Moran’s I and Local Geary statistics. The results show that the pattern of elderly debt and delinquency generally follows the life cycle hypothesis, except for credit card and business-related loans. There exist spatial clusters and spatial differences in different types of loans, suggesting regional heterogeneities that require area-specific policy responses.

This statistic shows the results of a survey regarding adults who were concerned about their level of credit card debt in Great Britain as of January 2020, listed by age groups. 54 percent of respondents aged 25 to 34 and 64 percent of respondents aged 35 to 44 years old worried about their credit cards debt.

Key information about Canada Household Debt

• Canada Household Debt reached 2,110.9 USD bn in Dec 2024, compared with the reported number of 2,161.8 USD bn in the previous month
• Canada Household Debt: USD mn data is updated monthly, available from Jan 1969 to Dec 2024
• The data reached an all-time high of 2,225.1 USD bn in Sep 2024 and a record low of 22.2 USD bn in Jan 1969

CEIC converts monthly Household Debt into USD. Statistics Canada provides Household Debt in local currency. The Federal Reserve Board period end market exchange rate is used for currency conversions. Loans are used due to the lack of Flow of Funds statistics. Household Debt prior to January 1990 is sourced from the Bank of Canada.


Further information about Canada Household Debt

• In the latest reports, Canada Household Debt accounted for 99.2 % of the country's Nominal GDP in Sep 2024
• Money Supply M2 in Canada increased 1,873.3 USD bn YoY in Nov 2024
• Canada Foreign Exchange Reserves was measured at 90.2 USD bn in Jan 2025
• The Foreign Exchange Reserves equaled 2.0 Months of Import in Dec 2024
• Canada Domestic Credit reached 4,647.3 USD bn in Nov 2024, representing an increased of 3.9 % YoY

Key information about Australia Household Debt: % of GDP

• Australia household debt accounted for 116.6 % of the country's Nominal GDP in Mar 2023, compared with the ratio of 118.0 % in the previous quarter.
• Australia household debt to GDP ratio is updated quarterly, available from Jun 1988 to Mar 2023.
• The data reached an all-time high of 129.4 % in Sep 2016 and a record low of 44.2 % in Sep 1988.

CEIC calculates quarterly Household Debt as % of Nominal GDP from quarterly Household Debt and quarterly Nominal GDP. The Australian Bureau of Statistics provides Household Debt in local currency and Nominal GDP in local currency.


Related information about Australia Household Debt: % of GDP

• In the latest reports, Australia Household Debt reached 1,957.6 USD bn in Mar 2023.
• Money Supply M2 in Australia increased 4.1 % YoY in Jun 2023.
• Australia Foreign Exchange Reserves was measured at 37.1 USD bn in Jun 2023.
• The Foreign Exchange Reserves equaled 1.5 Months of Import in May 2023.
• Australia Domestic Credit reached 3,627.0 USD bn in May 2023, representing an increased of 1.9 % YoY.
• The country's Non Performing Loans Ratio stood at 0.8 % in Mar 2023, compared with the ratio of 0.8 % in the previous quarter.

This is not an original dataset.

Dataset Versions

1. LoanData_Raw_v1.0.csv:

   • Contains missing values (NaNs) in columns such as age, ed, and income.
   • Data types include float, integer, and object for default status.
   • Represents the raw, unprocessed loan data.

2. LoanData_Preprocessed_v1.1.csv:

   • Similar structure to the raw dataset with some preprocessing.
   • Contains missing values (NaNs) in columns such as age, ed, and income.
   • The default column is encoded as integers.
   • This version represents an initial stage of data preprocessing.

3. LoanData_Preprocessed_v1.2.csv:

   • Fully preprocessed version with no missing values.
   • All columns have complete data.
   • Data types include integers for most columns and floats for financial metrics.
   • This version is ready for detailed analysis and modeling.

Features

Age - Description: Age of the loan applicant. - Data Type: Numerical

Education_Level (ed) - Description: Highest level of education attained by the applicant. - Categories: High School, Undergraduate, Graduate, Postgraduate

Employment_Years (employ) - Description: Number of years the applicant has been employed. - Data Type: Numerical

Address_Years (address) - Description: Number of years the applicant has lived at their current address. - Data Type: Numerical

Annual_Income (income) - Description: Applicant's annual income in thousands of dollars. - Data Type: Numerical

Debt_Income_Ratio (debtinc) - Description: Ratio of the applicant's total monthly debt payments to their monthly gross income. - Data Type: Numerical

Credit_Debt (creddebt) - Description: Amount of credit card debt the applicant has. - Data Type: Numerical

Other_Debt (othdebt) - Description: Amount of other debts the applicant has. - Data Type: Numerical

Default_Status (default) - Description: Indicates whether the applicant has defaulted on a loan. - Categories: Yes, No

These datasets provide valuable insights into the relationships between applicants' financial metrics and loan approval outcomes, as well as understanding the factors that influence borrower satisfaction.

Cost of living considerations led consumers worldwide to increasingly use debit cards, rather than credit cards. This is according to a survey held in 14 different countries across North America, Europe, and Latin America. Noticeable is that debit cards outpaced credit cards among all age groups, with only people age 70 up slightly preferring credit cards more. This may have two reasons. First, the United States has a growing credit card debt, with debit cards potentially being a cheaper option. Second, Europe boasts a relatively large debit card penetration rate. Alternative payment types like buy now, pay later or cryptocurrency did not increase as much, although a greater portion of younger age groups used these.

The source of information is the "Credit Balance Monthly Report Data" submitted by each member institution of the Credit Information Center. It aggregates the amount of all individual credit loans and calculates the average interest rate by age group and gender. If the loan is unsecured, it is defined as a personal credit loan. It does not include overdue, collection, and bad debt credit accounts.

Debt financing (mortgage, line of credit, term loan, credit card) terms and conditions, average interest rates and average length of term for small and medium enterprises in 2020 by region, CMA level, North American Industry Classification System (NAICS), demographics, age of business, employment size, rate of growth, etc.

Intended use of debt financing (mortgage, line of credit, term loan, credit card) for small and medium enterpries in 2020 by region, CMA level, North American Industry Classification System (NAICS), demographics, age of business, employment size, rate of growth, etc.

EKOS Research Associates and the Canada Millennium Scholarship Foundation conducted a monthly national study of the finances of post-secondary students from September 2001 until May 2002. The study was designed to capture the expenses and income of students on a monthly basis, in order to profile the financial circumstances of Canadian post-secondary students and the adequacy of available funding. The Web-based Students Financial Survey provided accurate, quantifiable results for the first ti me on such issues as the incidence and level of assistance, the level of debt from outstanding bank loans, personal lines of credit, and credit cards. The study also yielded up-to-date information on student assets (such as automobiles, computers, and electronics), student earnings, time usage, and types of expenses incurred. The survey featured a panel of 1,524 post-secondary students from across the country, who participated in a very brief monthly survey, either via Internet or telephone. Students were required to complete a longer baseline wave of the survey in order to participate in the study. The baseline survey asked a number of questions concerning summer income and existing debt, including credit card debt. This dataset was received from the Canada Millennium Scholarship Foundation as is. Issues with value labels and missing values were discovered and corrected as best as possible with the documentation received. The variable gasst: Do you receive any government assistance? was not corrected due to lack of documentation about this variable. Some caution should be used with this dataset. This dataset was freely received from, the Canadian Millenium Scholarship Foundation. So me work was required for the variable and value labels, and missing values. They were correct as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.