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

This statistic presents the average credit card debt of Americans in 2017, by income. In that year, the credit card debt of Americans who earned more than *** thousand U.S. dollars amounted to ****** U.S. dollars.

View quarterly updates and historical trends for US Credit Card Debt. from United States. Source: Federal Reserve Bank of New York. Track economic data wi…

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.

Introduction

Credit Card Statistics: A credit card is a widely used financial tool that allows consumers to make purchases or withdraw cash on credit, accruing debt to be repaid later. As of Q4 2024, Americans held approximately USD 1.21 trillion in credit card debt, marking a 4% increase from the previous year. The average credit card balance per consumer reached USD 6,730, up by 3.5% from 2023.

In the same period, the number of credit card accounts in the U.S. rose to about 617 million. Globally, Visa and Mastercard have approximately 1.3 billion and 1.1 billion credit cards in circulation, respectively. Credit cards accounted for 32% of all payment transactions in 2023, reflecting their significant role in consumer spending. However, 22% of credit card users make only minimum payments, indicating potential financial strain. Additionally, credit card delinquency rates rose to 3.6% in Q4 2024, highlighting challenges in debt repayment. These statistics underscore the importance of responsible credit card usage and financial management.

Credit cards also allow customers to build a debt balance that is related to the interest being charged. Let’s shed more light on “Credit Card Statistics†through this article.

CUST_ID: Credit card holder ID

BALANCE: Monthly average balance (based on daily balance averages)

BALANCE_FREQUENCY: Ratio of the last 12 months with balance

PURCHASES: Total purchase amount spent during last 12 months

ONEOFF_PURCHASES: Total amount of one-off purchases

INSTALLMENTS_PURCHASES: Total amount of installment purchases

CASH_ADVANCE: Total cash-advance amount

PURCHASES_ FREQUENCY: Frequency of purchases (Percent of months with at least one purchase)

ONEOFF_PURCHASES_FREQUENCY: Frequency of one-off-purchases PURCHASES_INSTALLMENTS_FREQUENCY: Frequency of installment purchases

CASH_ADVANCE_ FREQUENCY: Cash-Advance frequency

AVERAGE_PURCHASE_TRX: Average amount per purchase transaction

CASH_ADVANCE_TRX: Average amount per cash-advance transaction

PURCHASES_TRX: Average amount per purchase transaction

CREDIT_LIMIT: Credit limit

PAYMENTS: Total payments (due amount paid by the customer to decrease their statement balance) in the period

MINIMUM_PAYMENTS: Total minimum payments due in the period.

PRC_FULL_PAYMEN: Percentage of months with full payment of the due statement balance

TENURE: Number of months as a customer

The UK's average credit card debt per household grew by *** British pounds between December 2021 and December 2022, the first increase since 2020. Standing at ***** British pounds at December 2022, the figure contrasts with the decline in 2020 – when the debt declined from ***** British pounds to ***** British pounds. That particular drop was likely a result of Covid-19's economic impact, and consumers trying to get rid of their credit card debt. The increase in 2022 may be caused by growing interest rates and the cost of living crisis beginning to take shape.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Current Credit Score: 50th Percentile (RCCCBSCOREPCT50) from Q3 2012 to Q2 2025 about score, FR Y-14M, credit cards, consumer credit, large, balance, credits, percentile, loans, consumer, banks, depository institutions, and USA.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Average Purchase Volume by Credit Score Group: <660 Credit Score (RCCCBPURCHASEASLT660) from Q3 2012 to Q2 2025 about volume, score, FR Y-14M, credit cards, purchase, consumer credit, large, balance, credits, average, loans, consumer, banks, depository institutions, and USA.

The average credit card purchase value in the United Kingdom was over ***British pounds as of August 2025. This was slightly lower than in the same month of the previous year and lower than in January 2023, when it reached an all-time high, with each individual credit card transaction averaging **** British pounds. This contrasted with April 2020, when coronavirus measures caused the average credit card value to decline. However, the total credit card debt in the UK in June 2025 grew almost six percent year-on-year.

Data Dictionary The explanation of the features is as follows:

Customer Number: A sequential number assigned to the customers (this column is hiddenand excluded – this unique identifier will not be used directly). ActiveCreditCard: Did the customer active (Yes) or not active (No) the credit card. Reward: The type of reward program offered for the card. Mailer Type: Letter or postcard. Income Level: Low, Medium, or High. Age: customers’ age Tenure: The number of years that the customer has been a client of the bank. Job: Job type of customer (admin, blue-collar, entrepreneur, housemaid, management, retired, self-employed, services, student, technician, unemployed, unknown) Bank Accounts Open: How many non-credit-card accounts are held by the customer Overdraft Protection: Does the customer have overdraft protection on their checking account(s) (Yes or No). Credit Rating: Low, Medium, or High. Credit Cards Held: The number of credit cards held at the bank. Homes Owned: The number of homes owned by the customer. Household Size: Number of individuals in the family. Own Your Home: Does the customer own their home? (Yes or No). Average Balance: Average account balance (across all accounts over time). Q1, Q2, Q3, and Q4 Balance: Average balance for each quarter in the last year.

South Korea: Credit card debt balance per capita, U.S. dollars: The latest value from is U.S. dollars, unavailable from U.S. dollars in . In comparison, the world average is 0 U.S. dollars, based on data from countries. Historically, the average for South Korea from to is U.S. dollars. The minimum value, U.S. dollars, was reached in while the maximum of U.S. dollars was recorded in .

The average consumer debt balance in the United States has peaked in 2024 at roughly ******* U.S. dollars. However, average consumer debt had decreased between 2010 and 2013, when it reached approximately ****** U.S. dollars. Here, consumer debt refers to student and car loans, credit cards, personal loans, mortgages, and other types of debt.

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.

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 ...

Graph and download economic data for Large Bank Consumer Credit Card Balances: Average Purchase APR: Private Label (RCCCBAPRAVGPCTPL) from Q3 2012 to Q2 2025 about FR Y-14M, credit cards, consumer credit, purchase, large, balance, average, loans, consumer, banks, depository institutions, private, and USA.

Argentina: Credit card debt balance per capita, U.S. dollars: The latest value from is U.S. dollars, unavailable from U.S. dollars in . In comparison, the world average is 0 U.S. dollars, based on data from countries. Historically, the average for Argentina from to is U.S. dollars. The minimum value, U.S. dollars, was reached in while the maximum of U.S. dollars was recorded in .

Serbia Lending Rate: Weighted Avg: OA: NF: RC: Credit Card Debt data was reported at 9.090 % pa in Jun 2018. This stayed constant from the previous number of 9.090 % pa for May 2018. Serbia Lending Rate: Weighted Avg: OA: NF: RC: Credit Card Debt data is updated monthly, averaging 14.090 % pa from Sep 2010 (Median) to Jun 2018, with 94 observations. The data reached an all-time high of 23.750 % pa in Oct 2010 and a record low of 8.560 % pa in Mar 2018. Serbia Lending Rate: Weighted Avg: OA: NF: RC: Credit Card Debt data remains active status in CEIC and is reported by National Bank of Serbia. The data is categorized under Global Database’s Serbia – Table RS.M005: Lending Rate: Weighted Average: Outstanding Amounts.

Key information about United States Household Debt

• United States Household Debt reached 18,585.0 USD bn in Sep 2025, compared with the reported number of 18,388.0 USD bn in the previous quarter
• US Household Debt: USD mn data is updated quarterly, available from Mar 1999 to Sep 2025
• The data reached an all-time high of 18,585.0 USD bn in Sep 2025 and a record low of 4,540.0 USD bn in Mar 1999

• In the latest reports, United States Household Debt accounted for 61.4 % of the country's Nominal GDP in Jun 2025
• Money Supply M2 in United States increased 22,298.1 USD bn YoY in Oct 2025
• United States Foreign Exchange Reserves was measured at 38.3 USD bn in Oct 2025
• The Foreign Exchange Reserves equaled 0.1 Months of Import in Aug 2025
• United States Domestic Credit reached 31,813.3 USD bn in Mar 2025, representing an increased of 4.0 % YoY

China: Percent of credit card debt balance 90+ days delinquent: The latest value from is percent, unavailable from percent in . In comparison, the world average is 0.00 percent, based on data from countries. Historically, the average for China from to is percent. The minimum value, percent, was reached in while the maximum of percent was recorded in .