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.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Total Balances (RCCCBBALTOT) from Q3 2012 to Q2 2025 about FR Y-14M, credit cards, consumer credit, large, balance, loans, consumer, banks, depository institutions, and USA.

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.

Credit card charge-off rates reached their highest level in over 14 years by Q2 2024, as borrowers struggled to keep up with debts. This is according to figures gathered by the Federal Reserve from U.S. chartered commercial banks. Credit card became an increasingly more common way to pay after the coronavirus pandemic, as is shown in the distribution of different types of loans in the United States. U.S. consumers had built up their cash reserves, making them eligible to get a credit card. The high charge-off rates were joined by the highest U.S. credit card delinquency rates since the Financial Crisis of 2008.

Graph and download economic data for Delinquency Rate on Credit Card Loans, All Commercial Banks (DRCCLACBS) from Q1 1991 to Q3 2025 about credit cards, delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

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.

Credit card payments in Slovenia declined by ******* transactions in 2020, lowering the per capita use. As of 2024, credit card transactions have increased to 53.64 million, reflecting a recovery in usage. The decreasing use of credit cards in the country is in stark contrast to developments elsewhere. The credit card transactions per capita in Slovakia, for example, also declined during this time - presumably as consumers did not want to build up credit card debt during COVID-19 lockdown - but remained significantly higher than what was observed in Slovenia.

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 Charge-Off Rate on Credit Card Loans, All Commercial Banks (CORCCACBS) from Q1 1985 to Q3 2025 about charge-offs, credit cards, commercial, loans, banks, depository institutions, rate, and USA.

While interest rates in the United States declined ***** times by late 2024, average credit card interest rates did not immediately follow suit. This reveals itself when comparing the Federal Reserve interest rate against the APR, or annual percentage rates, of credit cards issued by commercial banks. The APR reached a record high in the country in 2024, likely adding to the growing credit card debt in the United States. This was below the APR of credit cards in a country like Brazil, however. It is expected that the credit card interest rates will continue to need time to catch up with the Fed interest rate.

Graph and download economic data for Household Debt Service Payments as a Percent of Disposable Personal Income (TDSP) from Q1 1980 to Q2 2025 about disposable, payments, personal income, debt, percent, households, personal, income, services, and USA.

Brazil Consumer Debt and Default Survey: Type of Debt (Percentage of Total Households): Up to 10 Minimum Wage: Credit Card data was reported at 79.854 % in Sep 2020. This records an increase from the previous number of 78.653 % for Aug 2020. Brazil Consumer Debt and Default Survey: Type of Debt (Percentage of Total Households): Up to 10 Minimum Wage: Credit Card data is updated monthly, averaging 77.270 % from Jan 2010 (Median) to Sep 2020, with 129 observations. The data reached an all-time high of 80.139 % in Jan 2020 and a record low of 67.913 % in Jan 2010. Brazil Consumer Debt and Default Survey: Type of Debt (Percentage of Total Households): Up to 10 Minimum Wage: Credit Card data remains active status in CEIC and is reported by National Confederation of Commerce of Goods, Services and Tourism. The data is categorized under Brazil Premium Database’s Domestic Trade and Household Survey – Table BR.HG003: Consumer Debt and Default Survey: by Type of Debt.

Graph and download economic data for Commercial Bank Interest Rate on Credit Card Plans, All Accounts (TERMCBCCALLNS) from Nov 1994 to Aug 2025 about credit cards, consumer credit, loans, consumer, interest rate, banks, depository institutions, interest, rate, and USA.

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 Debt and Default Survey: Type of Debt (Percentage of Total Households): Up to 10 Minimum Wage: Credit Card在2020-09达79.854 %，相较于2020-08的78.653 %有所增长。Consumer Debt and Default Survey: Type of Debt (Percentage of Total Households): Up to 10 Minimum Wage: Credit Card数据按月度更新，2010-01至2020-09期间平均值为77.270 %，共129份观测结果。该数据的历史最高值出现于2020-01，达80.139 %，而历史最低值则出现于2010-01，为67.913 %。CEIC提供的Consumer Debt and Default Survey: Type of Debt (Percentage of Total Households): Up to 10 Minimum Wage: Credit Card数据处于定期更新的状态，数据来源于National Confederation of Commerce of Goods, Services and Tourism，数据归类于Brazil Premium Database的Domestic Trade and Household Survey – Table BR.HG003: Consumer Debt and Default Survey: by Type of Debt。

The table shows the level of bank credit to households (both mortgage credit and consumer credit) around the world including the most recent value and recent changes. The numbers are in billion local currency units and are updated continuously as the national authorities release the new data. Household credit carries benefits and risks to the economy. On the positive side, it allows households to purchase real estate, cars, and other items by spreading the cost over time. This makes household consumption more even over time and not so dependent on fluctuations in incomes. On the negative side, many financial crises are associated with a massive build up in household credit. Easy money pushes up property values and raises the debt levels. Then, an increase in interest rates or a drop in incomes can put significant strain on the household budgets. Households cut their spending in order to deleverage (reduce their debt) and the economy enters a recession. Household credit is now a major component of bank credit in the advanced economies and is rapidly catching up with the levels of business credit in the developing world.

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.

Credit card issuers generate revenue from cardholders primarily through fees and interest earned on revolving credit. Companies compete by offering customers lower interest rates, flexible and secure payment options and rewards programs based on spending levels. Over the past five years, industry revenue has grown at a CAGR of 1.6% to $178.6 billion, including an expected jump of 0.6% in 2025 alone. Industry profit has climbed to 31.6% in 2025, up from 11.9% in 2020. Improving employment and consumer spending levels and promoting increases in revolving balances are expected to support performance. Revenue declined both in 2020 and 2021 due to the economic volatility. Since then, revenue has crawled along, as the consumer price index has climbed which has contributed to the aggregate household debt to jump as consumers are increasingly using their credit cards for purchases, pushing demand and revenue higher. Competing economic trends and technology adoption will determine industry growth. Performance will continue to improve as consumer spending keeps increasing. However, while national unemployment is likely to decline and support demand for credit cards, Federal Reserve Board actions to stem inflation may threaten revenue generation. In addition, mounting industry competition in rewards programs will challenge profit margins. External competitive threats from companies providing Buy Now Pay Later expand consumers' credit options. These appealing new low or no-interest financing plans offered directly from sellers on social media platforms seamlessly link products to payment, bypassing industry operators' similar payment offerings. Emerging technologies like cryptocurrencies and artificial intelligence systems represent a significant opportunity for credit card issuers to secure market share and reduce costs. Overall, credit card issuing revenue is set to increase at a CAGR of 0.8% to $185.9 billion over the five years to 2030.

🏦 Synthetic Loan Approval Dataset

A Realistic, High-Quality Dataset for Credit Risk Modelling

🎯 Why This Dataset?

Most loan datasets on Kaggle have unrealistic patterns where:

1. ❌ Credit scores don't matter
2. ❌ Approval logic is backwards
3. ❌ Models learn nonsense patterns

Unlike most loan datasets available online, this one is built on real banking criteria from US and Canadian financial institutions. Drawing from 3 years of hands-on finance industry experience, the dataset incorporates realistic correlations and business logic that reflect how actual lending decisions are made. This makes it perfect for data scientists looking to build portfolio projects that showcase not just coding ability, but genuine understanding of credit risk modelling.

📊 Dataset Overview

🔑 Key Features

Identifier:

-Customer ID (unique identifier for each application)

Demographics:

-Age, Occupation Status, Years Employed

Financial Profile:

-Annual Income, Credit Score, Credit History Length -Savings/Assets, Current Debt

Credit Behaviour:

-Defaults on File, Delinquencies, Derogatory Marks

Loan Request:

-Product Type, Loan Intent, Loan Amount, Interest Rate

Calculated Ratios:

-Debt-to-Income, Loan-to-Income, Payment-to-Income

💡 What Makes This Dataset Special?

1️⃣ Real-World Approval Logic The dataset implements actual banking criteria: - DTI ratio > 50% = automatic rejection - Defaults on file = instant reject - Credit score bands match real lending thresholds - Employment verification for loans ≥$20K

2️⃣ Realistic Correlations - Higher income → Better credit scores - Older applicants → Longer credit history - Students → Lower income, special treatment for small loans - Loan intent affects approval (Education best, Debt Consolidation worst)

3️⃣ Product-Specific Rules - Credit Cards: More lenient, higher limits - Personal Loans: Standard criteria, up to $100K - Line of Credit: Capped at $50K, manual review for high amounts

4️⃣ Edge Cases Included - Young applicants (age 18) building first credit - Students with thin credit files - Self-employed with variable income - High debt-to-income ratios - Multiple delinquencies

🎓 Perfect For - Machine Learning Practice: Binary classification with real patterns - Credit Risk Modelling: Learn actual lending criteria - Portfolio Projects: Build impressive, explainable models - Feature Engineering: Rich dataset with meaningful relationships - Business Analytics: Understand financial decision-making

📈 Quick Stats

Approval Rates by Product - Credit Card: 60.4% more lenient) - Personal Loan: 46.9 (standard) - Line of Credit: 52.6% (moderate)

Loan Intent (Best → Worst Approval Odds) 1. Education (63% approved) 2. Personal (58% approved) 3. Medical/Home (52% approved) 4. Business (48% approved) 5. Debt Consolidation (40% approved)

Credit Score Distribution - Mean: 644 - Range: 300-850 - Realistic bell curve around 600-700

Income Distribution - Mean: $50,063 - Median: $41,608 - Range: $15K - $250K

🎯 Expected Model Performance

With proper feature engineering and tuning: - Accuracy: 75-85% - ROC-AUC: 0.80-0.90 - F1-Score: 0.75-0.85

Important: Feature importance should show: 1. Credit Score (most important) 2. Debt-to-Income Ratio 3. Delinquencies 4. Loan Amount 5. Income

If your model shows different patterns, something's wrong!

🏆 Use Cases & Projects

Beginner - Binary classification with XGBoost/Random Forest - EDA and visualization practice - Feature importance analysis

Intermediate - Custom threshold optimization (profit maximization) - Cost-sensitive learning (false positive vs false negative) - Ensemble methods and stacking

Advanced - Explainable AI (SHAP, LIME) - Fairness analysis across demographics - Production-ready API with FastAPI/Flask - Streamlit deployment with business rules

⚠️ Important Notes

This is SYNTHETIC Data - Generated based on real banking criteria - No real customer data was used - Safe for public sharing and portfolio use

Limitations - Simplified approval logic (real banks use 100+ factors) - No temporal component (no time series) - Single country/currency assumed (USD) - No external factors (economy, market conditions)

Educational Purpose This dataset is designed for: - Learning credit risk modeling - Portfolio projects - ML practice - Understanding lending criteria

NOT for: - Actual lending decisions - Financial advice - Production use without validation

🤝 Contributing

Found an issue? Have suggestions? - Open an issue on GitHub - Suggest i...

Credit card payments in Croatia grew by ****** million transactions in 2024, although per capita use was still below *****. Nevertheless, the increasing use of credit cards in the country is in stark contrast to developments elsewhere. The credit card transactions per capita in Bulgaria, for example, declined during this time - presumably as consumers did not want to build up credit card debt during COVID-19 lockdown. Consequently, the use of cash in Croatia declined - although estimates state coins and banknotes still made up more than half of all POS transactions.