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.

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.

Credit Card Transaction: Quarterly: Volume data was reported at 9,502.716 Unit mn in Jun 2022. This records an increase from the previous number of 9,301.651 Unit mn for Mar 2022. Credit Card Transaction: Quarterly: Volume data is updated quarterly, averaging 6,627.430 Unit mn from Mar 2019 (Median) to Jun 2022, with 14 observations. The data reached an all-time high of 9,502.716 Unit mn in Jun 2022 and a record low of 4,705.638 Unit mn in Jun 2020. Credit Card Transaction: Quarterly: Volume data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Global Database’s Brazil – Table BR.KAA001: Credit Card Statistics.

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.

The “Customer Credit Card Data” dataset provides valuable insights into credit card usage patterns and financial behavior. Each record represents an individual credit card holder, and the dataset includes the following features:

Id | Features | Description --|:---------|:----------- 1|**Cust_Id:** | Identification of credit card holder
2|**Balance:** | A credit card balance or Total amount left in their account to make purchases 3|**Balance_Frequency:** | How frequently the balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated) 4|**Purchases:** | Total amount of purchases made from account 5|**One_Off_Purchases:** | Maximum purchase amount done in one-go 6|**Installments_Purchases:** | Amount of purchase done in installment 7|**Cash_Advance:** | Cash in advance given by the user 8|**Purchases_Frequency:** | How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased)
9|**One_Off_Purchases_Frequency:** | How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased)
10|**Purchases_Installments_Frequency:** | How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done)
11|**Cash_Advance_Frequency:** | How frequently the cash in advance being paid
12|**Cash_Advance_Trx:** | Number of Transactions made with "Cash in Advanced"
13|**Purchases_Trx:** | Number of purchase transactions made
14|**Credit_Limit:** | Limit of Credit Card for user
15|**Payments:** | Total amount of payments done by user
16|**Minimum_Payments:** | Minimum amount of payments made by user 17|**Prc_Full_Payment:** | Percentage of full payment paid by user
18|**Tenure:** | Tenure of credit card service for user

This dataset is valuable for analyzing credit card behavior, identifying trends, and building predictive models related to credit usage. Researchers, analysts, and financial institutions can leverage this data to gain deeper insights into customer profiles and optimize credit card services.

The credit card penetration in Thailand was forecast to continuously increase between 2024 and 2029 by in total 36.8 percentage points. After the fifteenth consecutive increasing year, the credit card penetration is estimated to reach 67.53 percent and therefore a new peak in 2029. Notably, the credit card penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population who use credit cards.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the credit card penetration in countries like Malaysia and Philippines.

The Credit Card Transactions Dataset provides detailed records of credit card transactions, including information about transaction times, amounts, and associated personal and merchant details. This dataset has over 1.85M rows.

How This Dataset Can Be Used:

Fraud Detection : Use machine learning models to identify fraudulent transactions by examining patterns in transaction amounts, locations, and user profiles. Enhancing fraud detection systems becomes feasible by analyzing behavioral patterns.

Customer Segmentation : Segment customers based on spending patterns, location, and demographics. Tailor marketing strategies and personalized offers to these different customer segments for better engagement.

Transaction Classification : Classify transactions into categories such as grocery or entertainment to understand spending behaviors. This helps in improving recommendation systems by identifying transaction categories and preferences.

Geospatial Analysis : Analyze transaction data geographically to map spending patterns and detect regional trends or anomalies based on latitude and longitude.

Predictive Modeling : Build models to forecast future spending behavior using historical transaction data. Predict potential fraudulent activities and financial trends.

Behavioral Analysis : Examine how factors like transaction amount, merchant type, and time influence spending behavior. Study the relationships between user demographics and transaction patterns.

Anomaly Detection : Identify unusual transaction patterns that deviate from normal behavior to detect potential fraud early. Employ anomaly detection techniques to spot outliers and suspicious activities.

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…

Daily Card Payments by Irish Households. A subset of the monthly Card Payment Statistics. The onset of the Covid-19 pandemic created the need for timely, high-frequency data, such as the Daily Credit and Debit Card Statistics, to better understand the impact of the pandemic on personal expenditure and economic activity. This high-frequency daily Credit and Debit Data captures expenditure of euro-denominated credit and debit cards issued to Irish residents. The dataset consists of total daily debit and credit card spending and ATM withdrawals, while from 1 October 2020, expenditure in a number of key sectors of the economy, and a split of online and in-store spending is also available.

Credit Card: VT: Bank Affiliated: Personal: Lump Sum Payments data was reported at 20,755,933.000 KRW mn in Apr 2018. This records a decrease from the previous number of 21,137,159.000 KRW mn for Mar 2018. Credit Card: VT: Bank Affiliated: Personal: Lump Sum Payments data is updated monthly, averaging 11,439,943.000 KRW mn from Jan 2003 (Median) to Apr 2018, with 184 observations. The data reached an all-time high of 21,137,159.000 KRW mn in Mar 2018 and a record low of 4,112,347.000 KRW mn in Feb 2003. Credit Card: VT: Bank Affiliated: Personal: Lump Sum Payments data remains active status in CEIC and is reported by The Bank of Korea. The data is categorized under Global Database’s Korea – Table KR.KA032: Credit Card Statistics: The Bank of Korea.

Content

This dataset contains a cleaned version of this dataset https://www.kaggle.com/rikdifos/credit-card-approval-prediction on credit cards.

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.

China Credit Card Payable Credit data was reported at 8,710.000 RMB bn in Dec 2024. This records an increase from the previous number of 8,610.000 RMB bn for Sep 2024. China Credit Card Payable Credit data is updated quarterly, averaging 3,675.000 RMB bn from Mar 2008 (Median) to Dec 2024, with 68 observations. The data reached an all-time high of 8,760.000 RMB bn in Sep 2022 and a record low of 88.410 RMB bn in Mar 2008. China Credit Card Payable Credit data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s Money and Banking – Table CN.KC: Bank Card Statistics.

Description of the Credit Card Eligibility Data: Determining Factors

The Credit Card Eligibility Dataset: Determining Factors is a comprehensive collection of variables aimed at understanding the factors that influence an individual's eligibility for a credit card. This dataset encompasses a wide range of demographic, financial, and personal attributes that are commonly considered by financial institutions when assessing an individual's suitability for credit.

Each row in the dataset represents a unique individual, identified by a unique ID, with associated attributes ranging from basic demographic information such as gender and age, to financial indicators like total income and employment status. Additionally, the dataset includes variables related to familial status, housing, education, and occupation, providing a holistic view of the individual's background and circumstances.

Researchers, analysts, and financial institutions can leverage this dataset to gain insights into the key factors influencing credit card eligibility and to develop predictive models that assist in automating the credit assessment process. By understanding the relationship between various attributes and credit card eligibility, stakeholders can make more informed decisions, improve risk assessment strategies, and enhance customer targeting and segmentation efforts.

This dataset is valuable for a wide range of applications within the financial industry, including credit risk management, customer relationship management, and marketing analytics. Furthermore, it provides a valuable resource for academic research and educational purposes, enabling students and researchers to explore the intricate dynamics of credit card eligibility determination.

Thailand Credit Card: Number of Account data was reported at 20,865,219.000 Unit in May 2018. This records an increase from the previous number of 20,727,375.000 Unit for Apr 2018. Thailand Credit Card: Number of Account data is updated monthly, averaging 13,116,463.000 Unit from Jan 1998 (Median) to May 2018, with 218 observations. The data reached an all-time high of 21,592,128.000 Unit in Nov 2015 and a record low of 1,629,301.000 Unit in Dec 1999. Thailand Credit Card: Number of Account data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.KA012: Credit Card Statistics.

Dataset

This dataset was created by Jeetesh Rajpal 2

Contents

Korea Credit Card Statistics: Credit Card Company data was reported at 8.000 Unit in Jun 2018. This stayed constant from the previous number of 8.000 Unit for Mar 2018. Korea Credit Card Statistics: Credit Card Company data is updated quarterly, averaging 7.000 Unit from Mar 1999 (Median) to Jun 2018, with 78 observations. The data reached an all-time high of 9.000 Unit in Sep 2014 and a record low of 5.000 Unit in Sep 2009. Korea Credit Card Statistics: Credit Card Company data remains active status in CEIC and is reported by Financial Supervisory Service. The data is categorized under Global Database’s South Korea – Table KR.KA013: Credit Card Statistics: Financial Supervisory Service.

Indonesia Electronic Card: Transaction: Credit Card: Volume: Purchase data was reported at 29,583,676.000 Unit in Jul 2019. This records an increase from the previous number of 26,495,911.000 Unit for Jun 2019. Indonesia Electronic Card: Transaction: Credit Card: Volume: Purchase data is updated monthly, averaging 18,427,526.860 Unit from Jan 2006 (Median) to Jul 2019, with 163 observations. The data reached an all-time high of 29,940,025.000 Unit in Dec 2018 and a record low of 7,946,883.000 Unit in Feb 2006. Indonesia Electronic Card: Transaction: Credit Card: Volume: Purchase data remains active status in CEIC and is reported by Bank of Indonesia. The data is categorized under Global Database’s Indonesia – Table ID.KAG001: Electronic Card Statistics.

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.

Graph and download economic data for Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks (CCLACBW027SBOG) from 2000-06-28 to 2025-11-19 about revolving, credit cards, loans, consumer, banks, depository institutions, and USA.