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 statistics by age group (Financial Information Service Co., Ltd.)

From the selected regions, the ranking by number of credit cards in use is led by the United States with 1.1 billion cards and is followed by Japan (295.11 million cards). In contrast, the ranking is trailed by Saudi Arabia with 2.73 million cards, recording a difference of 1.1 billion cards to the United States. Shown is the estimated number of credit cards currently in use.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).

Financial statistics credit card company information is data that provides general information, financial structure, management performance, and main business activities of credit card companies based on the base year and month and card company name. You can comprehensively analyze the market share, profitability, and soundness of card companies. This data consists of four operations. Each operation is as follows. ① Credit card company general status inquiry: Provides basic information on card companies such as establishment date, head office location, and card issuer status. ② Credit card company financial status inquiry: You can inquire about financial status items such as assets, liabilities, and equity capital. ③ Credit card company key management indicator inquiry: Provides management performance indicators such as ROA, ROE, and return on total assets. ④ Credit card company key business activities inquiry: Provides card company business performance such as usage amount, number of affiliated stores, and card payment delinquency rate.

The credit card penetration in Brazil was forecast to continuously increase between 2024 and 2029 by in total 16.6 percentage points. After the twelfth consecutive increasing year, the credit card penetration is estimated to reach 62.27 percent and therefore a new peak in 2029. 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).

Credit card contract store monthly sign-up statistics table (Financial Union Credit Information Center)

Global Credit card payments market size was valued at $571.09 Bn in 2023 & is predicted to grow $1,220.02 Bn by 2032 at CAGR of 8.8% from 2024 - 2032.

As required by the Credit CARD Act of 2009, we collect information annually from credit card issuers who have marketing agreements with universities, colleges, or affiliated organizations such as alumni associations, sororities, fraternities, and foundations.

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.

Explore consumer and credit card loans data in Saudi Arabia, including information on maturity terms, categories such as tourism, vehicles, education, health care, and more. Access quarterly and annual data on total credit card loans, with a focus on medium, long, and short-term personal loan options.

Consumer Loans, Tourism, Maturity Terms, Medium Term, Education, Health Care, Vehicles, Bank, SAMA Quarterly

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..Author Notes: The data from Q3 2017 to Q2 2019 have been updated.The dataset excludes real estate financing, financial leasing, and margin lending financing against shares."Total Credit Card Loans" Includes Visa, Master Card, American Express, and Others."Maturity Terms Of Personal Loans" represents loans granted by commercial banks to natural persons for financing personal, consumer and non-commercial purposes.For the data before 2014, the items of Furniture & Durable Goods, Education, Health care, Tourism and travel were included under 'Others'. "Short Term" : Less than one year"Medium Term" : 1 - 3 Years"Long Term" : Over 3 Years Loaans granted by commercial banks to natural persons for financing personal and consumer needs and for non-commercial purposes.

Different genders credit card revolving credit amount statistics form (Financial Alliance Credit Center) [Gender Equality]

We establish new facts about the way consumers allocate debt among their credit cards using data for a representative sample of cardholders in Mexico. We find that relative prices are weak predictors of the allocation of debt, purchases, and payments. Consumers allocate a large fraction of their debt to high-interest cards, incurring a cost of 31 percent above the minimum. Using an experiment, we find that consumers do not substitute in the price margin, although they respond to salient temporary low-interest offers. We conclude that limited attention and mental accounting best rationalize our results and discuss implications for the market.

The TCCP is a semi-annual survey on the terms of credit card plans offered by over 150 financial institutions. Twice per year, the Bureau is required by law to collect certain credit card price and availability information from a sample of credit card issuers and report this information to Congress and the public. The largest credit card issuers in the country are required to participate in the survey as well as a sample of geographically diverse issuers.

Overview: This record includes three datasets collected by the Consumer Financial Protection Bureau: Marketing Agreements and DataStudent Banking Reports to CongressDeposit Product Marking Agreements and DataCollege credit card marketing agreements and dataAs required by the Credit CARD Act of 2009, the Consumer Financial Protection Bureau (CFPB) collects information annually from credit card issuers who have marketing agreements with universities, colleges, or affiliated organizations such as alumni associations, sororities, fraternities, and foundations.The CFPB intends to continue updating the CSV file each year as it collects new data from college credit card issuers. The CFPB intends to ensure that the publicly available dataset is as accurate and complete as possible. This means that the dataset (as well as some of the charts and figures in this report) may not be completely consistent with past iterations of this report because submitting entities sometimes make corrections to earlier submissions. In all cases, the CFPB intends for the public dataset to be the CFPB’s definitive account of the data and it will be updated each year as new data becomes availableStudent banking reports to CongressThe Dodd-Frank Wall Street Reform and Consumer Protection Act of 2010 instructs the Bureau to monitor for risks to consumers in the offering or provision of consumer financial products or services, particularly when those products pose a disproportionate risk to traditionally underserved populations.College deposit product marketing agreements and dataThis page presents information about banking products provided to college students pursuant to agreements between institutions of higher education and financial service providers and governed in part by the Department of Education's cash management regulations.The agreements and related information presented here are a sample of the data used in the CFPB's annual report to Congress and should not be considered comprehensive. The scope of the CFPB's observations was limited to the agreements and other public disclosures that were published by institutions related to each award year (interested parties should note that any information in place at the time of publication but absent from the institutional disclosures as of June of each award year may not have been evaluated). Nevertheless, review of publicly available information is helpful in providing an overview of significant market dynamics at a point in time.The CFPB intends to ensure that the publicly available dataset is as accurate and complete as possible. This means that the dataset may not be completely consistent with past iterations of this report because the CFPB sometimes makes corrections to the dataset. In all cases, the CFPB intends for the public dataset to be the CFPB’s definitive account of the data.

Korea Credit Card: Number of Transactions (NT) data was reported at 1,072,938.000 Unit th in Apr 2018. This records an increase from the previous number of 1,050,391.000 Unit th for Mar 2018. Korea Credit Card: Number of Transactions (NT) data is updated monthly, averaging 491,639.500 Unit th from Jan 2003 (Median) to Apr 2018, with 184 observations. The data reached an all-time high of 1,072,938.000 Unit th in Apr 2018 and a record low of 163,131.000 Unit th in Feb 2004. Korea Credit Card: Number of Transactions (NT) 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. The data covers information from the Bank of Korea (BOK-Wire+), KFTC (Retail Payment Systems), credit card companies, and electronic financial business entities.

The credit card penetration in India was forecast to continuously increase between 2024 and 2029 by in total 0.8 percentage points. After the seventh consecutive increasing year, the credit card penetration is estimated to reach 5.61 percent and therefore a new peak in 2029. 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 Nepal and Pakistan.

Japan Info Needs Upon Arriving: China: Credit Card Store data was reported at 75.000 Person in Jun 2018. This records a decrease from the previous number of 88.000 Person for Mar 2018. Japan Info Needs Upon Arriving: China: Credit Card Store data is updated quarterly, averaging 81.500 Person from Mar 2018 (Median) to Jun 2018, with 2 observations. The data reached an all-time high of 88.000 Person in Mar 2018 and a record low of 75.000 Person in Jun 2018. Japan Info Needs Upon Arriving: China: Credit Card Store data remains active status in CEIC and is reported by Ministry of Land, Infrastructure, Transport and Tourism. The data is categorized under Global Database’s Japan – Table JP.Q034: Tourism and Leisure: Information Needs Upon Arriving.

The credit card penetration in Canada was forecast to continuously increase between 2024 and 2029 by in total 1.4 percentage points. After the seventh consecutive increasing year, the credit card penetration is estimated to reach 84.55 percent and therefore a new peak in 2029. 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 United States and Mexico.

A Credit Card Dataset for Machine Learning!

Don't ask me where this data come from, the answer is I don't know!

Context

Credit score cards are a common risk control method in the financial industry. It uses personal information and data submitted by credit card applicants to predict the probability of future defaults and credit card borrowings. The bank is able to decide whether to issue a credit card to the applicant. Credit scores can objectively quantify the magnitude of risk.

Generally speaking, credit score cards are based on historical data. Once encountering large economic fluctuations. Past models may lose their original predictive power. Logistic model is a common method for credit scoring. Because Logistic is suitable for binary classification tasks and can calculate the coefficients of each feature. In order to facilitate understanding and operation, the score card will multiply the logistic regression coefficient by a certain value (such as 100) and round it.

At present, with the development of machine learning algorithms. More predictive methods such as Boosting, Random Forest, and Support Vector Machines have been introduced into credit card scoring. However, these methods often do not have good transparency. It may be difficult to provide customers and regulators with a reason for rejection or acceptance.

Task

Build a machine learning model to predict if an applicant is 'good' or 'bad' client, different from other tasks, the definition of 'good' or 'bad' is not given. You should use some techique, such as vintage analysis to construct you label. Also, unbalance data problem is a big problem in this task.

Content & Explanation

There're two tables could be merged by ID:

Release Date: 2021-05-06.Release Schedule:.The data in this file come from the 2017 Economic Census. For information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.Includes only establishments of firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records:.Number of establishments.Sales, value of shipments, or revenue ($1,000).Sales, value of shipments, or revenue of NAPCS products relating to this inquiry ($1,000).Distribution of credit card products income (%).Response coverage of credit card products income inquiry (%)..Each record includes a code which represents a specific source of credit card products income category...Geography Coverage:.The data are shown for employer establishments at the U.S. level only. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown for selected 6- and 7-digit 2017 NAICS codes. For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector52/EC1752CCARD.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.