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 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).

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).

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.

Average credit card receivables per account by age group (Central Bank of Financial Information Services).

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.

Average revolving credit amount per household by age group (Financial Information Center)

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.

Daily, weekly and monthly data showing seasonally adjusted and non-seasonally adjusted UK spending using debit and credit cards. These are official statistics in development. Source: CHAPS, Bank of England.

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.

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

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.

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.

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.

Credit Approval (Mixed Attributes)

Continuous and Categorical Features

By UCI [source]

About this dataset

This dataset explores the phenomenon of credit card application acceptance or rejection. It includes a range of both continuous and categorical attributes, such as the applicant's gender, credit score, and income; as well as details about recent credit card activity including balance transfers and delinquency. This data presents a unique opportunity to investigate how these different attributes interact in determining application status. With careful analysis of this dataset, we can gain valuable insights into understanding what factors help ensure a successful application outcome. This could lead us to developing more effective strategies for predicting and improving financial credit access for everyone

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How to use the dataset

This dataset is an excellent resource for researching the credit approval process, as it provides a variety of attributes from both continuous and categorical sources. The aim of this guide is to provide tips and advice on how to make the most out of this dataset. - Understand the data: Before attempting to work with this dataset, it's important to understand what kind of information it contains. Since there is a mix of continuous and categorical attributes in this data set, make sure you familiarise yourself with all the different columns before proceeding further. - Exploratory Analysis: It is recommended that you conduct some exploratory analysis on your data in order to gain an overall understanding of its characteristics and distributions. By investigating things like missing values and correlations between different independent variables (IVs) or dependent variables (DVs), you can better prepare yourself for making meaningful analyses or predictions in further steps. - Data Cleaning: Once you have familiarised yourself with your data, begin cleaning up any potential discrepancies such as missing values or outliers by replacing them appropriately or removing them from your dataset if necessary - Feature Selection/Engineering: After cleansing your data set, feature selection/engineering may be necessary if certain columns are redundant or not proving useful for constructing meaningful models/analyses over your data set (usually observed after exploratory analysis). You should be very mindful when deciding which features should be removed so that no information about potentially important relationships are lost!
- Model Building/Analysis: Now that our data has been pre-processed appropriately we can move forward with developing our desired models / analyses over our newly transformed datasets!

Research Ideas

• Developing predictive models to identify customers who are likely to default on their credit card payments.
• Creating a risk analysis system that can identify customers who pose a higher risk for fraud or misuse of their credit cards.
• Developing an automated lending decision system that can use the data points provided in the dataset (i.e., gender, average monthly balance, etc.) to decide whether or not to approve applications for new credit lines and loans

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: crx.data.csv | Column name | Description | |:--------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------| | b | Gender (Categorical) | | 30.83 | Average Monthly Balance (Continuous) | | 0 | Number of Months Since Applicant's Last Delinquency (Continuous) | | w | Number of Months Since Applicant's Last Credit Card Approval (Continuous) | | 1.25 | Number Of Months since The applicant's last balance increase (Continuous) ...

The number of credit cards in use in the United Kingdom was forecast to continuously decrease between 2024 and 2029 by in total 0.02 million cards (-0.03 percent). The number is estimated to amount to 63.62 million cards in 2029. 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).

The statistical table of the proportion of revolving credit amount to accounts receivable by age group for credit cards (Financial Information and Credit System of the Bankers Association).

Japan Info Needs Upon Arriving: UK: Credit Card Store data was reported at 14.000 Person in Mar 2018. Japan Info Needs Upon Arriving: UK: Credit Card Store data is updated quarterly, averaging 14.000 Person from Mar 2018 (Median) to Mar 2018, with 1 observations. Japan Info Needs Upon Arriving: UK: 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.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_5c8e5801c2a64e2e6b16608296ef3e02/view