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).

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 “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 average for 2021 based on 36 countries was 46.87 percent. The highest value was in Canada: 82.74 percent and the lowest value was in Lithuania: 11.89 percent. The indicator is available from 2011 to 2021. Below is a chart for all countries where data are available.

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.

Indonesia: Percent of people aged 15+ who have a credit card: The latest value from 2021 is 1.6 percent, a decline from 2.44 percent in 2017. In comparison, the world average is 22.26 percent, based on data from 121 countries. Historically, the average for Indonesia from 2011 to 2021 is 1.54 percent. The minimum value, 0.5 percent, was reached in 2011 while the maximum of 2.44 percent was recorded in 2017.

Bangladesh: Percent of people aged 15+ who have a credit card: The latest value from 2021 is 0.62 percent, an increase from 0.2 percent in 2017. In comparison, the world average is 22.26 percent, based on data from 121 countries. Historically, the average for Bangladesh from 2011 to 2021 is 0.53 percent. The minimum value, 0.2 percent, was reached in 2017 while the maximum of 0.95 percent was recorded in 2011.

Context

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

Content

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.

Update (03/05/2021)

A simulator for transaction data has been released as part of the practical handbook on Machine Learning for Credit Card Fraud Detection - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html. We invite all practitioners interested in fraud detection datasets to also check out this data simulator, and the methodologies for credit card fraud detection presented in the book.

Acknowledgements

The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project

Please cite the following works:

Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon

Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE

Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)

Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier

Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing

Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019

Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019

Yann-Aël Le Borgne, Gianluca Bontempi Reproducible machine Learning for Credit Card Fraud Detection - Practical Handbook

Bertrand Lebichot, Gianmarco Paldino, Wissam Siblini, Liyun He, Frederic Oblé, Gianluca Bontempi Incremental learning strategies for credit cards fraud detection, IInternational Journal of Data Science and Analytics

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).

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

Credit Card Spending Habits in India

Gender, Location, and Transaction Trends

By Sadat Akash [source]

About this dataset

This dataset contains insights into a collection of credit card transactions made in India, offering a comprehensive look at the spending habits of Indians across the nation. From the Gender and Card type used to carry out each transaction, to which city saw the highest amount of spending and even what kind of expenses were made, this dataset paints an overall picture about how money is being spent in India today. With its variety in variables, researchers have an opportunity to uncover deeper trends in customer spending as well as interesting correlations between data points that can serve as invaluable business intelligence. Whether you're interested in learning more about customer preferences or simply exploring unbiased data analysis techniques, this data is sure to provide insight beyond what one could anticipate

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How to use the dataset

• To get started with this dataset, you first need to select the columns you want to analyze. Once your columns are selected, use pivot tables to create a summary of the total amount spent by month or city or other parameters of analysis. Some suggested analysis would include factors such as gender, seasonality/timing of spending etc which can help to better understand Indian consumer behaviour related to credit cards as well as provide insights into personal finance management that could be useful for improved financial decisions.
• Once a summary table is created from the selected columns it could be useful to add more detailed breakdowns by combining multiple criteria such as ‘amount’ with ‘expense type’ or ‘date’ etc., this way more informative visuals and summaries can be generated which could then again help in forming better conclusions about financial habits within India related to Credit Card usage trends and recommendations for future improvement measures if needed .
• Additionally , if available other external information (i.e population size/density/income levels etc.)could also be compared with these findings so further actionable areas of focus can be identified on an overall level or credited towards specific buyer personas / cities etc.

Research Ideas

• To analyze consumer trends and interests by looking at the type of purchases people make based on their gender and city.
• To detect potential credit card fraud or malicious activity, such as by analyzing changes in spending habits or unusual purchases, by city and gender.
• To predict spending patterns for promotional campaigns, such as during festivals or holidays, in order to better target customer segments according to city and gender based spending habits

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: Credit card transactions - India - Simple.csv | Column name | Description | |:--------------|:--------------------------------------------------------------| | City | The city in which the transaction took place. (String) | | Date | The date of the transaction. (Date) | | Card Type | The type of credit card used for the transaction. (String) | | Exp Type | The type of expense associated with the transaction. (String) | | Gender | The gender of the cardholder. (String) | | Amount | The amount of the transaction. (Number) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Sadat Akash.

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The United Kingdom: Percent of people aged 15+ who have a credit card: The latest value from 2021 is 62.11 percent, a decline from 65.37 percent in 2017. In comparison, the world average is 22.26 percent, based on data from 121 countries. Historically, the average for the United Kingdom from 2011 to 2021 is 60.18 percent. The minimum value, 51.56 percent, was reached in 2011 while the maximum of 65.37 percent was recorded in 2017.

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.

Graph and download economic data for Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks (CCLACBW027SBOG) from 2000-06-28 to 2025-05-28 about revolving, credit cards, loans, consumer, banks, depository institutions, and USA.

Cambodia: Percent of people aged 15+ who have a credit card: The latest value from 2021 is 0.24 percent, a decline from 0.55 percent in 2017. In comparison, the world average is 22.26 percent, based on data from 121 countries. Historically, the average for Cambodia from 2011 to 2021 is 0.96 percent. The minimum value, 0.14 percent, was reached in 2011 while the maximum of 2.89 percent was recorded in 2014.

The credit card penetration in France was forecast to continuously increase between 2024 and 2029 by in total 0.4 percentage points. According to this forecast, in 2029, the credit card penetration will have increased for the eighth consecutive year to 40.65 percent. 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 Belgium and Netherlands.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Total Balances (RCCCBBALTOT) from Q3 2012 to Q4 2024 about FR Y-14M, consumer credit, credit cards, large, balance, loans, consumer, banks, depository institutions, and USA.

Debt Balance Credit Cards in the United States decreased to 1.18 Trillion USD in the first quarter of 2025 from 1.21 Trillion USD in the fourth quarter of 2024. This dataset includes a chart with historical data for the United States Debt Balance Credit Cards.

Inequalities in Financial Services

Bank Accounts and Credit Cards

By Dataquest [source]

About this dataset

Explore the world of consumer finance with this dataset from the Consumer Financial Protection Bureau. This data set includes a rich compilation of detailed bank and credit card customer complaints and provides an invaluable insight into customer experiences in the financial sector. With over [number] records spanning across [date stream], this data set is ideal for researchers, policymakers, financial institutions and anyone looking to learn more about consumer finance.

For each record in the dataset, you'll find details such as date received, product name, issue category, consumer complaint narrative, company response to customer enquiries, state origin of complaint (where appropriate) , even tags associated with the complaint. You can also uncover how timely the company responded to customer query usingthe Timely Response? field or whether customers disputed a firm's reply with Consumer Disputed? field. Utilizing all these features along with deep analysis can aid businesses in creating better consumer experiences prepared explainable models on root causes responsible for issues like disputes or late-responses ultimately leadingtoindustrywidepolicy change that benefit customers alike. Enjoyed exploring data? Hop online to check out additional records available at https://www.consumerfinance.gov/data-research/consumer-complaints/#download-the-data . This dataset is released under Public Domain Licensing Info which meant everyone’s free access!

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How to use the dataset

It is important to note that some fields are optional and missing values are expected for those fields due to how many data points had been reported at time of collection. It can be beneficial to list all unrecorded information separately for comparison considerations if relevant for your research needs.

The data points found within this dataset can not only help you explore differences between experiences based on non-similar factors such as age but also broaden understanding into more specific discussions such as identifying racial disparities in access new types of technology like mobile banking applications versus traditional forms like checks or savings accounts. By using this tool along with other sources of information you should be able create a comprehensive picture regarding both individual's differences experiences in addition broader trends applicable across large swaths impacted people on both local and national levels. These findings could then be used potentially lead positive changes into institutions responsible providing us with these services over time alongside continued evaluation if growth has effectively occurred .

Research Ideas

• Identifying states and specific areas with the highest number of financial complaints to target education and awareness initiatives.
• Analyzing trends in complaint investigations to improve customer service response times and accuracy rates.
• Developing a machine learning model that can accurately predict if a company will respond to a financial complaint in a timely manner

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: Bank_Account_or_Service_Complaints.csv | Column name | Description | |:---------------------------------|:------------------------------------------------------------------------------------| | Date received | The date the complaint was received by the CFPB. (Date) | | Product | The type of financial product or service the complaint is related to. (Text) | | Sub-product | The sub-category of the product the complaint is related to. (Text) | | Issue | The issue the consumer is complaining about. (Text) | | Sub-issue | The sub-category of the issue the consumer is complaining about. (Text) | | Consumer complaint narrative | The narrative of the complaint provided by the consumer. (Text) | | Company public response | The public response from the company regarding the complaint. (Text) ...

Graph and download economic data for Large Bank Consumer Credit Card Balances: Share of Accounts Making the Minimum Payment (RCCCBSHRMIN) from Q3 2012 to Q4 2024 about shares, accounts, FR Y-14M, payments, consumer credit, large, balance, loans, consumer, banks, depository institutions, and USA.