The market size for credit cards in the United States grew by over six percent between 2022 and 2023, a continuation of previous years. This according to estimates from on the value of transactions conducted with cards with a credit function. Credit cards are the most popular payment method available in the country for several years in a row, with a market share that slightly increased during the first year of COVID-19. The United States' credit card penetration is forecast to reach more than 65 percent come 2025.

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.

The value of credit card purchases made in the UK grew to a record in July 2024, at 22.36 billion British pounds. It contrasted the low point in April 2020, when monthly credit card spending fell to approximately 7.8 billion British pounds. As of November 2024 the credit card purchases made it to 20.84 billion British pounds.

Predict revenue surprises, track market share, and compare performance metrics for thousands of companies based on anonymized debit and credit card data of millions of US households. Orion data is sourced from a variety of US financial institutions with broad geographic and demographic representation, combined to create one of the most comprehensive and accurate views of the consumer economy. AI-powered earnings predictions available for over 450 tickers on this dataset through EarnestAI Reported Metric Predictions.

In 2023, the value of credit card spending in China amounted to 8.69 trillion yuan. Credit card usage buckled under the pressure of increasingly widespread mobile payments. The growth rate of credit card spending had been declining over the past years.

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.

The value of payments made with cards that have a credit function in Austria from 2006 to 2020 increased with some fluctuation during the period observed with a significant decrease in 2020. In 2013, the value of payments amounted to approximately five billion euros, by 2019 this had increased to roughly 6.4 billion euros, an increase of 1.4 billion euros. In 2020, the value dropped to approximately five billion euros. Credit cards have become increasingly popular over this period, with around 0.48 credit cards per capita in 2019. Originally denominated in euros, the data was converted to U.S. dollars by Statista to allow for cross-country comparisons worldwide.

Hong Kong SAR (China) Total Number Of Credit Card Transactions: RS: Hong Kong Spending data was reported at 250,868.000 Number th in Dec 2024. This records an increase from the previous number of 246,085.000 Number th for Sep 2024. Hong Kong SAR (China) Total Number Of Credit Card Transactions: RS: Hong Kong Spending data is updated quarterly, averaging 171,897.000 Number th from Dec 2013 (Median) to Dec 2024, with 45 observations. The data reached an all-time high of 250,868.000 Number th in Dec 2024 and a record low of 101,854.000 Number th in Mar 2014. Hong Kong SAR (China) Total Number Of Credit Card Transactions: RS: Hong Kong Spending data remains active status in CEIC and is reported by Hong Kong Monetary Authority. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.KA004: Statistics of Payment Cards Issued in Hong Kong. [COVID-19-IMPACT]

Thailand Credit Card: Spending Value: DU: Foreign Issued Card data was reported at 16,825.900 THB mn in May 2018. This records a decrease from the previous number of 18,944.350 THB mn for Apr 2018. Thailand Credit Card: Spending Value: DU: Foreign Issued Card data is updated monthly, averaging 13,265.450 THB mn from Jan 2005 (Median) to May 2018, with 161 observations. The data reached an all-time high of 25,584.880 THB mn in Jan 2018 and a record low of 5,281.500 THB mn in Mar 2005. Thailand Credit Card: Spending Value: DU: Foreign Issued Card 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.

Thailand Credit Card: Spending Value: Abroad Usage data was reported at 10,895.540 THB mn in Sep 2018. This records an increase from the previous number of 10,887.000 THB mn for Aug 2018. Thailand Credit Card: Spending Value: Abroad Usage data is updated monthly, averaging 3,164.610 THB mn from Jan 1998 (Median) to Sep 2018, with 222 observations. The data reached an all-time high of 14,369.340 THB mn in Apr 2018 and a record low of 344.590 THB mn in Feb 1998. Thailand Credit Card: Spending Value: Abroad Usage 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.

The value of payments made with cards with a credit function in Germany increased overall between 2012 and 2023. In 2012, the value of credit card payments amounted to approximately 3.28 billion euros in the country. In 2023, it exceeded 13 billion euros. Originally denominated in euros, the data was converted to U.S. dollars by Statista to allow for cross-country comparisons worldwide.

Thailand Credit Card: Spending Value data was reported at 161,527.030 THB mn in May 2018. This records an increase from the previous number of 160,486.880 THB mn for Apr 2018. Thailand Credit Card: Spending Value data is updated monthly, averaging 77,718.080 THB mn from Jan 1998 (Median) to May 2018, with 218 observations. The data reached an all-time high of 201,724.670 THB mn in Dec 2017 and a record low of 10,953.094 THB mn in Sep 1998. Thailand Credit Card: Spending Value 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.

Thailand Credit Card: Spending Value: Cash Advance: CB: Other Corporations data was reported at 3,476.530 THB mn in Sep 2018. This records a decrease from the previous number of 4,273.420 THB mn for Aug 2018. Thailand Credit Card: Spending Value: Cash Advance: CB: Other Corporations data is updated monthly, averaging 4,689.890 THB mn from Jan 2012 (Median) to Sep 2018, with 81 observations. The data reached an all-time high of 7,239.800 THB mn in Jan 2013 and a record low of 3,278.880 THB mn in Sep 2014. Thailand Credit Card: Spending Value: Cash Advance: CB: Other Corporations 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.

Thailand Credit Card: Spending Value: Abroad Usage: CB: Bank Card data was reported at 0.000 THB mn in May 2018. This stayed constant from the previous number of 0.000 THB mn for Apr 2018. Thailand Credit Card: Spending Value: Abroad Usage: CB: Bank Card data is updated monthly, averaging 0.000 THB mn from Jan 2012 (Median) to May 2018, with 77 observations. Thailand Credit Card: Spending Value: Abroad Usage: CB: Bank Card 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.

Thailand Credit Card: Spending Value: CB: Affinity Card data was reported at 88,992.270 THB mn in May 2018. This records an increase from the previous number of 87,046.590 THB mn for Apr 2018. Thailand Credit Card: Spending Value: CB: Affinity Card data is updated monthly, averaging 73,070.710 THB mn from Jan 2012 (Median) to May 2018, with 77 observations. The data reached an all-time high of 107,719.470 THB mn in Dec 2017 and a record low of 37,709.530 THB mn in Feb 2012. Thailand Credit Card: Spending Value: CB: Affinity Card 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.

Credit Card Spending in New Zealand remained unchanged at 6463 NZD Million in April. This dataset provides - New Zealand Credit Card Spending- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Envestnet®| Yodlee®'s Credit Card Spending Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform.

Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production.

We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data.

Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing?

Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics.

Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

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 “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.

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Credit Card Payments Market Size 2025-2029

The credit card payments market size is forecast to increase by USD 181.9 billion at a CAGR of 8.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing number of online transactions and the rise in mobile biometrics for secure payments. These trends reflect the shifting consumer preference towards convenient and secure payment methods. However, the market faces challenges in developing economies, where a lack of awareness and infrastructure for online payments persists. This creates both opportunities and obstacles for market participants. Companies can capitalize on the growing demand for secure digital payments by investing in innovative technologies and expanding their reach into emerging markets. Simultaneously, they must navigate regulatory complexities and address consumer education needs to effectively penetrate these markets. Overall, the market presents a dynamic and promising landscape for businesses seeking to capitalize on the growing trend towards digital payments while addressing the unique challenges of each market segment.

What will be the Size of the Credit Card Payments Market during the forecast period?

Request Free SampleThe market encompasses financial services providers issuing and processing payment cards for consumers and businesses. These financial transactions enable the borrowing of money for purchases during a billing cycle, with a pre-set credit limit. Credit cards have become a preferred alternative to cash and checks due to their convenience, security, and reward programs. However, the market faces challenges such as increasing competition and the risk of credit card fraud. Despite these challenges, the market's size and growth potential remain significant, driven by the increasing adoption of contactless payments and the integration of credit card technology into various industries, including e-commerce and mobile wallets. The market's future direction lies in enhancing security measures, expanding digital payment options, and offering personalized services to meet evolving consumer needs.

How is this Credit Card Payments Industry segmented?

The credit card payments industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userConsumer or individualCommercialProduct TypeGeneral purpose credit cardsSpecialty credit cardsOthersApplicationFood and groceriesHealth and pharmacyRestaurants and barsConsumer electronicsOthersGeographyNorth AmericaUSCanadaAPACChinaIndiaJapanSouth KoreaEuropeGermanyUKSouth AmericaArgentinaBrazilMiddle East and Africa

By End-user Insights

The consumer or individual segment is estimated to witness significant growth during the forecast period.Credit cards enable consumers to borrow money for purchases and repay over a specified billing cycle. A good credit record signifies a consumer's ability to pay bills on time and access credit easily with favorable terms. Conversely, a bad credit record indicates past difficulties in repaying debts. Credit card usage is prevalent in various sectors, including food and groceries, health and pharmacy, restaurants and bars, consumer electronics, media and entertainment, travel and tourism, and premium cards. Credit card fraud, such as skimmers, SQL injections, and fraudulent online applications, poses significant risks. Technical advances, including contactless payment technology, tokenization, and NFC technology, aim to enhance security. Financial services providers must address fraud risks and data breaches through security measures and consumer education. Digital payment methods, including mobile payments and cryptocurrencies, are gaining popularity, challenging traditional credit card payments. Credit card issuers, such as Visa and MasterCard, continue to innovate with loyalty programs, mobile payment networks, and e-commerce platforms. Consumers must remain vigilant against fraudulent activities, including identity theft, phishing emails, and payment app scams.

Get a glance at the market report of share of various segments Request Free Sample

The Consumer or individual segment was valued at USD 239.90 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 37% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The North American market holds significant potential for credit card payments due to its technologically advanced economies and high smartphone usage. Ke