Graph and download economic data for Total Revenue for Credit Card Issuing, Establishments Subject to Federal Income Tax, Employer Firms (REVEF52221TAXABL) from 2009 to 2022 about issues, employer firms, finance companies, credit cards, accounting, companies, revenue, establishments, finance, tax, financial, services, and USA.

Credit card issuers generate revenue from cardholders primarily through fees and interest earned on revolving credit. Companies compete by offering customers lower interest rates, flexible and secure payment options and rewards programs based on spending levels. Over the past five years, industry revenue has grown at a CAGR of 1.6% to $178.6 billion, including an expected jump of 0.6% in 2025 alone. Industry profit has climbed to 31.6% in 2025, up from 11.9% in 2020. Improving employment and consumer spending levels and promoting increases in revolving balances are expected to support performance. Revenue declined both in 2020 and 2021 due to the economic volatility. Since then, revenue has crawled along, as the consumer price index has climbed which has contributed to the aggregate household debt to jump as consumers are increasingly using their credit cards for purchases, pushing demand and revenue higher. Competing economic trends and technology adoption will determine industry growth. Performance will continue to improve as consumer spending keeps increasing. However, while national unemployment is likely to decline and support demand for credit cards, Federal Reserve Board actions to stem inflation may threaten revenue generation. In addition, mounting industry competition in rewards programs will challenge profit margins. External competitive threats from companies providing Buy Now Pay Later expand consumers' credit options. These appealing new low or no-interest financing plans offered directly from sellers on social media platforms seamlessly link products to payment, bypassing industry operators' similar payment offerings. Emerging technologies like cryptocurrencies and artificial intelligence systems represent a significant opportunity for credit card issuers to secure market share and reduce costs. Overall, credit card issuing revenue is set to increase at a CAGR of 0.8% to $185.9 billion over the five years to 2030.

In the fiscal year 2024, the operating revenue of Rakuten Card amounted to around ***** billion Japanese yen. Rakuten Card is a subsidiary of Rakuten Group that was established in 2001. It issues credit cards and offers related services, partly in cooperation with other companies in the Rakuten Group.

In the fiscal year 2024, the revenue from the credit card business of AEON Financial Service Co., Ltd. amounted to around ***** billion Japanese yen, up from about ***** billion yen in the previous year. AEON Financial Service is a financial services group that is part of AEON Group.

United States - Total Revenue for Credit Card Issuing, All Establishments, Employer Firms was 160564.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Total Revenue for Credit Card Issuing, All Establishments, Employer Firms reached a record high of 160564.00000 in January of 2022 and a record low of 81454.00000 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Total Revenue for Credit Card Issuing, All Establishments, Employer Firms - last updated from the United States Federal Reserve on November of 2025.

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.

Credit card processors and money transferring companies have witnessed substantial growth fueled by an expanding adoption of electronic payments. Recent trends show a remarkable increase in electronic transactions, with more businesses embracing a credit card-friendly approach. This has directly contributed to burgeoning revenue streams for providers. The heightened use of debit and credit cards, along with solid economic growth that has bolstered consumer spending and per capita disposable income, underpin this upward trajectory. Additionally, digitization trends, accelerated by the push toward e-commerce, have further cemented the integration of cards in everyday transactions, demonstrating the industry's resilience and adaptability to evolving market demands. Despite these positive trends, shifting economic conditions have significantly impacted revenue volatility for credit card processors and money transfer services. Initially, the pandemic reduced consumer spending, leading to a decreased demand for these services in 2020. Despite this, e-commerce sales surged, permitting some stability in revenue. As the US economy reopened, consumer spending increased, leading to substantial revenue growth in 2021. However, rampant inflation in 2022 dampened e-commerce performance, yet high wage growth kept revenue positive. This inflation also caused consumers to bolster their use of credit cards to cover rising expenses, raising profitability. More recently, recessionary fears, spurred by higher interest rates, further constrained consumer spending and corporate expenditures, slowing growth. Despite these challenges, strong e-commerce activities have kept the industry resilient. Overall, revenue for credit card processing and money transferring companies has swelled at a CAGR of 6.7% over the past five years, reaching $146.3 billion in 2025. This includes a 2.8% rise in revenue in that year. Providers are expected to face a slew of negative and positive trends moving forward. Cash usage in the US has dropped significantly because of digitization and the convenience of credit and debit cards. This trend is expected to accelerate over the next five years as economic growth and pandemic-driven online shopping further shift consumer preferences to electronic payments. As a result, providers will need to innovate, investing in biometrics and AI to enhance efficiency and security. Policy changes like new tariffs and extended tax cuts are also set to impact consumer spending and providers’ revenue. Despite these uncertainties, continued GDP growth and rising consumer confidence are forecast to sustain high demand for digital payment services, benefiting the industry's largest players. Overall, revenue for credit card processing and money transferring companies in the United States is forecast to expand at a CAGR of 2.6% over the next five years, reaching $166.3 billion in 2030.

This statistic shows the revenue of the industry “credit card issuing“ in Texas from 2012 to 2017, with a forecast to 2024. It is projected that the revenue of credit card issuing in Texas will amount to approximately ******* million U.S. Dollars by 2024.

This report analyses the ratio of credit card debt to discretionary income. Credit card debt covers all personal advances on credit and charge cards, both interest-bearing and non-interest bearing, that are outstanding. Discretionary income is the amount of income remaining after deducting necessary household expenses and can be used to repay debt. The data for this report is sourced from the Reserve Bank of New Zealand (Te Putea Matua) and Statistics New Zealand (Tatauranga Aotearoa). The data is presented as credit card debt as a percentage of discretionary income for each financial year.

Global Credit Card market size 2021 was recorded $432.68 Billion whereas by the end of 2025 it will reach $580.2 Billion. According to the author, by 2033 Credit Card market size will become $1043.28. Credit Card market will be growing at a CAGR of 7.61% during 2025 to 2033.

In 2023, Spirit Airlines received over ** million U.S. dollars in consideration from credit card loyalty programs. A year earlier, the respective figure was nearly ** million dollars.

Global Credit Cards market size 2021 was recorded $532.748 Billion whereas by the end of 2025 it will reach $736.6 Billion. According to the author, by 2033 Credit Cards market size will become $1408.16. Credit Cards market will be growing at a CAGR of 8.437% during 2025 to 2033.

United States - Sources of Revenue: Credit Card Income from Consumers for Credit Intermediation and Related Activities, All Establishments, Employer Firms was 172640.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Credit Card Income from Consumers for Credit Intermediation and Related Activities, All Establishments, Employer Firms reached a record high of 172640.00000 in January of 2022 and a record low of 88677.00000 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Credit Card Income from Consumers for Credit Intermediation and Related Activities, All Establishments, Employer Firms - last updated from the United States Federal Reserve on November of 2025.

United States - Sources of Revenue: Credit Card Income from Businesses and Governments for Credit Intermediation and Related Activities, All Establishments, Employer Firms was 45065.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Sources of Revenue: Credit Card Income from Businesses and Governments for Credit Intermediation and Related Activities, All Establishments, Employer Firms reached a record high of 48768.00000 in January of 2018 and a record low of 28396.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Sources of Revenue: Credit Card Income from Businesses and Governments for Credit Intermediation and Related Activities, All Establishments, Employer Firms - last updated from the United States Federal Reserve on November of 2025.

In 2024, credit card purchase transactions in Japan amounted to about ***** trillion Japanese yen. This represented an increase of **** percent compared to the previous year. Credit cards have been the leading cashless payment method in recent years.

United States - Total Revenue for Credit Card Issuing, Establishments Subject to Federal Income Tax, Employer Firms was 160564.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Total Revenue for Credit Card Issuing, Establishments Subject to Federal Income Tax, Employer Firms reached a record high of 160564.00000 in January of 2022 and a record low of 81454.00000 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Total Revenue for Credit Card Issuing, Establishments Subject to Federal Income Tax, Employer Firms - last updated from the United States Federal Reserve on October of 2025.

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.

Business Context: Analytics driving every industry based on a variety of technology platforms which collect information from various sources by analysing what customers certainly want. The Credit Card industry is also data rich industry and data can be leveraged in infinite ways to understand customer behaviour. The data from a credit card processor shows the consumer types and their business spending behaviours. Therefore, companies can develop the marketing campaigns that directly address consumers’ behaviour. In return, this helps to make better sales and the revenue undoubtedly grows greater sales. Understanding the consumption pattern for credit cards at an individual consumer level is important for customer relationship management. This understanding allows banks to customize for consumers and make strategic marketing plans. Thus it is imperative to study the relationship between the characteristics of the consumers and their consumption patterns. Business Objectives: One of the leading banks provided below data a. Customer Demographics b. Customer Behavioural data (information on liabilities, assets and history of transactions with the bank for each customer). Data has been provided for a particular set of customers' credit card spend in the previous 3 months (April, May & June) and their expected average spend in the coming 3 months (July, August & September) c. Credit consumption Data Dictionary a. CustomerDemographics.csv ID – Customer ID - Unique ID for every Customer Account_type - Account Type (current or saving) Gender- Gender of customer (M or F) Age - Age of customer Income – Income Levels (High/Medium/Low) Emp_Tenure_Years – Experience – Employment Tenure of customer in Years Tenure_with_Bank – Number of years with bank Region_code Code assigned to region of residence (has order) NetBanking_Flag – Whether customer is using net banking for the transactions Avg_days_between_transaction – Average days between two transactions b. CustomerBehaviorData.csv ID – Customer ID - Unique ID for every Customer CC_cons_apr - Credit card spend in April DC_cons_apr - Debit card spend in April CC_cons_may - Credit card spend in May DC_cons_may - Debit card spend in May CC_cons_jun - Credit card spend in June DC_cons_jun - Debit card spend in June CC_count_apr - Number of credit card transactions in April CC_count_may - Number of credit card transactions in May CC_count_jun - Number of credit card transactions in June DC_count_apr - Number of debit card transactions in April DC_count_may - Number of debit card transactions in May DC_count_jun - Number of debit card transactions in June Card_lim - Maximum Credit Card Limit allocated Personal_loan_active - Active personal loan with other bank Vehicle_loan_active - Active Vehicle loan with other bank Personal_loan_closed - Closed personal loan in last 12 months Vehicle_loan_closed - Closed vehicle loan in last 12 months Investment_1 - DEMAT investment in june Investment_2 - Fixed deposit investment in june Investment_3 - Life Insurance investment in June Investment_4 - General Insurance Investment in June Debit_amount_apr - Total amount debited for April Credit_amount_apr - Total amount credited for April Debit_count_apr- Total number of times amount debited in april Credit_count_apr - Total number of times amount credited in april Max_credit_amount_apr - Maximum amount credited in April Debit_amount_may - Total amount debited for May Credit_amount_may - Total amount credited for May Credit_count_may - Total number of times amount credited in May Debit_count_may - Total number of times amount debited in May Max_credit_amount_may - Maximum amount credited in May Debit_amount_jun - Total amount debited for June Credit_amount_jun - Total amount credited for June Credit_count_jun - Total number of times amount credited in June Debit_count_jun - Total number of times amount debited in June Max_credit_amount_jun - Maximum amount credited in June Loan_enq - Loan enquiry in last 3 months (Y or N) Emi_active - Monthly EMI paid to other bank for active loans c. CreditConsumptionData.csv ID – Customer ID - Unique ID for every Customer cc_cons (Target) - Average Credit Card Spend in next three months Note: Some customers are having missing values for credit consumption. You need to build the model using customer’s data where credit consumption is non- missing’s. You need to predict the credit consumption for next three months for the customers having missing values. Model Evaluation Metric: You should validate model using Root Mean Square Percentage Error (RMSPE) between the predicted credit card consumption and Actual Credit Consumption. Expected Outputs: a. Detailed code with comments b. Data Exploratory analysis c. Model validation outputs d. Model documentation with all the details e. Predicted values for customers where target variable having missing values

Credit Card Issuing Revenue - Historical chart and current data through 2022.

This statistic shows the revenue of the industry “credit card issuing“ in Ohio from 2012 to 2017, with a forecast to 2024. It is projected that the revenue of credit card issuing in Ohio will amount to approximately ******* million U.S. Dollars by 2024.