A manager at the bank is disturbed with more and more customers leaving their credit card services. They would really appreciate if one could predict for them who is gonna get churned so they can proactively go to the customer to provide them better services and turn customers' decisions in the opposite direction

I got this dataset from a website with the URL as https://leaps.analyttica.com/home. I have been using this for a while to get datasets and accordingly work on them to produce fruitful results. The site explains how to solve a particular business problem.

Now, this dataset consists of 10,000 customers mentioning their age, salary, marital_status, credit card limit, credit card category, etc. There are nearly 18 features.

We have only 16.07% of customers who have churned. Thus, it's a bit difficult to train our model to predict churning customers.

When it comes to credit card users in the United States, 42 percent of 18 - 29 year olds do so in the U.S. This is according to exclusive insights from the Consumer Insights Global survey which shows that 49 percent of 30 - 49 year old consumers also fall into this category.Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than 2,000,000 interviews.

Graph and download economic data for Large Bank Consumer Credit Card Originations: Number of New Accounts (RCCCONUMACT) from Q3 2012 to Q4 2024 about accounts, FR Y-14M, origination, consumer credit, credit cards, large, new, loans, consumer, banks, depository institutions, and USA.

Analysis of ‘Credit Card Customer Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aryashah2k/credit-card-customer-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

A Customer Credit Card Information Dataset which can be used for Identifying Loyal Customers, Customer Segmentation, Targeted Marketing and other such use cases in the Marketing Industry.

A few tasks that can be performed using this dataset is as follows: - Perform Data-Cleaning,Preprocessing,Visualizing and Feature Engineering on the Dataset. - Implement Heirarchical Clustering, K-Means Clustering models. - Create RFM (Recency,Frequency,Monetary) Matrix to identify Loyal Customers.

Content

The Attributes Include: - Sl_No - Customer Key - AvgCreditLimit - TotalCreditCards - Totalvisitsbank - Totalvisitsonline - Totalcallsmade

--- Original source retains full ownership of the source dataset ---

Of the banking institutions in the United States that issued credit cards, Citibank and Chase were the only ones with over 80 million active accounts. This according to estimates based on either credit card expenses per issuer, or outstanding credit loans per issuer. Note that an issues like Chase does not issue their own branded credit cards, but use Visa or MasterCard instead. The number of Visa credit cards in circulation in the United States was over 300 million by 2020, whereas overall MasterCard credit cards were below that.

Why credit cards?

Credit cards are popular in the United States. Unlike debit cards, credit cards do not require funds to be present before purchase and are therefore not necessarily linked to a bank account. It is common for card holders to own more than one credit card. Reasons for this include benefits such as rewards programs, as well as the fact that some retailers only take certain credit card brands.

The downside of buying on credit

Since credit cards allow the user to defer payment, some users spend more than they can pay immediately. This leads to credit card debt. Since the interest rates on credit card plans are generally over 10 percent, this debt is difficult to pay off. With such a large number of credit cards in circulation in the United States, many analysts watch levels of credit card debt closely.

Dataset

This dataset was created by darth manav

Contents

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.

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.

Graph and download economic data for Large Bank Consumer Credit Card Balances: 90 or More Days Past Due Rates: Balances Based (RCCCBBALDPD90P) from Q3 2012 to Q4 2024 about 90 days +, FR Y-14M, consumer credit, credit cards, large, balance, loans, consumer, banks, depository institutions, rate, and USA.

Consumer Credit in the United States increased to 17.87 USD Billion in April from 8.60 USD Billion in March of 2025. This dataset provides the latest reported value for - United States Consumer Credit Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

CUST_ID: Credit card holder ID

BALANCE: Monthly average balance (based on daily balance averages)

BALANCE_FREQUENCY: Ratio of the last 12 months with balance

PURCHASES: Total purchase amount spent during last 12 months

ONEOFF_PURCHASES: Total amount of one-off purchases

INSTALLMENTS_PURCHASES: Total amount of installment purchases

CASH_ADVANCE: Total cash-advance amount

PURCHASES_ FREQUENCY: Frequency of purchases (Percent of months with at least one purchase)

ONEOFF_PURCHASES_FREQUENCY: Frequency of one-off-purchases PURCHASES_INSTALLMENTS_FREQUENCY: Frequency of installment purchases

CASH_ADVANCE_ FREQUENCY: Cash-Advance frequency

AVERAGE_PURCHASE_TRX: Average amount per purchase transaction

CASH_ADVANCE_TRX: Average amount per cash-advance transaction

PURCHASES_TRX: Average amount per purchase transaction

CREDIT_LIMIT: Credit limit

PAYMENTS: Total payments (due amount paid by the customer to decrease their statement balance) in the period

MINIMUM_PAYMENTS: Total minimum payments due in the period.

PRC_FULL_PAYMEN: Percentage of months with full payment of the due statement balance

TENURE: Number of months as a customer

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.

The Credit Card Agreements (CCA) database includes credit card agreements from more than 600 card issuers. These agreements include general terms and conditions, pricing, and fee information and are collected quarterly pursuant to requirements in the CARD Act.

Snapshot img

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 prevalence of online transactions. The digital shift in consumer behavior, fueled by the convenience and accessibility of e-commerce platforms, is leading to a surge in credit card payments. Another key trend shaping the market is the adoption of mobile biometrics for payment processing. This advanced technology offers enhanced security and ease of use, making it an attractive option for both consumers and merchants. However, the market also faces challenges. In developing economies, a lack of awareness and infrastructure for online payments presents a significant obstacle. Bridging the digital divide and educating consumers about the benefits and security of online transactions will be crucial for market expansion in these regions. Effective strategies, such as partnerships with local financial institutions and targeted marketing campaigns, can help overcome this challenge and unlock new opportunities for growth.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by advancements in technology and shifting consumer preferences. Payment optimization through EMV chip technology and payment authorization systems enhances security and streamlines transactions. Cross-border payments and chargeback prevention are crucial for businesses expanding globally. Ecommerce payment solutions, BNPL solutions, and mobile payments cater to the digital age, offering flexibility and convenience. Payment experience is paramount, with user interface design and alternative payment methods enhancing customer satisfaction. Merchant account services and payment gateway integration enable seamless transaction processing. Payment analytics and loyalty programs help businesses understand customer behavior and boost retention. Interchange fees, chargeback management, and dispute resolution are essential components of credit card processing. Data encryption and fraud detection ensure payment security. Multi-currency support and digital wallets cater to diverse customer needs. Customer support and subscription management are vital for maintaining positive relationships and managing recurring billing. Processing rates, settlement cycles, and PCI compliance are key considerations for businesses seeking efficient and cost-effective payment solutions. The ongoing integration of these elements shapes the dynamic and evolving credit card payments landscape.

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 AmericaUSCanadaEuropeGermanyUKAPACChinaIndiaJapanSouth KoreaSouth AmericaArgentinaBrazilRest of World (ROW).

By End-user Insights

The consumer or individual segment is estimated to witness significant growth during the forecast period.The market is a dynamic and evolving landscape that caters to businesses and consumers alike. Recurring billing enables merchants to automatically charge customers for goods or services on a regular basis, streamlining the payment process for both parties. EMV chip technology enhances payment security, reducing the risk of fraud. Payment optimization techniques help businesses minimize transaction costs and improve authorization rates. Cross-border payments facilitate international business, while chargeback prevention measures protect merchants from revenue loss due to disputed transactions. Ecommerce payment solutions provide convenience for consumers and merchants, with payment gateway integration ensuring seamless transactions. Rewards programs and buy now, pay later (BNPL) solutions incentivize consumer spending. Mobile payments and digital wallets offer flexibility and convenience. Merchants can accept various payment methods, including cryptocurrencies, and benefit from payment analytics and conversion rate optimization. Payment volume continues to grow, necessitating robust fraud detection systems and multi-currency support. Customer support is crucial for resolving disputes and addressing payment issues. Alternative payment methods cater to diverse consumer preferences. The payment experience is key

This statistic present the number of credit card holders in the United States from 2000 to 2010 and a forecast thereof for 2018, by type of credit card. There were 100 million Visa credit card holders in the United States in 2010 and this number was predicted to increase to 124 million in 2018.

Download the Meat Freshness Image Dataset with 2,266 images labeled into Fresh, Half-Fresh, and Spoiled categories. Perfect for building AI models in food safety and quality control to detect meat freshness based on visual cues.

Graph and download economic data for Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks (CCLACBW027SBOG) from 2000-06-28 to 2025-06-18 about revolving, credit cards, loans, consumer, banks, depository institutions, and USA.

1) Data introduction • Credit-card-details dataset aims to predict credit card approvals based on various variables.

2) Data utilization (1)Credit-card-details data has characteristics that: • Classifies credit card approval based on 18 variables such as gender, education level, marital status, etc. Approval = 0, Reject = 1. (2) Credit-card-details data can be used to: • Customer Insights: By analyzing data, banks can gain insights into the profiles of customers who are likely to be approved, which allows for targeted marketing and personalized offers. • Educational Purpose: This dataset serves as a resource for teaching and demonstrating concepts related to binary classification, data preprocessing, feature engineering, and machine learning applications in finance.

The G.19 Statistical Release, Consumer Credit, reports outstanding credit extended to individuals for household, family, and other personal expenditures, excluding loans secured by real estate. Total consumer credit comprises two major types: revolving and nonrevolving. Revolving credit plans may be unsecured or secured by collateral and allow a consumer to borrow up to a prearranged limit and repay the debt in one or more installments. Credit card loans comprise most of revolving consumer credit measured in the G.19, but other types, such as prearranged overdraft plans, are also included. Nonrevolving credit is closed-end credit extended to consumers that is repaid on a prearranged repayment schedule and may be secured or unsecured. To borrow additional funds, the consumer must enter into an additional contract with the lender. Consumer motor vehicle and education loans comprise the majority of nonrevolving credit, but other loan types, such as boat loans, recreational vehicle loans, and personal loans, are also included. This statistical release is designated by OMB as a Principal Federal Economic Indicator (PFEI).

Envestnet®| Yodlee®'s Credit Card 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