The monthly card payment statistics provide data in relation to credit and debit card transactions undertaken by Irish resident households. The data includes the monthly value and volume of transactions across both credit and debit cards by Irish households. The data is collected from issuers of credit and debit cards and specifically from reporting agents that are resident in Ireland (including established foreign branches). The aggregate data is further broken down into, remote and non-remote card spending; contactless and mobile wallet card spending; sectoral card spending; domestic and non-domestic card spending; regional card spending in Ireland; and cash withdrawals. A breakdown of the number of credit & debit cards currently issued to Irish residents is also provided. Scope: Only Personal Cards are in scope for reporting, business cards and cards issued to non-Irish residents are not in scope. The reporting instructions in the Landing page link has descriptive details about the table and the column names used in data collection

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.

This Dataset contains year, month, bank-type and bank-wise total value and volume of card payments and cash withdrawal transactions of credit and debit Cards at point of sale (PoS), ATMs and online during each month

Turkey Credit Card Transaction: Domestic: Value: Domestic Cards: Cash Adv data was reported at 6,035.550 TRY mn in Sep 2018. This records an increase from the previous number of 5,979.190 TRY mn for Aug 2018. Turkey Credit Card Transaction: Domestic: Value: Domestic Cards: Cash Adv data is updated monthly, averaging 1,693.490 TRY mn from Jan 2002 (Median) to Sep 2018, with 201 observations. The data reached an all-time high of 6,527.270 TRY mn in May 2018 and a record low of 180.050 TRY mn in Feb 2002. Turkey Credit Card Transaction: Domestic: Value: Domestic Cards: Cash Adv data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

Turkey Number of Transactions: Domestic: Credit Cards: Cash Advance data was reported at 103.853 Unit mn in 2017. This records an increase from the previous number of 97.225 Unit mn for 2016. Turkey Number of Transactions: Domestic: Credit Cards: Cash Advance data is updated yearly, averaging 89.184 Unit mn from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 103.853 Unit mn in 2017 and a record low of 39.000 Unit mn in 2002. Turkey Number of Transactions: Domestic: Credit Cards: Cash Advance data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA013: Credit and Debit Cards Statistics: Annual.

Statistical data on the number of credit cards in circulation, amount of credit card transactions, cash advances, and revolving credit amounts.

Rewards-based Credit Card Market Outlook

The global rewards-based credit card market size was valued at approximately $1.2 trillion in 2023 and is projected to reach a staggering $2.5 trillion by 2032, growing at a compound annual growth rate (CAGR) of 8.1% during the forecast period. One of the primary growth factors driving this expansion is the increasing consumer preference for rewards and incentives when making purchases, which boosts the attractiveness and adoption of rewards-based credit cards worldwide.

Several factors contribute to the robust growth of the rewards-based credit card market. Firstly, consumers are increasingly drawn to credit cards that offer tangible benefits such as cashback, travel rewards, and points that can be redeemed for various products and services. This trend is reinforced by rising disposable incomes and a growing middle class in emerging economies, which has led to a surge in credit card issuance and usage. Secondly, technological advancements and the proliferation of digital payment platforms have made it easier for consumers to manage and track their rewards, enhancing the overall user experience and driving market growth.

Additionally, the competitive landscape among credit card issuers is intensifying, with banks, credit unions, and fintech companies continually innovating their rewards programs to attract and retain customers. Customization of rewards to meet the diverse needs and preferences of different consumer segments has become a key strategy for market players. Furthermore, strategic partnerships between credit card issuers and merchants provide exclusive offers and discounts, further incentivizing the use of rewards-based credit cards.

Another significant growth driver is the increasing emphasis on cashless transactions, spurred by government initiatives and policies aimed at promoting digital payments. This shift towards a cashless economy has led to a rise in credit card adoption, particularly in regions such as Asia Pacific and Latin America. Moreover, the convenience and security features associated with credit card usage, including fraud protection and purchase insurance, make them a preferred payment method for many consumers, thereby boosting market growth.

On the regional front, North America holds a significant share of the rewards-based credit card market, driven by high consumer spending, a well-established credit card infrastructure, and a strong preference for reward programs. Europe follows closely, with a growing inclination towards digital payments and an expanding base of credit card users. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by increasing urbanization, rising disposable incomes, and supportive government policies promoting digital transactions.

Card Type Analysis

The rewards-based credit card market can be segmented by card type into cash back, travel rewards, points rewards, and others. Cash back credit cards remain immensely popular among consumers due to their simplicity and direct financial benefits. These cards offer a percentage of cash back on purchases, which can be reinvested or used to offset future expenses. The straightforward nature of cash back rewards makes them appealing to a broad range of consumers, driving their significant market share.

Travel rewards credit cards are another prominent category, offering benefits such as airline miles, hotel stays, and travel-related perks. With the resurgence of travel post-pandemic, these cards are gaining traction among consumers who seek to maximize their travel experiences through accumulated rewards. Partnerships between credit card issuers and travel service providers further enhance the value proposition of travel rewards cards, making them a preferred choice for frequent travelers.

Points rewards credit cards allow users to accumulate points on their purchases, which can be redeemed for a variety of goods and services, including merchandise, gift cards, and experiences. The flexibility and variety offered by points rewards programs appeal to consumers seeking tailored reward options. Credit card issuers often run promotional campaigns to boost point accumulation, further driving the popularity of this card type.

Other types of rewards-based credit cards include those offering specific benefits such as store loyalty rewards, gas rewards, and dining rewards. These niche cards cater to specific consumer needs and preferences, providing targeted benefits that enhance customer satisfaction

Cash-and-Equivalents Time Series for Synchrony Financial. Synchrony Financial, together with its subsidiaries, operates as a consumer financial services company in the United States. It provides credit products, such as credit cards, commercial credit products, and consumer installment loans. The company also offers private label credit cards, dual and general purpose co-branded cards, short- and long-term installment loans, and consumer banking products; and deposit products, including certificates of deposit, individual retirement accounts, money market accounts, savings accounts, and sweep and affinity deposits, as well as accepts deposits through third-party securities brokerage firms. In addition, it provides debt cancellation products to its credit card customers through online and mobile channels; and healthcare payments and financing solutions under the CareCredit and Walgreens brands; payments and financing solutions in the apparel, specialty retail, outdoor, music, and luxury industries, such as American Eagle, Dick's Sporting Goods, Guitar Center, Kawasaki, Pandora, Polaris, Suzuki, and Sweetwater. The company offers its credit products through programs established with a group of national and regional retailers, local merchants, manufacturers, buying groups, industry associations, and healthcare service providers; and deposit products through various channels, such as digital and print. It serves digital, health and wellness, retail, home, auto, telecommunications, pet, outdoor, and other industries. The company was founded in 1932 and is headquartered in Stamford, Connecticut.

Turkey Credit Card Transaction: Total: Vol: Domestic Cards: Cash Advance data was reported at 8,723,182.000 Unit in Mar 2018. This records an increase from the previous number of 7,640,921.000 Unit for Feb 2018. Turkey Credit Card Transaction: Total: Vol: Domestic Cards: Cash Advance data is updated monthly, averaging 7,044,988.000 Unit from Jan 2002 (Median) to Mar 2018, with 195 observations. The data reached an all-time high of 9,036,489.000 Unit in May 2011 and a record low of 2,546,266.000 Unit in Feb 2003. Turkey Credit Card Transaction: Total: Vol: Domestic Cards: Cash Advance data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

Analysis of ‘Credit Card Dataset for Clustering’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arjunbhasin2013/ccdata on 12 November 2021.

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

This case requires to develop a customer segmentation to define marketing strategy. The sample Dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables.

Following is the Data Dictionary for Credit Card dataset :-

CUST_ID : Identification of Credit Card holder (Categorical) BALANCE : Balance amount left in their account to make purchases ( BALANCE_FREQUENCY : How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated) PURCHASES : Amount of purchases made from account ONEOFF_PURCHASES : Maximum purchase amount done in one-go INSTALLMENTS_PURCHASES : Amount of purchase done in installment CASH_ADVANCE : Cash in advance given by the user PURCHASES_FREQUENCY : How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased) ONEOFFPURCHASESFREQUENCY : How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased) PURCHASESINSTALLMENTSFREQUENCY : How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done) CASHADVANCEFREQUENCY : How frequently the cash in advance being paid CASHADVANCETRX : Number of Transactions made with "Cash in Advanced" PURCHASES_TRX : Numbe of purchase transactions made CREDIT_LIMIT : Limit of Credit Card for user PAYMENTS : Amount of Payment done by user MINIMUM_PAYMENTS : Minimum amount of payments made by user PRCFULLPAYMENT : Percent of full payment paid by user TENURE : Tenure of credit card service for user

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

Turkey Credit Card Transaction: Intl: Vol: Domestic Cards: Cash Advance data was reported at 58,203.000 Unit in Mar 2018. This records an increase from the previous number of 56,290.000 Unit for Feb 2018. Turkey Credit Card Transaction: Intl: Vol: Domestic Cards: Cash Advance data is updated monthly, averaging 60,060.000 Unit from Jan 2002 (Median) to Mar 2018, with 195 observations. The data reached an all-time high of 164,165.000 Unit in Nov 2006 and a record low of 19,769.000 Unit in Mar 2002. Turkey Credit Card Transaction: Intl: Vol: Domestic Cards: Cash Advance data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

This dataset provides access to data about general purpose credit cards, which are open-end loans used by consumers to pay for day-to-day expenses, finance purchases, or provide cash advances. ***Microdata: YesLevel of Analysis: Local - FIPS CodeVariables Present: Yes - separate documentFile Layout: .csvCodebook: Yes Methods: General Overview https://www.consumerfinance.gov/data-research/consumer-credit-trends/Weights (with appropriate documentation): NoPublications: NoAggregate Data: No

A database containing details of all current rates/charges on current accounts, loans, savings accounts, credit cards and mortgages available in the market. Access to this information is provided via the CCPC website.

The credit card penetration in Africa was forecast to continuously increase between 2024 and 2029 by in total 0.2 percentage points. According to this forecast, in 2029, the credit card penetration will have increased for the eighth consecutive year to 2.92 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 Caribbean and North America.

Turkey Number of Transactions: Credit Card Issued by BKM Member Bank: Cash Adv data was reported at 98.120 Unit mn in 2017. This records an increase from the previous number of 90.973 Unit mn for 2016. Turkey Number of Transactions: Credit Card Issued by BKM Member Bank: Cash Adv data is updated yearly, averaging 84.784 Unit mn from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 98.120 Unit mn in 2017 and a record low of 39.000 Unit mn in 2002. Turkey Number of Transactions: Credit Card Issued by BKM Member Bank: Cash Adv data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA013: Credit and Debit Cards Statistics: Annual.

Cash-and-Equivalents Time Series for Commerzbank AG. Commerzbank AG provides banking and capital market products and services to private and small business customers, corporate, financial service providers, and institutional clients in Germany, rest of Europe, the Americas, Asia, and internationally. It operates through two segments, Private and Small-Business Customers, and Corporate Clients. The company offers saving, fixed deposit, checking, business, current, call money, and joint accounts; insurance products; credit and debit cards; pension; payment solutions; overdraft services; and various loans. It also offers instalment loans; real estate financing; corporate financing; financing advisory; merger and acquisition; foreign trade; trade financing; term deposits; and online banking services, as well as trades securities, such as shares, funds, ETFs, and bonds. In addition, the company provides wealth planning, asset management, estate and foundation management, asset risk analysis, and succession planning services. Commerzbank AG was founded in 1870 and is headquartered in Frankfurt am Main, Germany.

Turkey Credit Card Transaction: Domestic: Value: Total Cards: Cash Advance data was reported at 6,130.140 TRY mn in Jun 2018. This records a decrease from the previous number of 6,979.360 TRY mn for May 2018. Turkey Credit Card Transaction: Domestic: Value: Total Cards: Cash Advance data is updated monthly, averaging 1,791.885 TRY mn from Jan 2002 (Median) to Jun 2018, with 198 observations. The data reached an all-time high of 6,979.360 TRY mn in May 2018 and a record low of 197.710 TRY mn in Feb 2002. Turkey Credit Card Transaction: Domestic: Value: Total Cards: Cash Advance data remains active status in CEIC and is reported by The Interbank Card Center. The data is categorized under Global Database’s Turkey – Table TR.KA012: Credit and Debit Cards Statistics.

Title: Simulated Point of Sale (POS) Data with Seasonal Trends

Description: This dataset contains simulated Point of Sale (POS) transactions with seasonal trends. It is generated using Faker, a Python library for generating fake data, to mimic the behavior of a retail store over a period of time. The data includes various attributes such as timestamp, customer ID and name, item category and name, quantity, price per item, total price, and payment method.

Features:

Timestamp: The date and time of the transaction. Customer_ID: Unique identifier for each customer. Customer_Name: Name of the customer. Item_Category: Category of the purchased item, influenced by seasonal trends. Item_Name: Name of the purchased item. Quantity: Quantity of the purchased item. Price_Per_Item: Price per unit of the purchased item. Total_Price: Total price of the transaction (quantity * price per item). Payment_Method: Method of payment used for the transaction (Cash, Credit Card, Debit Card). Objective: This dataset can be used for various analytical purposes, including but not limited to:

Analyzing seasonal trends in customer purchasing behavior. Studying the popularity of different item categories across seasons. Predicting future sales based on historical data and seasonal patterns. Evaluating the effectiveness of marketing campaigns or promotions during different seasons. Usage: Data scientists, analysts, and machine learning enthusiasts can utilize this dataset to explore and practice data analysis, visualization, and predictive modeling techniques.

Acknowledgement: The dataset is generated for educational and practice purposes, using Faker library for fake data generation.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Which of the four factors directly impact your total cost of using the credit card?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Long-Term-Investments Time Series for Bank First National Corp. Bank First Corporation operates as a holding company for Bank First, N.A. that provides consumer and commercial financial services to businesses, professionals, and consumers, in Wisconsin. It offers checking, savings, money market, cash management, retirement, and health savings accounts; other time deposits; certificates of deposit; and residential mortgage products. The company's loan products include real estate loans, including commercial real estate, residential mortgage, and home equity loans; commercial and industrial loans for working capital, accounts receivable, inventory financing, and other business purposes; construction and development loans; residential 1-4 family loans; and consumer loans for personal and household purposes, including secured and unsecured installment loans, and revolving lines of credit. It also provides credit cards; insurance; data processing and other information technology; investment and safekeeping; treasury management; and online, telephone, and mobile banking services. The company was formerly known as Bank First National Corporation and changed its name to Bank First Corporation in June 2019. Bank First Corporation was founded in 1894 and is headquartered in Manitowoc, Wisconsin.