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.

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 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. Note, only Personal Cards are in scope for this reporting, business cards and cards issued to non-Irish residents are not included. Additionally, data files uploaded here follow the SDMX –ML format where Series Key are the primary identifier for a reporting period (Date for which the data is reported is represented in the Reporting Period field). For example : PCI.M.IE.W2.PCS_ALL.11.PN is the series key and each element/dimension between the delimiter “.” is expanded with a description in subsequent columns ending with the subscript “DESC” to understand the meaning of each element/dimension. The Observation_free column represents the value (€ EUR) or Volume (PN) of transactions depending on the last element/dimension, EUR or PN. For further information on the Payment Statistics Monthly, the reporting instructions in the Landing page link has additional details about the table and the column names used in this data collection.

The debit card penetration in the United Kingdom was forecast to continuously decrease between 2024 and 2029 by in total 0.2 percentage points. According to this forecast, in 2029, the debit card penetration will have decreased for the eighth consecutive year to 94.82 percent. The penetration rate refers to the share of the total population who use debit 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 Statistics provide data in relation to monthly credit card transactions. A breakdown of the number of credit cards issued to Irish residents is also provided.

The number of debit cards in use in the United Kingdom was forecast to continuously increase between 2024 and 2029 by in total 4.8 million cards (+4.47 percent). After the eleventh consecutive increasing year, the number is estimated to reach 112.2 million cards and therefore a new peak in 2029. Shown is the estimated number of debit 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).

Context

The card payments data is published by the Reserve Bank of India on a monthly basis. The statistics cover the methods of payment used in retail transactions and ATM transactions in India. It constitutes payments via debit cards, credit cards, ATMs etc, . It can can be used to check trend of card based payment in India.

Content

The data contains monthly statistics of the following information from Apr'2011 to Aug'2019 1. Number of ATM deployed on site by the bank. 1. Number of ATM deployed off site by the bank. 1. Number of POS deployed online by the bank 1. Number of POS deployed offline by the bank 1. Total number of credit cards issued outstanding (after adjusting the number of cards withdrawan/cancelled). 1. Total number of financial transactions done by the credit card issued by the bank at ATMs 1. Total number of financial transactions done by the credit card issued by the bank at POS terminals 1. Total value of financial transactions done by the credit card issued by the bank at ATMs 1. Total value of financial transactions done by the credit card issued by the bank at POS terminals. 1. Total number of debit cards issued outstanding (after adjusting the number of cards withdrawan/cancelled). 1. Total number of financial transactions done by the debit card issued by the bank at ATMs 1. Total number of financial transactions done by the debit card issued by the bank at POS terminals 1. Total value of financial transactions done by the debit card issued by the bank at ATMs 1. Total value of financial transactions done by the debit card issued by the bank at POS terminals.

Acknowledgements

The data is scraped from RBI monthly statistics https://www.rbi.org.in/scripts/ATMView.aspx More details on how this data is collected and cleaned is documented in this kernel https://www.kaggle.com/karvalo/indian-card-payment-data-gathering-and-analysis

Turkey Number of Debit Cards data was reported at 131,593,443.000 Unit in 2017. This records an increase from the previous number of 117,011,685.000 Unit for 2016. Turkey Number of Debit Cards data is updated yearly, averaging 62,606,715.500 Unit from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 131,593,443.000 Unit in 2017 and a record low of 29,560,303.000 Unit in 2000. Turkey Number of Debit Cards 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.

Envestnet®| Yodlee®'s Debit 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

The number of credit cards in use in the United Kingdom was forecast to continuously decrease between 2024 and 2029 by in total 0.02 million cards (-0.03 percent). The number is estimated to amount to 63.62 million cards in 2029. 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).

This dataset had adapted from 'Credit Card Churn Prediction: https://www.kaggle.com/datasets/anwarsan/credit-card-bank-churn ' for visualization in our university project. We have modified customer information, spending behavior, and also added revenue targets.

Scenario 🕶️ In 2019, the marketing team launched a campaign to attract millennial customers (born 1980-1996) with the goal of increasing revenue and enhancing the brand's appeal to a younger audience.
As the BI team, your task is to create a dashboard for users. 1. The Vice President of Sales wants to view the performance of the credit business. 2. The marketing team is interested in understanding customer segments and customer spending to measure Customer Lifetime Value (CLV) and Marketing Cost per Acquired Customer (MCAC).

⚠️Note: This is just a suggestion to guide the creation of the dashboard

Example in Tableau

Executive summary https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F508a2d2d89dabdfd368743f86c2a71e1%2Fexecutive%20overview.JPG?generation=1696110593484137&alt=media" alt=""> Customer behavior https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F1e4a1f62a25eab3c6707d002243894c7%2Fcustomer_behaviour.JPG?generation=1696110689732332&alt=media" alt="">

Card Payments by Irish Households. The Credit and Debit Card Statistics provide data in relation to credit and debit card transactions, including a sectoral breakdown of expenditure, E-commerce, spending outside Ireland, and data pertaining to the role of debit cards. A breakdown of the number of credit/debit cards currently issued to Irish residents is also provided.

The credit card penetration in Germany was forecast to continuously increase between 2024 and 2029 by in total *** percentage points. After the seventh consecutive increasing year, the credit card penetration is estimated to reach ***** 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 *** 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 Switzerland and Austria.

Credit Card Transaction: Volume: Club, Association, Social Services data was reported at 3,695,407.000 Unit in Mar 2018. This records an increase from the previous number of 3,363,160.000 Unit for Dec 2017. Credit Card Transaction: Volume: Club, Association, Social Services data is updated quarterly, averaging 2,975,104.000 Unit from Mar 2008 (Median) to Mar 2018, with 41 observations. The data reached an all-time high of 3,876,354.000 Unit in Jun 2016 and a record low of 689,743.000 Unit in Jun 2008. Credit Card Transaction: Volume: Club, Association, Social Services 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.

The credit card penetration in the United States was forecast to continuously increase between 2024 and 2029 by in total *** percentage points. After the seventh consecutive increasing year, the credit card penetration is estimated to reach ***** 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 *** 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 Canada and Mexico.

The debit card penetration in Kenya was forecast to continuously decrease between 2024 and 2029 by in total *** percentage points. After the twelfth consecutive decreasing year, the debit card penetration is estimated to reach ***** percent and therefore a new minimum in 2029. The penetration rate refers to the share of the total population who use debit 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 *** 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 debit card penetration in countries like Mozambique and Uganda.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_5c8e5801c2a64e2e6b16608296ef3e02/view

Turkey Credit Card Transaction: Domestic: Vol: Domestic Cards data was reported at 314,825,545.000 Unit in Jun 2018. This records a decrease from the previous number of 325,009,181.000 Unit for May 2018. Turkey Credit Card Transaction: Domestic: Vol: Domestic Cards data is updated monthly, averaging 164,316,749.500 Unit from Jan 2002 (Median) to Jun 2018, with 198 observations. The data reached an all-time high of 325,009,181.000 Unit in May 2018 and a record low of 42,731,360.000 Unit in Jan 2002. Turkey Credit Card Transaction: Domestic: Vol: Domestic Cards 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.

During my internship at Codsoft, I undertook an intriguing project focused on Credit Card Analysis. Leveraging a comprehensive dataset, I delved into the realm of data analysis to glean insights and uncover valuable trends.

The Credit Card Analysis Dataset provided a rich foundation for exploration, allowing me to dissect various aspects of credit card usage and consumer behavior. With meticulous attention to detail, I employed cutting-edge analytical techniques to extract meaningful patterns and observations.

My project involved a deep dive into the intricacies of credit card transactions, payment histories, spending patterns, and customer demographics. Through rigorous data analysis, I sought to unravel key factors influencing credit card usage, identify potential risk factors, and optimize strategies for customer engagement and retention.

In addition to conducting thorough analysis, I also crafted a robust codebase to streamline the process and ensure replicability. By implementing efficient data processing pipelines, exploratory data analysis techniques, and visualization tools, I facilitated a seamless journey from raw data to actionable insights.

Furthermore, I utilized advanced statistical methods and machine learning algorithms to develop predictive models aimed at forecasting credit card usage trends, detecting fraudulent activities, and enhancing risk management strategies.

This project not only honed my technical skills in data analysis and programming but also provided invaluable insights into the dynamic landscape of financial services and consumer behavior. Through collaboration with mentors and peers at Codsoft, I was able to refine my analytical prowess and contribute meaningfully to the organization's objectives.

Moving forward, I am eager to continue exploring innovative applications of data analytics in finance and beyond, leveraging my internship experience at Codsoft as a springboard for future endeavors.

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.