BEA has been researching the use of card transaction data as an early barometer of spending in the United States. Since the emergence of COVID-19, dramatic and fast-moving changes to the U.S. economy have increased the public and policymakers' need for more frequent and timely economic data. In response, BEA is presenting these estimates using daily payment card data to measure the effects of the pandemic on spending, updated approximately every two weeks. Note that these payment card transactions are not necessarily representative of total spending in an industry and the data have other limitations, described below. The estimates in these charts and tables are not a substitute for BEA's monthly and quarterly official data, which are grounded in well-tested and proven methodologies. An event study methodology is used to estimate the difference (in percentage points) in spending from the typical level (relative to the day of week, month, and annual trends) prior to the pandemic declared by the World Health Organization on March 11, 2020.

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.

Credit Card Fraud Detection Dataset (European Cardholders, September 2013)

As a data contributor, I'm sharing this crucial dataset focused on the detection of fraudulent credit card transactions. Recognizing these illicit activities is paramount for protecting customers and the integrity of financial systems.

About the Dataset:

This dataset encompasses credit card transactions made by European cardholders during a two-day period in September 2013. It presents a real-world scenario with a significant class imbalance, where fraudulent transactions are considerably less frequent than legitimate ones. Out of a total of 284,807 transactions, only 492 are instances of fraud, representing a mere 0.172% of the entire dataset.

Content of the Data:

Due to confidentiality concerns, the majority of the input features in this dataset have undergone a Principal Component Analysis (PCA) transformation. This means the original meaning and context of features V1, V2, ..., V28 are not directly provided. However, these principal components capture the variance in the underlying transaction data.

The only features that have not been transformed by PCA are:

• Time: Numerical. Represents the number of seconds elapsed between each transaction and the very first transaction recorded in the dataset.
• Amount: Numerical. The transaction amount in Euros (€). This feature could be valuable for cost-sensitive learning approaches.

The target variable for this classification task is:

• Class: Integer. Takes the value 1 in the case of a fraudulent transaction and 0 otherwise.

Important Note on Evaluation:

Given the substantial class imbalance (far more legitimate transactions than fraudulent ones), traditional accuracy metrics based on the confusion matrix can be misleading. It is strongly recommended to evaluate models using the Area Under the Precision-Recall Curve (AUPRC), as this metric is more sensitive to the performance on the minority class (fraudulent transactions).

How to Use This Dataset:

1. Download the dataset file (likely in CSV format).
2. Load the data using libraries like Pandas.
3. Understand the class imbalance: Be aware that fraudulent transactions are rare.
4. Explore the features: Analyze the distributions of 'Time', 'Amount', and the PCA-transformed features (V1-V28).
5. Address the class imbalance: Consider using techniques like oversampling the minority class, undersampling the majority class, or using specialized algorithms designed for imbalanced datasets.
6. Build and train binary classification models to predict the 'Class' variable.
7. Evaluate your models using AUPRC to get a meaningful assessment of performance in detecting fraud.

Acknowledgements and Citation:

This dataset has been collected and analyzed through a research collaboration between Worldline and the Machine Learning Group (MLG) of ULB (Université Libre de Bruxelles).

When using this dataset in your research or projects, please cite the following works as appropriate:

• Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015.
• Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon.
• Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE.
• Andrea Dal Pozzolo. Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi).
• Fabrizio Carcillo, Andrea Dal Pozzolo, Yann-Aël Le Borgne, Olivier Caelen, Yannis Mazzer, Gianluca Bontempi. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier.
• Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Gianluca Bontempi. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing.
• Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019.
• Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi *Combining Unsupervised and Supervised...

This dataset is designed to support research and model development in the area of fraud detection. It consists of real-world credit card transactions made by European cardholders over a two-day period in September 2013. Out of 284,807 transactions, 492 are labeled as fraudulent (positive class), making this a highly imbalanced classification problem.

Performance Note:

Due to the extreme class imbalance, standard accuracy metrics are not informative. We recommend using the Area Under the Precision-Recall Curve (AUPRC) or F1-score for model evaluation.

Features:

• Time Series Data: Each row represents a transaction, with the Time feature indicating the number of seconds elapsed since the first transaction.
• Dimensionality Reduction Applied: Features V1 through V28 are anonymized principal components derived from a PCA transformation due to confidentiality constraints.
• Raw Transaction Amount: The Amount field reflects the transaction value, useful for cost-sensitive modeling.
• Binary Classification Target: The Class label is 1 for fraud and 0 for legitimate transactions.

Usage:

• Machine learning model training for fraud detection.
• Evaluation of anomaly detection and imbalanced classification methods.
• Development of cost-sensitive learning approaches using the Amount variable.

Data Summary:

• Total Records: 284,807
• Fraud Cases: 492
• Imbalance Ratio: Fraudulent transactions account for just 0.172% of the dataset.
• Columns: 31 total (28 PCA features, plus Time, Amount, and Class)

License:

The dataset is provided under the CC0 (Public Domain) license, allowing users to freely use, modify, and distribute the data without any restrictions.

Acknowledgements

The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project

Please cite the following works:

Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon

Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE

Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)

Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier

Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing

Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019

Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019

Yann-Aël Le Borgne, Gianluca Bontempi Reproducible machine Learning for Credit Card Fraud Detection - Practical Handbook

Bertrand Lebichot, Gianmarco Paldino, Wissam Siblini, Liyun He, Frederic Oblé, Gianluca Bontempi Incremental learning strategies for credit cards fraud detection, IInternational Journal of Data Science and Analytics

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).

Mexico Card Transactions: Credit Cards: Day Care Centres data was reported at 0.250 MXN mn in 15 Apr 2025. This records an increase from the previous number of 0.213 MXN mn for 14 Apr 2025. Mexico Card Transactions: Credit Cards: Day Care Centres data is updated daily, averaging 0.239 MXN mn from Jan 2009 (Median) to 15 Apr 2025, with 5949 observations. The data reached an all-time high of 2.354 MXN mn in 05 Feb 2025 and a record low of 0.000 MXN mn in 12 Jul 2020. Mexico Card Transactions: Credit Cards: Day Care Centres data remains active status in CEIC and is reported by Bank of Mexico. The data is categorized under Global Database’s Mexico – Table MX.KA008: Card Transactions. Starting December 01, 2022, the form through which the card clearing houses (EGlobal and Prosa) report the operations that were cleared was modified to improve the quality of information, as well as add relevant information considering the new technologies. Additional institutions that were not previously reported due to lack of updated catalogues are now included.

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).

Dataset_1: The dataset consists of transaction timestamps (in hours) for a sample of online banking transactions. The timestamps represent the time of day when the transactions occurred.

Dataset_2: The dataset, encapsulated as a pandas DataFrame "trans_David", chronicles the transactional activities of an individual named David. A salient column, "channel_cd", signifies the payment channel employed by David for each transaction. The dataset encompasses 40 entries across 14 columns, with 'channel_cd' being the focal point for the derivation of the 'freq_channel' feature.

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).

Don't forget to hit the Upvote

This dataset represents a detailed compilation of trips made using yellow taxis in New York City. The data encapsulates a wide range of information, from pickup and drop_off times to fare amounts and payment types, offering a comprehensive view into urban mobility and the economics of taxi rides within the city. This dataset is invaluable for anyone interested in urban transportation trends, fare analysis, geographic movement patterns within New York City, and the study of temporal variations in taxi usage.

Column Description:

VendorID: A code indicating the provider associated with the trip record.

tpep_pickup_datetime: The date and time when the meter was engaged.

tpep_dropoff_datetime: The date and time when the meter was disengaged.

passenger_count: The number of passengers in the vehicle. This is a driver-entered value.

trip_distance: The distance of the trip measured in miles.

RatecodeID: The final rate code in effect at the end of the trip.

store_and_fwd_flag: Indicates whether the trip record was held in vehicle memory before sending to the vendor, Y=store and forward, N=not a store and forward trip.

PULocationID: The Taxi and Limousine Commission (TLC) Taxi Zone ID for the pickup location.

DOLocationID: The Taxi and Limousine Commission (TLC) Taxi Zone ID for the dropoff location.

payment_type: A numeric code signifying how the passenger paid for the trip.

fare_amount: The time-and-distance fare calculated by the meter.

extra: Miscellaneous extras and surcharges.

mta_tax: $0.50 MTA tax that is automatically triggered based on the metered rate in use.

tip_amount: Tip amount – This field is automatically populated for credit card tips. Cash tips are not included.

tolls_amount: Total amount of all tolls paid in trip.

improvement_surcharge: $0.30 improvement surcharge assessed trips at the flag drop. The surcharge began in 2015.

total_amount: The total amount charged to passengers. Does not include cash tips.

congestion_surcharge: A surcharge applied on trips that start, end, or pass through certain areas at specific times.

Usage Ideas

Suggest several research questions or project ideas that could be explored using the dataset. For example:

-Analyzing the impact of weather conditions on taxi usage.

-Exploring the correlation between trip distances and fares to identify pricing patterns.

-Investigating the effect of different times of day or days of the week on taxi demand.

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These data are derived from returns submitted to the Australian Prudential Regulation Authority (APRA) by banks authorised under the Banking Act 1959. APRA assumed responsibility for the supervision and regulation of banks on 1 July 1998. Data prior to that date were submitted to the RBA.

Prior to March 2002, banks reported quarterly to APRA on the Off-balance Sheet Business Return. From that date until the end of 2007, banks reported quarterly on ARF 112.2: Capital Adequacy – Off-balance Sheet Business. Following the introduction of a new capital framework (Basel II) on 1 January 2008, the data between March 2008 and March 2011 were reported on either ARF 112.2: Capital Adequacy – Off-balance Sheet Business, ARF 112.2A: Standardised Credit Risk – Off-balance Sheet Exposures, or ARF 118.0: Off-balance Sheet Business, depending on whether the bank had been approved by APRA to use a Basel II advanced approach to credit risk. Following the revocation of Australian Prudential Standard APS150 on 30 June 2011, banks using the advanced approach to credit risk have been required to report data with reference to the Basel II framework. From June 2011, data are reported on ARF 112.2A: Standardised Credit Risk – Off-balance Sheet Exposures, ARF 118.0: Off-balance Sheet Business, or ARF 118.1: Other Off-balance Sheet Exposures, depending on whether the bank has been approved by APRA to use a Basel II advanced approach to credit risk.

‘Consolidated group’, for a locally incorporated bank, refers to the global operations of the bank and its subsidiaries, excluding those involved in insurance, funds management/trustee and non-financial business. For a foreign bank authorised to operate in Australia as a branch, the data relate to the operations of the branch only. Figures are as at the last business day of the quarter and refer to the principal amount (face value) of the transaction.

From March 2002, banks are required to report separately activity in the banking and trading books for interest rate contracts, foreign exchange contracts, and other derivative contracts. Banking and trading book figures are added to produce the data reported in the table. Before March 2002, exposures were netted across the banking and trading books (except credit derivatives). This has necessitated a break in the series.

‘Direct credit substitutes’ covers any irrevocable obligations that carry the same credit risk as a direct extension of credit. This includes the issue of guarantees, confirmation of letters of credit, standby letters of credit serving as financial guarantees for loans, securities and any other financial liabilities, and certain bills endorsed under bill endorsement lines. ‘Direct credit substitutes’ does not include credit derivatives, which are shown separately.

‘Trade- and performance-related items’ covers contingent liabilities arising from trade-related obligations secured against an underlying shipment of goods and any irrevocable obligations to make a payment to a third party if a counterparty fails to perform a contractual non-monetary obligation. This includes documentary letters of credit issued, acceptances on trade bills, shipping guarantees issued, issue of performance bonds, bid bonds, warranties, indemnities, standby letters of credit in relation to a non-monetary obligation of a counterparty under a particular transaction, and any other trade- and performance-related items.

‘Commitments and other non-market-related items’ includes lending of securities or posting of securities as collateral, assets sold with recourse, forward asset purchases, partly paid shares and securities, placements of forward deposits, underwriting facilities, standby lines of credit, redraw facilities, undrawn credit card facilities, and all other non-market-related off-balance sheet items.

‘Interest rate contracts – OTC forwards’ covers single currency over-the-counter interest rate forwards including forward rate agreements.

‘Interest rate contracts – OTC swaps’ covers single currency over-the-counter interest rate swaps.

‘Interest rate contracts – Other’ covers other single currency over-the-counter and exchange-traded interest rate contracts including interest rate options written and purchased.

‘Foreign exchange contracts – OTC forwards’ covers over-the-counter foreign exchange forwards including foreign exchange forward contracts involving gold.

‘Foreign exchange contracts – OTC swaps’ covers over-the-counter foreign exchange swaps including cross currency interest rate swaps and foreign exchange swap contracts involving gold.

‘Foreign exchange contracts – Other’ covers other over-the-counter and exchange-traded foreign exchange contracts including other foreign exchange contracts involving gold.

‘Credit derivatives’ covers all credit derivatives contracts, both where protection is purchased and protection is sold. Banks were required to report credit derivatives exposure to APRA from June 2000 following a change to the Off-balance Sheet Business Return. This has necessitated a break in the series.

‘Other off-balance sheet business’ covers equity contracts including written and purchased options positions, derivatives based on gold and precious metals, base metals, energy and other commodities, and all other derivative activity.

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For many, the word 'mainframe' conjures images of antiquated technology employing large spools of magnetic tape for data storage and punch cards to execute commands in COBOL, the same programming language your uncle used. While this is no longer the mainframe's reality, given its reputation, it would surprise some to find out that many of the GC's critical business applications supporting security and financial transactions are all mainframe-based. The GC is not unique in this regard. The banking and insurance sectors still rely heavily on mainframes to help day-to-day banking, shopping, and credit card transactions. Despite their dependable service history, the mainframe suffers from a growing risk; a shrinking pool of skilled IT professionals supporting this technology and a small market of companies still supporting and innovating on mainframe platforms. For that reason, with the fullness of time, it is not a question of if the GC will stop using mainframes, but a question of when. That may come in a few years for some organizations, but it may be decades for others. Technological advancement has also made it possible to process mainframe workloads with the same or more significant computing power through modern and alternative technology. Further research is required regarding the security of those platforms and the subsequent cost effectiveness. The first version of the GC mainframe strategy provided an overview of the mainframe's status as technology across other industry sectors and then looked at their usage within the GC. It presented the risks associated with operating mainframes that the GC would need to manage continuously. The most pernicious of those was a workforce with mainframe skills. The second version of the strategy provides a migration approaches path for GC departments and makes recommendations to SSC and departments to incrementally approach migration, from discovery to architecture planning. The strategy will outline questions each department should be asking themselves as they consider their mainframe applications' future. The key to this is the question of fit for purpose. Given the strategic direction of your organization, do your mainframe applications continue to support that course? If not, what migration options exist? Finally, the strategy provides recommendations to improve the stewardship of mainframes and the applications they host.

Abstract

To educate consumers about responsible use of financial products, many governments, non-profit organizations and financial institutions have started to provide financial literacy courses. However, participation rates for non-compulsory financial education programs are typically extremely low.

Researchers from the World Bank conducted randomized experiments around a large-scale financial literacy course in Mexico City to understand the reasons for low take-up among a general population, and to measure the impact of this financial education course. The free, 4-hour financial literacy course was offered by a major financial institution and covered savings, retirement, and credit use. Motivated by different theoretical and logistics reasons why individuals may not attend training, researchers randomized the treatment group into different subgroups, which received incentives designed to provide evidence on some key barriers to take-up. These incentives included monetary payments for attendance equivalent to $36 or $72 USD, a one-month deferred payment of $36 USD, free cost transportation to the training location, and a video CD with positive testimonials about the training.

A follow-up survey conducted on clients of financial institutions six months after the course was used to measure the impacts of the training on financial knowledge, behaviors and outcomes, all relating to topics covered in the course.

The baseline dataset documented here is administrative data received from a screener that was used to get people to enroll in the financial course. The follow-up dataset contains data from the follow-up questionnaire.

Geographic coverage

Mexico City

Analysis unit

-Individuals

Universe

Participants in a financial education evaluation

Kind of data

Sample survey data [ssd]

Sampling procedure

Researchers used three different approaches to obtain a sample for the experiment.

The first one was to send 40,000 invitation letters from a collaborating financial institution asking about interest in participating. However, only 42 clients (0.1 percent) expressed interest.

The second approach was to advertise through Facebook, with an ad displayed 16 million times to individuals residing in Mexico City, receiving 119 responses.

The third approach was to conduct screener surveys on streets in Mexico City and outside branches of the partner institution. Together this yielded a total sample of 3,503 people. Researchers divided this sample into a control group of 1,752 individuals, and a treatment group of 1,751 individuals, using stratified randomization. A key variable used in stratification was whether or not individuals were financial institution clients. The analysis of treatment impacts is based on the sample of 2,178 individuals who were financial institution clients.

The treatment group received an invitation to participate in the financial education course and the control group did not receive this invitation. Those who were selected for treatment were given a reminder call the day before their training session, which was at a day and time of their choosing.

Mode of data collection

Face-to-face [f2f]

Research instrument

The follow-up survey was conducted between February and July 2012 to measure post-training financial knowledge, behavior and outcomes. The questionnaire was relatively short (about 15 minutes) to encourage participation.

Interviewers first attempted to conduct the follow-up survey over the phone. If the person did not respond to the survey during the first attempt, researchers offered one a 500 pesos (US$36) Walmart gift card for completing the survey during the second attempt. If the person was still unavailable for the phone interview, a surveyor visited his/her house to conduct a face-to-face interview. If the participant was not at home, the surveyor delivered a letter with information about the study and instructions for how to participate in the survey and to receive the Walmart gift card. Surveyors made two more attempts (three attempts in total) to conduct a face-to-face interview if a respondent was not at home.

Response rate

72.8 percent of the sample was interviewed in the follow-up survey. The attrition rate was slightly higher in the treatment group (29 percent) than in the control group (25.3 percent).

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