Credit card debt in the United States has been growing at a fast pace between 2021 and 2025. In the fourth quarter of 2024, the overall amount of credit card debt reached its highest value throughout the timeline considered here. COVID-19 had a big impact on the indebtedness of Americans, as credit card debt decreased from *** billion U.S. dollars in the last quarter of 2019 to *** billion U.S. dollars in the first quarter of 2021. What portion of Americans use credit cards? A substantial portion of Americans had at least one credit card in 2025. That year, the penetration rate of credit cards in the United States was ** percent. This number increased by nearly seven percentage points since 2014. The primary factors behind the high utilization of credit cards in the United States are a prevalent culture of convenience, a wide range of reward schemes, and consumer preferences for postponed payments. Which companies dominate the credit card issuing market? In 2024, the leading credit card issuers in the U.S. by volume were JPMorgan Chase & Co. and American Express. Both firms recorded transactions worth over one trillion U.S. dollars that year. Citi and Capital One were the next banks in that ranking, with the transactions made with their credit cards amounting to over half a trillion U.S. dollars that year. Those industry giants, along with other prominent brand names in the industry such as Bank of America, Synchrony Financial, Wells Fargo, and others, dominate the credit card market. Due to their extensive customer base, appealing rewards, and competitive offerings, they have gained a significant market share, making them the preferred choice for consumers.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Malawi: Percent of people aged 15+ who have a credit card: The latest value from 2021 is 0.97 percent, a decline from 1.29 percent in 2017. In comparison, the world average is 22.26 percent, based on data from 121 countries. Historically, the average for Malawi from 2011 to 2021 is 1.29 percent. The minimum value, 0.97 percent, was reached in 2021 while the maximum of 1.53 percent was recorded in 2014.

Credit risk is the probability of a financial loss resulting from a borrower's failure to repay a loan. Essentially, credit risk refers to the risk that a lender may not receive the owed principal and interest, which results in an interruption of cash flows and increased costs for collection.

Dataset features

1. Debt (Задолженность): The total outstanding debt amount owed by the client.
2. Overdue Days (Просрочка, дни): The number of days a payment is overdue.
3. Initial Limit (Первоначальный лимит): The initial credit limit assigned to the client.
4. Birth Date (BIRTHDATE): The client's date of birth.
5. Sex (SEX): The client's gender (e.g., male, female).
6. Education (EDU): The client's level of education (e.g., high school, university).
7. Income (INCOME): The client's monthly or annual income.
8. Loan Term (TERM): The duration of the loan or credit agreement in months.
9. Credit History Rating (Рейтинг кредитной истории): A score or rating reflecting the client's credit history.
10. Living Area (LV_AREA): The geographical area or region where the client resides.
11. Settlement Name (LV_SETTLEMENTNAME): The name of the city, town, or settlement where the client lives.
12. Industry Name (INDUSTRYNAME): The industry or sector in which the client is employed.
13. Probability of Default (PDN): The estimated likelihood that the client will default on the loan.
14. Client ID (CLIENTID): A unique identifier assigned to the client.
15. Scoring Mark (SCORINGMARK): A credit score or risk assessment mark assigned to the client.
16. Underage Children Count (UNDERAGECHILDRENCOUNT): The number of underage children the client has.
17. Velcom Scoring (VELCOMSCORING): A specific scoring metric (possibly telecom-related) used in risk assessment.
18. Family Status (FAMILYSTATUS): The client's marital or family status (e.g., single, married, divorced).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2Fe3f381f2b16279a11f8a88975c643fb3%2FIndias-largest-bank-HDFC-Bank-has-climbed-back-to-the-top-ten-banks-in-the-world-in-terms-of-market-capitalization.jpg?generation=1746093705818402&alt=media" alt="">

Company Information:

The data set is based upon https://www.kaggle.com/prateikmahendra/loan-data"> Lending Club Information . - TheIrish Dummy Banks is a peer to peer lending bank based in the ireland, in which bank provide funds for potential borrowers and bank earn a profit depending on the risk they take (the borrowers credit score). Irish Fake bank provides loan to their loyal customers. The complete data set is borrowed from Lending Club For more basic information about the company please check out the wikipedia article about the company. This dataset is copied and clean from kaggle but it has been changed. The any kind of similarity is just for learning purposes. I dont have any intention for Plagiarism I just like to be clear myself.

<a src="https://en.wikipedia.org/wiki/Lending_Club"> Lending Club Information </a>

The central idea and coding is abstract from Kevin mark ham youtube video series, Introduction to machine learning with scikit-learn video series. You can find link under resources section.

Data Description

• LoanStatNew Description

• addr_state The state provided by the borrower in the loan application

• annual_inc The self-reported annual income provided by the borrower during registration.

• annual_inc_joint The combined self-reported annual income provided by the co-borrowers during registration

• application_type Indicates whether the loan is an individual application or a joint application with two co-borrowers

• collection_recovery_fee post charge off collection fee

• collections_12_mths_ex_med Number of collections in 12 months excluding medical collections

• delinq_2yrs The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years

• desc Loan description provided by the borrower

• dti A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, - - - excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income.

• dti_joint A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, - excluding mortgages and the requested LC loan, divided by the co-borrowers' combined self-reported monthly income

• earliest_cr_line The month the borrower's earliest reported credit line was opened

• emp_length Employment length in years. Possible values are between 0 and 10 where 0 means less than one year

• and 10 means ten or more years.

• emp_title The job title supplied by the Borrower when applying for the loan.*

• fico_range_high The upper boundary range the borrower’s FICO at loan origination belongs to.

• fico_range_low The lower boundary range the borrower’s FICO at loan origination belongs to.

• funded_amnt The total amount committed to that loan at that point in time.

• funded_amnt_inv The total amount committed by investors for that loan at that point in time.

• grade LC assigned loan grade

• home_ownership The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER.

Context

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

Content

The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.

It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.

Update (03/05/2021)

A simulator for transaction data has been released as part of the practical handbook on Machine Learning for Credit Card Fraud Detection - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html. We invite all practitioners interested in fraud detection datasets to also check out this data simulator, and the methodologies for credit card fraud detection presented in the book.

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

Credit Card Lead Prediction

Happy Customer Bank is a mid-sized private bank that deals in all kinds of banking products, like Savings accounts, Current accounts, investment products, credit products, among other offerings.

The bank also cross-sells products to its existing customers and to do so they use different kinds of communication like telecasting, e-mails, recommendations on net banking, mobile banking, etc.

In this case, the Happy Customer Bank wants to cross-sell its credit cards to its existing customers. The bank has identified a set of customers that are eligible for taking these credit cards.

Now, the bank is looking for your help in identifying customers that could show higher intent towards a recommended credit card, given:

This dataset was part of May 2021 Jobathon conducted my analytics vidhya, for more info check:https://datahack.analyticsvidhya.com/contest/job-a-thon-2/

SharkTank dataset of USA/American business reality television series. Currently, the data set has information from SharkTank season 1 to Shark Tank US season 16. The dataset has 53 fields/columns and 1440+ records.

Below are the features/fields in the dataset:

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Website of startup/company
• Pitchers Gender - Gender of pitchers
• Pitchers City - US city of pitchers
• Pitchers State - US state or country of pitchers, two letter shortcut
• Pitchers Average Age - Average age of all pitchers, <30 young, 30-50 middle, >50 old
• Entrepreneur Names - Pitcher names
• Multiple Entrepreneurs - Multiple entrepreneurs are present ? 1-yes, 0-no
• US Viewership - Viewership in US, TRP rating, in millions
• Original Ask Amount - Original Ask Amount, in USD
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in USD
• Got Deal - Got the deal or not, 1-yes, 0-no
• Total Deal Amount - Total Deal Amount, in USD, including debt/loan amount
• Total Deal Equity - Total Deal Equity, in percentages
• Deal Valuation - Deal Valuation, in USD
• Number of sharks in deal - Number of sharks in deal
• Investment Amount Per Shark - Investment Amount Per Shark
• Equity Per Shark - Equity received by each Shark
• Royalty Deal - Is it royalty deal or not (1-yes)
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Loan - Loan/debt (line of credit) amount given by sharks, in USD
• Deal has conditions - Deal has conditions or not? (yes or no)
• Barbara Corcoran Investment Amount - Amount Invested by Barbara Corcoran
• Barbara Corcoran Investment Equity - Equity received by Barbara Corcoran
• Mark Cuban Investment Amount - Amount Invested by Mark Cuban
• Mark Cuban Investment Equity - Equity received by Mark Cuban
• Lori Greiner Investment Amount - Amount Invested by Lori Greiner
• Lori Greiner Investment Equity - Equity received by Lori Greiner
• Robert Herjavec Investment Amount - Amount Invested by Robert Herjavec
• Robert Herjavec Investment Equity - Equity received by Robert Herjavec
• Daymond John Investment Amount - Amount Invested by Daymond John
• Daymond John Investment Equity - Equity received by Daymond John
• Kevin O Leary Investment Amount - Amount Invested by Kevin O'Leary
• Kevin O Leary Investment Equity - Equity received by Kevin O'Leary
• Guest Investment Amount - Amount Invested by Guests
• Guest Investment Equity - Equity received by Guests
• Guest Name - Name of Guest shark, if invested in deal
• Barbara Corcoran Present - Whether Barbara Corcoran present in episode or not
• Mark Cuban Present - Whether Mark Cuban present in episode or not
• Lori Greiner Present - Whether Lori Greiner present in episode or not
• Robert Herjavec Present - Whether Robert Herjavec present in episode or not
• Daymond John Present - Whether Daymond John present in episode or not
• Kevin O Leary Present - Whether Kevin O Leary present in episode or not
• Guest Present - Whether Guest present in episode or not

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F68da45b26ffa5db1a68a4e1589eb8fba%2Fgraph1.gif?generation=1740945615150985&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fec069b191ef6c0c36d24f129e9fecc80%2Ffoto3.png?generation=1740945620897811&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F7e45c8e94dd53b5d9332be8185c67d1c%2Fgraph2.gif?generation=1740945628087024&alt=media" alt="">

When people have problems with financial products and services, they can file a complaint with the Consumer Financial Protection Bureau (CFPB). This agency collects complaints and sends them to the company involved to help solve the issue.

Between 2017 and 2023, many customers filed complaints about Bank of America related to different financial products, such as bank accounts, credit cards, loans, and mortgages. Each complaint includes details like:

The date it was submitted to the CFPB. The date the CFPB sent it to the bank for review. The specific financial product involved (e.g., checking account, credit card, mortgage). The issue (e.g., unauthorized transactions, loan repayment problems, fees). The bank's response (e.g., refunding money, explaining the issue, or rejecting the complaint). Common Issues in Complaints Unauthorized Transactions – Customers reported money missing from their accounts or transactions they didn’t make. High or Unexpected Fees – Some people were charged fees they didn’t expect, like overdraft or maintenance fees. Loan and Mortgage Problems – Customers faced issues with loan payments, refinancing, or incorrect charges. Credit Card Disputes – Some users had trouble resolving incorrect charges on their credit cards. How Bank of America Responded The bank usually responded by:

Providing an explanation for the charge or issue. Issuing refunds when mistakes were found. Denying the complaint if they believed no error occurred. Not all complaints resulted in a solution for the customer, but reporting issues helps the CFPB track banking problems and ensure companies follow fair financial practices.

Commercial paper, in the global financial market, is an unsecured promissory note with a fixed maturity of not more than 270 days.

Commercial paper is a money-market security issued (sold) by large corporations to obtain funds to meet short-term debt obligations (for example, payroll), and is backed only by an issuing bank or company promise to pay the face amount on the maturity date specified on the note. Since it is not backed by collateral, only firms with excellent credit ratings from a recognized credit rating agency will be able to sell their commercial paper at a reasonable price. Commercial paper is usually sold at a discount from face value, and generally carries lower interest repayment rates than bonds due to the shorter maturities of commercial paper. Typically, the longer the maturity on a note, the higher the interest rate the issuing institution pays. Interest rates fluctuate with market conditions, but are typically lower than banks' rates.

Commercial paper – though a short-term obligation – is issued as part of a continuous rolling program, which is either a number of years long (as in Europe), or open-ended (as in the U.S.)

Acknowledgements

This dataset was made available by the Federal Reserve. You can find the original dataset, updated daily, here.

Inspiration

• Based solely on this dataset, when would you say the Great Recession financial crisis started? How does that compare with media reports?

Collection consists of data collected within the loan application form that the credit unions use to assess loan applications. The aim was to investigate if contemplation can improve the financial information that credit union loan applicants provide? Our rationale for focusing on this financial behavior in this group was four-fold. Firstly, people generally (see above; Santander, 2016) and this group specifically (as identified by staff at the credit union) have a tendency to underestimate their expenditure. Secondly, contemplation potentially encourages a degree of self-reflection, a process associated with greater self-control and self-regulation (Howell and Shepperd, 2013). This suggests that after contemplation, decision-making will be more thorough, detailed and personally beneficial (Yeung and Summerfield, 2012). In other words, contemplating expenditure may encourage people to give more accurate estimates of expenditure. Thirdly, staff used expenditure to help decide if the client could afford the loan they were applying for. Thus, a more accurate estimate of expenditure would benefit the staff in terms of the expediency of the loan application process. Finally, we propose that using such a sample was a more vigorous test of contemplation than occurred in our earlier studies. In that, the people who went to this credit union likely had more complex financial and social histories than the students and university staff who participated in Studies 1 and 2. For example, ~87% of credit loan applicants were in receipt of child benefit, 58% were not employed (vs. 5.1% National Average; Office for National Statistics, 2016), 63% had been in receipt of a social fund loan, and 67% have used high cost lenders (see Table 1). Unfortunately, an analysis of peoples financial histories and behaviors has identified a relationship between these demographic factors and poor financial management (i.e., ineffective planning for financial event, and having less self-efficacy and confidence in financial management; Money Advice Service, 2015), with financial illiteracy further related to poor financial outcomes (Hastings and Tejeda-Ashton, 2008). Thus, an intervention that improves financial estimations will be of benefit to credit union loan applicants and staff alike. With these points in mind we proposed the following applied hypothesis: Prompting credit union loan applicants to contemplate their expenditure would improve their estimates of expenditure. We expected that this improvement would occur in three areas: (i) Thoroughness: more expenditure information would be provided; (ii) Totals: larger estimates of expenditure, i.e., clients give an estimate of expenditure that more closely matches what they actually spend; (iii) Discrepancies between clients and staff : greater agreement between clients and staff for the above two measures.Despite personal debt being an ever increasing problem within our society the psychological understanding of debt and interventions to the problem remain elusive. The present project provides a novel solution by using insights from those with Obsessive-Compulsive Disorder, who are known to excessively monitor (eg, "Did I turn the oven off?"), and apply this to those who don’t monitor their finances. The research will examine which cognitive factors explain why debtors fail to adequately monitor their debt. Then examine debtors’ attentional biases with debt-related stimuli and how this relates to how they monitor their finances. This information will be used to modify how debtors interact with debt-related stimuli, and quantify its influence on financial behaviours. Finally, this will be applied to the design of a Manage Your Debt Application System (MYDAS) mobile phone intervention which aims to improve how debtors monitor their debt. This research will have the following implications: (1) Science: By providing an empirical understanding of the thought process of debtors and an intervention to change those thought processes key to debt. (2) Society: By providing new tools to identify problem debtors and interventions (MYDAS) the research will benefit debtors (reduce debt), creditors (repayment) and debt agencies. The data was collected within the loan application form that the credit union used to assess each applicants loan application request.