Data is of various customers of a bank with their credit limit, the total number of credit cards the customer has, and different channels through which customer has contacted the bank for any queries, different channels include visiting the bank, online and through a call centre.

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

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

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

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

Credit Card Customer Data Description This dataset contains information about credit card customers, including their account balances, transaction behaviors, and payment patterns. The data is structured in a tabular format with the following columns:

CUST_ID: Unique identifier for each customer. BALANCE: Current balance on the credit card account. BALANCE_FREQUENCY: Frequency of the balance updates (range: 0 to 1). PURCHASES: Total purchases made with the credit card. ONEOFF_PURCHASES: Total one-off purchases made with the credit card. INSTALLMENTS_PURCHASES: Total purchases made in installments. CASH_ADVANCE: Total cash advances taken from the credit card. PURCHASES_FREQUENCY: Frequency of purchases made (range: 0 to 1). ONEOFF_PURCHASES_FREQUENCY: Frequency of one-off purchases (range: 0 to 1). PURCHASES_INSTALLMENTS_FREQUENCY: Frequency of installment purchases (range: 0 to 1). CASH_ADVANCE_FREQUENCY: Frequency of cash advances taken (range: 0 to 1). CASH_ADVANCE_TRX: Total number of cash advance transactions. PURCHASES_TRX: Total number of purchase transactions. CREDIT_LIMIT: Credit limit assigned to the customer’s account. PAYMENTS: Total payments made towards the credit card balance. MINIMUM_PAYMENTS: Minimum payments required. PRC_FULL_PAYMENT: Percentage of full payments made (range: 0 to 1). TENURE: Duration (in months) the account has been active. Insights This dataset can be used to analyze customer spending behavior, assess credit risk, and identify trends in credit card usage. It provides a comprehensive overview of customer transactions, payment patterns, and credit management.

Use Cases Customer Segmentation: Group customers based on their spending habits and payment behaviors. Credit Risk Assessment: Evaluate risk levels associated with different customer profiles. Predictive Modeling: Develop models to predict future spending or likelihood of default.

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

CUST_ID: Credit card holder ID

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

BALANCE_FREQUENCY: Ratio of the last 12 months with balance

PURCHASES: Total purchase amount spent during last 12 months

ONEOFF_PURCHASES: Total amount of one-off purchases

INSTALLMENTS_PURCHASES: Total amount of installment purchases

CASH_ADVANCE: Total cash-advance amount

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

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

CASH_ADVANCE_ FREQUENCY: Cash-Advance frequency

AVERAGE_PURCHASE_TRX: Average amount per purchase transaction

CASH_ADVANCE_TRX: Average amount per cash-advance transaction

PURCHASES_TRX: Average amount per purchase transaction

CREDIT_LIMIT: Credit limit

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

MINIMUM_PAYMENTS: Total minimum payments due in the period.

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

TENURE: Number of months as a customer

Finances are an important part of life. When looking at the credit card users in selected countries worldwide, Israel and Turkey lead the ranking. ** percent of consumers from Israel as well as ** percent from Turkey are part of this category. Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than ********* interviews.

Graph and download economic data for Large Bank Consumer Credit Card Balances: Total Purchase Volume (RCCCBPURCHASETOT) from Q3 2012 to Q2 2025 about volume, FR Y-14M, credit cards, consumer credit, purchase, large, balance, loans, consumer, banks, depository institutions, and USA.

Graph and download economic data for Large Bank Consumer Credit Card Originations: Number of New Accounts (RCCCONUMACT) from Q3 2012 to Q2 2025 about accounts, FR Y-14M, origination, credit cards, consumer credit, large, new, loans, consumer, banks, depository institutions, and USA.

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

Graph and download economic data for Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks (CCLACBW027SBOG) from 2000-06-28 to 2025-11-19 about revolving, credit cards, loans, consumer, banks, depository institutions, and USA.

Graph and download economic data for Large Bank Consumer Credit Card Originations: Original Credit Score: 25th Percentile (RCCCOSCOREPCT25) from Q3 2012 to Q2 2025 about score, FR Y-14M, origination, credit cards, consumer credit, large, credits, percentile, loans, consumer, banks, depository institutions, and USA.

Business Problem A business manager of a consumer credit card bank is facing the problem of customer attrition. They want to analyze the data to find out the reason behind this and leverage the same to predict customers who are likely to drop off.

Graph and download economic data for Large Bank Consumer Credit Card Balances: 60 or More Days Past Due Rates: Balances Based (RCCCBBALDPD60P) from Q3 2012 to Q2 2025 about 60 days +, FR Y-14M, credit cards, consumer credit, large, balance, loans, consumer, banks, depository institutions, rate, and USA.

Consumer Credit in the United States increased to 13.09 USD Billion in September from 3.13 USD Billion in August of 2025. This dataset provides the latest reported value for - United States Consumer Credit Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Explore consumer and credit card loans data in Saudi Arabia, including information on maturity terms, categories such as tourism, vehicles, education, health care, and more. Access quarterly and annual data on total credit card loans, with a focus on medium, long, and short-term personal loan options.

Consumer Loans, Tourism, Maturity Terms, Medium Term, Education, Health Care, Vehicles, Bank, SAMA Quarterly

Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..Author Notes: The data from Q3 2017 to Q2 2019 have been updated.The dataset excludes real estate financing, financial leasing, and margin lending financing against shares."Total Credit Card Loans" Includes Visa, Master Card, American Express, and Others."Maturity Terms Of Personal Loans" represents loans granted by commercial banks to natural persons for financing personal, consumer and non-commercial purposes.For the data before 2014, the items of Furniture & Durable Goods, Education, Health care, Tourism and travel were included under 'Others'. "Short Term" : Less than one year"Medium Term" : 1 - 3 Years"Long Term" : Over 3 Years Loaans granted by commercial banks to natural persons for financing personal and consumer needs and for non-commercial purposes.

In 2023, there were *** million credit card holders in Poland, a decrease compared to the previous years.

1) Data Introduction • The Credit Card Transactions Dataset includes more than 20 million credit card transactions over the decades of 2,000 U.S. resident consumers created by IBM's simulations, providing details of each transaction and fraudulent labels.

2) Data Utilization (1) Credit Card Transactions Dataset has characteristics that: • This dataset provides a variety of properties that are similar to real credit card transactions, including transaction amount, time, card information, purchase location, and store category (MCC). (2) Credit Card Transactions Dataset can be used to: • Development of Credit Card Fraud Detection Model: Using transaction history and properties, you can build a fraud (abnormal transaction) detection model based on machine learning. • Analysis of consumption patterns and risks: Long-term and diverse transaction data can be used to analyze customer consumption behavior and identify risk factors.

Dataset

This dataset was created by Shashank Kumar Ranjan

Contents

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This dataset provides granular credit card transaction records, including customer demographics, card details, merchant information, and transaction metadata. It is ideal for banks and fintechs seeking to analyze spending patterns, segment customers, and model risk, enabling data-driven product design and market research.