Introduction

Credit Card Statistics: A credit card is a widely used financial tool that allows consumers to make purchases or withdraw cash on credit, accruing debt to be repaid later. As of Q4 2024, Americans held approximately USD 1.21 trillion in credit card debt, marking a 4% increase from the previous year. The average credit card balance per consumer reached USD 6,730, up by 3.5% from 2023.

In the same period, the number of credit card accounts in the U.S. rose to about 617 million. Globally, Visa and Mastercard have approximately 1.3 billion and 1.1 billion credit cards in circulation, respectively. Credit cards accounted for 32% of all payment transactions in 2023, reflecting their significant role in consumer spending. However, 22% of credit card users make only minimum payments, indicating potential financial strain. Additionally, credit card delinquency rates rose to 3.6% in Q4 2024, highlighting challenges in debt repayment. These statistics underscore the importance of responsible credit card usage and financial management.

Credit cards also allow customers to build a debt balance that is related to the interest being charged. Let’s shed more light on “Credit Card Statistics†through this article.

The credit card penetration in Thailand was forecast to continuously increase between 2024 and 2029 by in total 36.8 percentage points. After the fifteenth consecutive increasing year, the credit card penetration is estimated to reach 67.53 percent and therefore a new peak in 2029. Notably, the credit card penetration of was continuously increasing over the past years.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 Malaysia and Philippines.

The credit card penetration in Brazil was forecast to continuously increase between 2024 and 2029 by in total 16.6 percentage points. After the twelfth consecutive increasing year, the credit card penetration is estimated to reach 62.27 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 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 Transactions Dataset provides detailed records of credit card transactions, including information about transaction times, amounts, and associated personal and merchant details. This dataset has over 1.85M rows.

How This Dataset Can Be Used:

Fraud Detection : Use machine learning models to identify fraudulent transactions by examining patterns in transaction amounts, locations, and user profiles. Enhancing fraud detection systems becomes feasible by analyzing behavioral patterns.

Customer Segmentation : Segment customers based on spending patterns, location, and demographics. Tailor marketing strategies and personalized offers to these different customer segments for better engagement.

Transaction Classification : Classify transactions into categories such as grocery or entertainment to understand spending behaviors. This helps in improving recommendation systems by identifying transaction categories and preferences.

Geospatial Analysis : Analyze transaction data geographically to map spending patterns and detect regional trends or anomalies based on latitude and longitude.

Predictive Modeling : Build models to forecast future spending behavior using historical transaction data. Predict potential fraudulent activities and financial trends.

Behavioral Analysis : Examine how factors like transaction amount, merchant type, and time influence spending behavior. Study the relationships between user demographics and transaction patterns.

Anomaly Detection : Identify unusual transaction patterns that deviate from normal behavior to detect potential fraud early. Employ anomaly detection techniques to spot outliers and suspicious activities.

Card fraud losses across the world increased by more than 10 percent between 2020 and 2021, the largest increase since 2018. It was estimated that merchants and card acquirers lost well over 30 billion U.S. dollars, with - so the source adds - roughly 12 billion U.S. dollar coming from the United States alone. Note that the figures provided here included both credit card fraud and debit card fraud. The source does not separate between the two, and also did not provide figures on the United States - a country known for its reliance on credit cards.

Credit card statistics by age group (Financial Information Service Co., Ltd.)

The credit card penetration in Canada was forecast to continuously increase between 2024 and 2029 by in total 1.4 percentage points. After the seventh consecutive increasing year, the credit card penetration is estimated to reach 84.55 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 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 United States and Mexico.

Business Context: Analytics driving every industry based on a variety of technology platforms which collect information from various sources by analysing what customers certainly want. The Credit Card industry is also data rich industry and data can be leveraged in infinite ways to understand customer behaviour. The data from a credit card processor shows the consumer types and their business spending behaviours. Therefore, companies can develop the marketing campaigns that directly address consumers’ behaviour. In return, this helps to make better sales and the revenue undoubtedly grows greater sales. Understanding the consumption pattern for credit cards at an individual consumer level is important for customer relationship management. This understanding allows banks to customize for consumers and make strategic marketing plans. Thus it is imperative to study the relationship between the characteristics of the consumers and their consumption patterns. Business Objectives: One of the leading banks provided below data a. Customer Demographics b. Customer Behavioural data (information on liabilities, assets and history of transactions with the bank for each customer). Data has been provided for a particular set of customers' credit card spend in the previous 3 months (April, May & June) and their expected average spend in the coming 3 months (July, August & September) c. Credit consumption Data Dictionary a. CustomerDemographics.csv ID – Customer ID - Unique ID for every Customer Account_type - Account Type (current or saving) Gender- Gender of customer (M or F) Age - Age of customer Income – Income Levels (High/Medium/Low) Emp_Tenure_Years – Experience – Employment Tenure of customer in Years Tenure_with_Bank – Number of years with bank Region_code Code assigned to region of residence (has order) NetBanking_Flag – Whether customer is using net banking for the transactions Avg_days_between_transaction – Average days between two transactions b. CustomerBehaviorData.csv ID – Customer ID - Unique ID for every Customer CC_cons_apr - Credit card spend in April DC_cons_apr - Debit card spend in April CC_cons_may - Credit card spend in May DC_cons_may - Debit card spend in May CC_cons_jun - Credit card spend in June DC_cons_jun - Debit card spend in June CC_count_apr - Number of credit card transactions in April CC_count_may - Number of credit card transactions in May CC_count_jun - Number of credit card transactions in June DC_count_apr - Number of debit card transactions in April DC_count_may - Number of debit card transactions in May DC_count_jun - Number of debit card transactions in June Card_lim - Maximum Credit Card Limit allocated Personal_loan_active - Active personal loan with other bank Vehicle_loan_active - Active Vehicle loan with other bank Personal_loan_closed - Closed personal loan in last 12 months Vehicle_loan_closed - Closed vehicle loan in last 12 months Investment_1 - DEMAT investment in june Investment_2 - Fixed deposit investment in june Investment_3 - Life Insurance investment in June Investment_4 - General Insurance Investment in June Debit_amount_apr - Total amount debited for April Credit_amount_apr - Total amount credited for April Debit_count_apr- Total number of times amount debited in april Credit_count_apr - Total number of times amount credited in april Max_credit_amount_apr - Maximum amount credited in April Debit_amount_may - Total amount debited for May Credit_amount_may - Total amount credited for May Credit_count_may - Total number of times amount credited in May Debit_count_may - Total number of times amount debited in May Max_credit_amount_may - Maximum amount credited in May Debit_amount_jun - Total amount debited for June Credit_amount_jun - Total amount credited for June Credit_count_jun - Total number of times amount credited in June Debit_count_jun - Total number of times amount debited in June Max_credit_amount_jun - Maximum amount credited in June Loan_enq - Loan enquiry in last 3 months (Y or N) Emi_active - Monthly EMI paid to other bank for active loans c. CreditConsumptionData.csv ID – Customer ID - Unique ID for every Customer cc_cons (Target) - Average Credit Card Spend in next three months Note: Some customers are having missing values for credit consumption. You need to build the model using customer’s data where credit consumption is non- missing’s. You need to predict the credit consumption for next three months for the customers having missing values. Model Evaluation Metric: You should validate model using Root Mean Square Percentage Error (RMSPE) between the predicted credit card consumption and Actual Credit Consumption. Expected Outputs: a. Detailed code with comments b. Data Exploratory analysis c. Model validation outputs d. Model documentation with all the details e. Predicted values for customers where target variable having missing values

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.

Average unpaid installment related amounts per household for credit cards in each age group statistics table (Credit Information Center)

Content

In recent years, the credit card issuers in Taiwan faced the cash and credit card debt crisis and the delinquency is expected to peak in the third quarter of 2006 (Chou,2006). In order to increase market share, card-issuing banks in Taiwan over-issued cash and credit cards to unqualified applicants. At the same time, most cardholders, irrespective of their repayment ability, overused credit card for consumption and accumulated heavy credit and cash–card debts. The crisis caused the blow to consumer finance confidence and it is a big challenge for both banks and cardholders

Acknowledgements

Credit for this dataset belongs to UCI ML Repository

Column Information

PAY_0: Repayment status in September, 2005 (-1=pay duly, 1=payment delay for one month, 2=payment delay for two months,8=payment delay for eight - months, 9=payment delay for nine months and above) PAY_2: Repayment status in August, 2005 (scale same as above) PAY_3: Repayment status in July, 2005 (scale same as above) PAY_4: Repayment status in June, 2005 (scale same as above) PAY_5: Repayment status in May, 2005 (scale same as above) PAY_6: Repayment status in April, 2005 (scale same as above) BILL_AMT1: Amount of bill statement in September, 2005 (NT dollar) BILL_AMT2: Amount of bill statement in August, 2005 (NT dollar) BILL_AMT3: Amount of bill statement in July, 2005 (NT dollar) BILL_AMT4: Amount of bill statement in June, 2005 (NT dollar) BILL_AMT5: Amount of bill statement in May, 2005 (NT dollar) BILL_AMT6: Amount of bill statement in April, 2005 (NT dollar) PAY_AMT1: Amount of previous payment in September, 2005 (NT dollar) PAY_AMT2: Amount of previous payment in August, 2005 (NT dollar) PAY_AMT3: Amount of previous payment in July, 2005 (NT dollar) PAY_AMT4: Amount of previous payment in June, 2005 (NT dollar) PAY_AMT5: Amount of previous payment in May, 2005 (NT dollar) PAY_AMT6: Amount of previous payment in April, 2005 (NT dollar)

Average revolving credit balance per account by gender (Joint Credit Information Center)

Credit Card Financial Dashboard

This project presents a comprehensive Credit Card Financial Report created using Power BI. It aims to provide a detailed analysis of credit card operations on a weekly basis, offering real-time insights into key performance metrics and trends. The dashboard empowers stakeholders to monitor and analyze credit card operations effectively, facilitating informed decision-making processes.

Project Overview:

The "Credit Card Financial Dashboard" leverages a Kaggle dataset containing anonymized credit card transaction data. The dataset includes information such as transaction volume, transaction types, transaction amounts, customer demographics, spending behavior, and credit limits.

Project Contents:

1. Credit Card Transaction Report: This page provides a detailed analysis of credit card transactions, including transaction volume, types of transactions (e.g., purchases, cash advances), transaction amounts, and any anomalies or trends observed. Visualizations such as bar charts, line graphs, and pie charts are utilized to present the data effectively. 2. Credit Card Customer Report: The second page focuses on analyzing customer-related metrics, such as customer demographics (age, gender), spending behavior (average transaction amount, frequency of transactions), credit limits, and any customer-specific insights that can aid in decision-making processes. Visualizations such as demographic distributions, spending patterns, and credit limit distributions are included to provide a comprehensive overview of customer behavior.

Credit Card Statistics by Gender of Cardholders (Financial Joint Credit Information Center)

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.

As required by the Credit CARD Act of 2009, we collect information annually from credit card issuers who have marketing agreements with universities, colleges, or affiliated organizations such as alumni associations, sororities, fraternities, and foundations.

Average number of credit cards per household by age group (Financial Joint Credit Information Center)

Credit card contract store monthly sign-up statistics table (Financial Union Credit Information Center)

Credit card cash advance limit statistics table (Joint Financial Credit Reference Center)

Predicting Credit Card Customer Segmentation

Exploring Key Customer Characteristics

By [source]

About this dataset

This dataset contains a wealth of customer information collected from within a consumer credit card portfolio, with the aim of helping analysts predict customer attrition. It includes comprehensive demographic details such as age, gender, marital status and income category, as well as insight into each customer’s relationship with the credit card provider such as the card type, number of months on book and inactive periods. Additionally it holds key data about customers’ spending behavior drawing closer to their churn decision such as total revolving balance, credit limit, average open to buy rate and analyzable metrics like total amount of change from quarter 4 to quarter 1, average utilization ratio and Naive Bayes classifier attrition flag (Card category is combined with contacts count in 12months period alongside dependent count plus education level & months inactive). Faced with this set of useful predicted data points across multiple variables capture up-to-date information that can determine long term account stability or an impending departure therefore offering us an equipped understanding when seeking to manage a portfolio or serve individual customers

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How to use the dataset

This dataset can be used to analyze the key factors that influence customer attrition. Analysts can use this dataset to understand customer demographics, spending patterns, and relationship with the credit card provider to better predict customer attrition.

Research Ideas

• Using the customer demographics, such as gender, marital status, education level and income category to determine which customer demographic is more likely to churn.
• Analyzing the customer’s spending behavior leading up to churning and using this data to better predict the likelihood of a customer of churning in the future.
• Creating a classifier that can predict potential customers who are more susceptible to attrition based on their credit score, credit limit, utilization ratio and other spending behavior metrics over time; this could be used as an early warning system for predicting potential attrition before it happens

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: BankChurners.csv | Column name | Description | |:---------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------| | CLIENTNUM | Unique identifier for each customer. (Integer) | | Attrition_Flag | Flag indicating whether or not the customer has churned out. (Boolean) | | Customer_Age | Age of customer. (Integer) | | Gender | Gender of customer. (String) | | Dependent_count | Number of dependents that customer has. (Integer) | | Education_Level ...