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.

Debt Balance Credit Cards in the United States increased to 1.23 Trillion USD in the third quarter of 2025 from 1.21 Trillion USD in the second quarter of 2025. This dataset includes a chart with historical data for the United States Debt Balance Credit Cards.

The G.19 Statistical Release, Consumer Credit, reports outstanding credit extended to individuals for household, family, and other personal expenditures, excluding loans secured by real estate. Total consumer credit comprises two major types: revolving and nonrevolving. Revolving credit plans may be unsecured or secured by collateral and allow a consumer to borrow up to a prearranged limit and repay the debt in one or more installments. Credit card loans comprise most of revolving consumer credit measured in the G.19, but other types, such as prearranged overdraft plans, are also included. Nonrevolving credit is closed-end credit extended to consumers that is repaid on a prearranged repayment schedule and may be secured or unsecured. To borrow additional funds, the consumer must enter into an additional contract with the lender. Consumer motor vehicle and education loans comprise the majority of nonrevolving credit, but other loan types, such as boat loans, recreational vehicle loans, and personal loans, are also included. This statistical release is designated by OMB as a Principal Federal Economic Indicator (PFEI).

Households Debt in the United States decreased to 68.30 percent of GDP in the first quarter of 2025 from 69.40 percent of GDP in the fourth quarter of 2024. This dataset provides - United States Households Debt To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset represents credit card usage and financial behaviour among 1,000 Indian consumers residing in Ranchi. It was collected to support research and academic projects analyzing:

The impact of credit card rates (interest, annual fees, late payment fees) on consumer financial behavior across demographics.

The psychological and behavioral effects of credit card usage, such as impulsive spending, debt accumulation, and financial stress.

Consumer awareness of hidden charges and regulations affecting credit card usage.

The dataset combines demographic, financial, behavioral, and psychological variables to provide a comprehensive overview of credit card usage patterns in India.

Columns / Data Dictionary Column Name Description Customer_ID Unique identifier for each customer Age Age of the customer (18–70) Gender Male, Female, Other Income_Level Income group: Low, Medium, High Education Highest education level Location Urban, Semi-Urban, Rural Credit_Limit Credit limit assigned (₹20,000 – ₹5,00,000) Interest_Rate Annual interest rate (%) Annual_Fee Annual fee charged (₹0 – ₹5,000) Late_Payment_Fee Penalty fee for late payments Hidden_Charges_Awareness Whether the customer is aware of hidden charges (Yes/No) Regulation_Awareness Awareness of regulatory changes (High/Medium/Low) Monthly_Spending Average monthly spending Impulse_Purchases Whether impulse purchases are made (Yes/No) Debt_Accumulation Level of debt accumulation (Low/Moderate/High) Repayment_Behavior Repayment type (On-time/Partial/Default) Credit_Score_Category Credit score category (Poor/Fair/Good/Excellent) Stress_Level Stress level due to credit card usage (Low/Medium/High) Satisfaction_With_Credit_Card Customer satisfaction rating (1–5) Dependency_On_Credit Dependency level on credit (Low/Medium/High) Inspiration / Use Cases

Research on credit card debt and consumer behavior in India

Machine learning projects: classification or prediction of repayment behavior

Financial analytics and risk assessment modeling

Understanding psychological factors influencing spending and stress among Indian consumers

Description

This data set represents thousands of loans made through the Lending Club platform, which is a platform that allows individuals to lend to other individuals. Of course, not all loans are created equal. Someone who is a essentially a sure bet to pay back a loan will have an easier time getting a loan with a low interest rate than someone who appears to be riskier. And for people who are very risky? They may not even get a loan offer, or they may not have accepted the loan offer due to a high interest rate. It is important to keep that last part in mind, since this data set only represents loans actually made, i.e. do not mistake this data for loan applications!

Format

A data frame with 10,000 observations on the following 55 variables.

emp_title

Job title.

emp_length

Number of years in the job, rounded down. If longer than 10 years, then this is represented by the value 10.

state

Two-letter state code.

homeownership

The ownership status of the applicant's residence.

annual_income

Annual income.

verified_income

Type of verification of the applicant's income.

debt_to_income

Debt-to-income ratio.

annual_income_joint

If this is a joint application, then the annual income of the two parties applying.

verification_income_joint

Type of verification of the joint income.

debt_to_income_joint

Debt-to-income ratio for the two parties.

delinq_2y

Delinquencies on lines of credit in the last 2 years.

months_since_last_delinq

Months since the last delinquency.

earliest_credit_line

Year of the applicant's earliest line of credit

inquiries_last_12m

Inquiries into the applicant's credit during the last 12 months.

total_credit_lines

Total number of credit lines in this applicant's credit history.

open_credit_lines

Number of currently open lines of credit.

total_credit_limit

Total available credit, e.g. if only credit cards, then the total of all the credit limits. This excludes a mortgage.

total_credit_utilized

Total credit balance, excluding a mortgage.

num_collections_last_12m

Number of collections in the last 12 months. This excludes medical collections.

num_historical_failed_to_pay

The number of derogatory public records, which roughly means the number of times the applicant failed to pay.

months_since_90d_late

Months since the last time the applicant was 90 days late on a payment.

current_accounts_delinq

Number of accounts where the applicant is currently delinquent.

total_collection_amount_ever

The total amount that the applicant has had against them in collections.

current_installment_accounts

Number of installment accounts, which are (roughly) accounts with a fixed payment amount and period. A typical example might be a 36-month car loan.

accounts_opened_24m

Number of new lines of credit opened in the last 24 months.

months_since_last_credit_inquiry

Number of months since the last credit inquiry on this applicant.

num_satisfactory_accounts

Number of satisfactory accounts.

num_accounts_120d_past_due

Number of current accounts that are 120 days past due.

num_accounts_30d_past_due

Number of current accounts that are 30 days past due.

num_active_debit_accounts

Number of currently active bank cards.

total_debit_limit

Total of all bank card limits.

num_total_cc_accounts

Total number of credit card accounts in the applicant's history.

num_open_cc_accounts

Total number of currently open credit card accounts.

num_cc_carrying_balance

Number of credit cards that are carrying a balance.

num_mort_accounts

Number of mortgage accounts.

account_never_delinq_percent

Percent of all lines of credit where the applicant was never delinquent.

tax_liens

a numeric vector

public_record_bankrupt

Number of bankruptcies listed in the public record for this applicant.

loan_purpose

The category for the purpose of the loan.

application_type

The type of application: either individual or joint.

loan_amount

The amount of the loan the applicant received.

term

The number of months of the loan the applicant received.

interest_rate

Interest rate of the loan the applicant received.

installment

Monthly payment for the loan the applicant received.

grade

Grade associated with the loan.

sub_grade

Detailed grade associated with the loan.

issue_month

Month the loan was issued.

loan_status

Status of the loan.

initial_listing_status

Initial listing status of the loan. (I think this has to do with whether the lender provided the entire loan or if the loan is across multiple lenders.)

disbursement_method

Dispersement method of the loan.

balance

Current...

Data Dictionary The explanation of the features is as follows:

Customer Number: A sequential number assigned to the customers (this column is hiddenand excluded – this unique identifier will not be used directly). ActiveCreditCard: Did the customer active (Yes) or not active (No) the credit card. Reward: The type of reward program offered for the card. Mailer Type: Letter or postcard. Income Level: Low, Medium, or High. Age: customers’ age Tenure: The number of years that the customer has been a client of the bank. Job: Job type of customer (admin, blue-collar, entrepreneur, housemaid, management, retired, self-employed, services, student, technician, unemployed, unknown) Bank Accounts Open: How many non-credit-card accounts are held by the customer Overdraft Protection: Does the customer have overdraft protection on their checking account(s) (Yes or No). Credit Rating: Low, Medium, or High. Credit Cards Held: The number of credit cards held at the bank. Homes Owned: The number of homes owned by the customer. Household Size: Number of individuals in the family. Own Your Home: Does the customer own their home? (Yes or No). Average Balance: Average account balance (across all accounts over time). Q1, Q2, Q3, and Q4 Balance: Average balance for each quarter in the last year.

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

Consumer Credit in the United Kingdom decreased to 1119 GBP Million in October from 1398 GBP Million in September of 2025. This dataset provides the latest reported value for - United Kingdom Consumer Credit - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

Households Debt in Canada decreased to 99.58 percent of GDP in the first quarter of 2025 from 100.39 percent of GDP in the fourth quarter of 2024. This dataset provides - Canada Households Debt To Gdp- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Statistics Netherlands collects data on consumer credit granted to individuals and the resulting debt. The following monthly amounts are given for the various types of credit institutions and types of credit: - amount of credit granted; - amount of interest; - amount of repayments; - amount of outstanding balance; - amount of limits granted. Also stated is the number of: - outstanding contracts; - limits granted; - new loans supplied; - new limits granted.

Data available from January 1998 to December 2013.

Status of the figures: The figures in this table are provisional when published. As this table has been discontinued, they will not become definite. In July data of the previous year are made definite based on the results of a yearly survey. The data for January-May of the current year are also adjusted, but remains provisional. Other adjustments may be made when new or additional information from respondents becomes available.

Changes as of 24 February 2014: Data for December 2013 have been added and the table has been discontinued.

As a result of a number of ambiguities, on 23 February 2012 the figures on Credit card credit were removed as a matter of precaution. The figures will be replaced as soon as possible.

When will new figures be published? As a result of budget restrictions affecting Statistics Netherlands, from 2014 onwards a number of statistics will be either discontinued, published less frequently or published in less detail. Following consultation of our main users, one of the statistics to be discontinued is the series on Consumer Credit. This table is therefore the last in the series. For more information about reduction in the statistical work programme, see Statistics Netherlands Strategic multi-annual programme 2014-2018.

Short-Term-Debt Time Series for Jack Henry & Associates Inc. Jack Henry & Associates, Inc. operates as a financial technology company that connects people and financial institutions through technology solutions and payment processing services. It operates through four segments: Core, Payments, Complementary, and Corporate and Other. The Core segment provides core information processing platforms to banks and credit unions, which consist of integrated applications required to process deposit, loan, general ledger transactions, and maintain centralized accountholder information. The Payments segment offers secure payment processing tools and services, including ATM, automated clearing house origination and remote deposit capture processing, and risk management products and services, as well as debit and credit card processing services, and online and mobile bill pay solutions. The Complementary segment provides software, and hosted processing platforms and services comprising digital/mobile banking, treasury, online account opening, fraud/anti-money laundering, and lending/deposit solutions. The Corporate and Other segment offers hardware and other products. It offers specialized financial performance, imaging and payment, information security and risk management, retail delivery, and online and mobile solutions to financial services organizations and corporate entities. The company also provides SilverLake system, a system primarily designed for commercial-focused banks; Symitar, a system designed for credit unions; CIF 20/20, a parameter-driven system for banks; and Core Director, a system with point-and-click operation for banks. It provides digital products and services under the Banno Digital Platform, and electronic payment solutions; hardware systems; implementation, training, and support and service solutions; data center solutions; and data and transaction processing, and software licensing and related services, as well as professional services. The company was founded in 1976 and is headquartered in Monett, Missouri.

Private Debt to GDP in the United States decreased to 142 percent in 2024 from 147.50 percent in 2023. United States Private Debt to GDP - values, historical data, forecasts and news - updated on December of 2025.

Total-Debt-To-Ebitda Time Series for First Foundation Inc.. First Foundation Inc., together with its subsidiaries, provides banking services, investment advisory, wealth management, and trust services to individuals, businesses, and other organizations in the United States. The company operates in two segments, Banking and Wealth Management. It offers a range of deposit products, including personal and business checking accounts, savings accounts, interest-bearing demand deposit accounts, money market accounts, and time certificate of deposits; and loan products consisting of multifamily and single family residential real estate loans, commercial real estate loans, commercial term loans, and line of credits, as well as consumer loans, such as personal installment loans and line of credits, and home equity line of credits. The company provides various specialized services comprising trust services, online and mobile banking, remote deposit capture services, merchant credit card services, ATM cards, Visa debit cards, and business sweep accounts, as well as insurance brokerage services and equipment financing solutions. In addition, it offers investment management and financial planning services; financial, investment, and economic advisory and related services; and treasury management services, such as bill pay, check/payee/ACH positive pay, wire origination, internal and external transfers, account reconciliation reporting, mobile deposit, lockbox, cash vault services and merchant processing. Further, the company provides support services, including the processing and transmission of financial and economic data for charitable organizations. It operates through a network of branch offices and loan production offices. The company was founded in 1985 and is headquartered in Dallas, Texas.

Total-Long-Term-Debt Time Series for Discover Financial Services. Discover Financial Services, through its subsidiaries, provides digital banking products and services, and payment services in the United States. It operates in two segments, Digital Banking and Payment Services. The Digital Banking segment offers Discover-branded credit cards to individuals; personal loans, home loans, and deposit products; and direct-to-consumer deposit products comprising savings accounts, certificates of deposit, money market accounts, IRA certificates of deposit, IRA savings accounts and checking accounts, and sweep accounts. The Payment Services segment operates the PULSE to access automated teller machines (ATMs), debit, and electronic funds transfer network; Diners Club, a payments network; and Network Partners business, which provides payment transaction processing and settlement, merchant acquisition, ATM access, and related payments services in the global network. Discover Financial Services was incorporated in 1960 and is headquartered in Riverwoods, Illinois. As of May 18, 2025, Discover Financial Services operates as a subsidiary of Capital One Financial Corporation.

The dataset comprises a broad range of variables to understand the full picture of consumers’ financial health: family socio-demographics, total income, total expenses, employment information, as well as all credit details. The features considered for the analyses were: socio-demographic characterization (marital status, level of education completed, number of people in the household), the perceived causes for over-indebtedness (from a predetermined pool of causes), and data concerning their economic situation, including the total income and expenses of the household as well as data concerning their credits and debts (amount of the monthly installments for credit cards, housing credit, car credit, personal credit and other types of credit or debts; total monthly installment concerning all credits). Each household is represented by one record (one observation) of the dataset with many features to describe their characteristics and behavior

Co-Branded Credit Card Defaults

Analyzing Application and Credit History Data

By Amit Kishore [source]

About this dataset

This dataset provides insights into the predictability of co-branded credit card default in a retail network of a company. With over [x] columns of data, this dataset contains information ranging from applicants' demographics and credit scores to their limits and payment history. This comprehensive dataset was constructed with the goal of understanding how demographic factors influence credit risk and ultimately, co-branded credit card default rates. From age to income, marital status to educational background, each variable is used to create an understanding of the risks associated with applicants taking out co-branded cards in the retail network. Additionally, get an inside look at current trends in loan application behavior — see how often customers use loan or have applied for new cards over set time intervals — as well as monthly payments and query history. Use this unique dataset to develop an improved model for predicting credit card default that could help financial institutions assess potential cusotmers more accuracyly!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset aims to help predict co-branded credit card defaults in retail networks by providing a variety of information about the applicants. The dataset includes information such as age, gender, marital status, employment status, education level, monthly income and expenses, credit history length, number of loans and credit cards owned by the applicant, number of times they applied for loan/credit card inquiries and how many times they used each loan/credit card in the last months.
- In order to use this dataset effectively to predict co-branded credit card default rates in a retail network it is important to understand the data and how it's related each other. It is also important to consider any external factors that can influence an individual's likelihood of defaulting on a loan.
- The first step is to look at the descriptive statistics for each column so that we can get some idea as to what kind of values are seen most often among our data points and if there are any outliers present. This will give us an idea about which features may be most relevant when predicting defaults or if our model may need more contextual information from outside sources like socio-economic or political factors.
- Once we have identified any relevant features from our descriptive statistics analysis we'll then want to start exploring different ways these variables are related with one another and what kind of relationship these variables have with regards to defaults (both positively correlated/directly increase default risk plus negatively correlated/directly decrease default risk). This can be done through simple pair plots which show distribution and correlations between two given columns or triangular heatmaps which allow us explore correlations among multiple columns at once. Building upon these relationships further allows us then determine possible causes behind the observed correlations between different variable groups – allowing us get even more insight into why certain individuals are more likely than others be defaulters on their co-branded cards (whether it because they simply had bad luck or because there were larger systematic factors playing out).
- Having identified all relevant features from this data exploration process along with any external “background” data points - we finally move into constructing our machine learning models using appropriate algorithms suitable for predicting probability outcomes such as SVM or XGBoost tree ensembles etc.. When building out your ML model you’ll want ensure that all parameters necessary for accurate predictions have been included before deploying them on production systems so as not compromise neither customer privacy nor product quality standards set by regulatory authorities governing such models across countries globally

Research Ideas

• Using the given dataset to create a predictive model that can be used to identify customers at risk of defaulting on their co-branded credit cards. This could help determine which customers should be offered special incentives or strategies in order to reduce their risk of defaulting.
• Using the given dataset to create a financial health recommendation engine that analyzes customer’s existing credit cards and recommends other ways they can improve their financial situation (e.g., balance transfers, better rewards programs, etc.).
• Extracting insights from the data by...

Debt-To-Equity-Ratio Time Series for First Foundation Inc.. First Foundation Inc., together with its subsidiaries, provides banking services, investment advisory, wealth management, and trust services to individuals, businesses, and other organizations in the United States. The company operates in two segments, Banking and Wealth Management. It offers a range of deposit products, including personal and business checking accounts, savings accounts, interest-bearing demand deposit accounts, money market accounts, and time certificate of deposits; and loan products consisting of multifamily and single family residential real estate loans, commercial real estate loans, commercial term loans, and line of credits, as well as consumer loans, such as personal installment loans and line of credits, and home equity line of credits. The company provides various specialized services comprising trust services, online and mobile banking, remote deposit capture services, merchant credit card services, ATM cards, Visa debit cards, and business sweep accounts, as well as insurance brokerage services and equipment financing solutions. In addition, it offers investment management and financial planning services; financial, investment, and economic advisory and related services; and treasury management services, such as bill pay, check/payee/ACH positive pay, wire origination, internal and external transfers, account reconciliation reporting, mobile deposit, lockbox, cash vault services and merchant processing. Further, the company provides support services, including the processing and transmission of financial and economic data for charitable organizations. It operates through a network of branch offices and loan production offices. The company was founded in 1985 and is headquartered in Dallas, Texas.

Credit bureaus and rating agencies in the US have experienced notable growth in recent years due to heightened demand for information. The reliance on data analytics has driven increased interest in these services, which provide vital information on creditworthiness for both individuals and businesses. This has been particularly significant as businesses and individuals seek to make well-informed financial decisions. Despite economic challenges throughout the period, inflationary pressures and high interest rates, the industry has thrived and profit has climbed, indicating its resilience and the critical nature of the services it offers in a data-driven economy. While long-term demand for information has buoyed the industry, providers’ trajectory has been influenced by broader economic conditions, notably equity market fluctuations. The industry weathered economic disruptions, specifically at the onset of the period, although rapid fiscal and monetary responses bolstered investor confidence and led to robust growth in equity markets, contributing to massive revenue growth at the start of the period. Soaring interest rates heightened recessionary fears among investors, hindering demand for equities and limiting stock price growth. These effects have permeated the real economy, as consumer and business borrowing have slowed, thereby limiting growth in aggregate household debt and corporate debt. Overall, revenue for credit bureaus and rating agencies in the US has grown at a CAGR of 2.7% to $17.6 billion over the past five years, including an expected increase of 0.6% in 2025 alone. In addition, industry profit has climbed and will comprise 11.7% of revenue in the current year. Looking ahead, credit bureaus and rating agencies will face a more tempered growth trajectory over the next five years. The broad adoption of online services and data analytics has led to market saturation, reducing opportunities for exponential revenue growth. Nonetheless, stable economic growth and business formation are expected to sustain a steady demand for credit reporting and rating services. The predicted slower growth in equity prices will moderate financial institutions' borrowing capacity, which will also contribute to the slowdown in revenue growth. Overall, revenue for credit bureaus and rating agencies in the United States is forecast to inch upward at a CAGR of 0.7% to $18.2 billion over the five years to 2030.