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.

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.

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...

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

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.

Share-of-Periods-With-Dividend-Payments-In-Percent Time Series for Moodys Corporation. Moody's Corporation operates as an integrated risk assessment firm worldwide. It operates in two segments, Moody's Analytics and Moody's Investors Services. The Moody's Analytics segment develops a range of products and services that support the risk management activities of institutional participants in financial markets. It offers credit research, credit models and analytics, economics data and models, and structured finance solutions; data sets on companies and securities; and SaaS solutions supporting banking, insurance, and know your customer workflows. The Moody's Investors Service segment publishes credit ratings and provides assessment services on various debt obligations, programs and facilities, and entities that issue such obligations, such as various corporate, financial institution, and governmental obligations, as well as structured finance securities. The company was formerly known as Dun and Bradstreet Company and changed its name to Moody's Corporation in September 2000. The company was founded in 1900 and is headquartered in New York, New York.

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.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Turkey. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 499,842,484,724.00 Turkish Liras as of 12/31/2023, the highest value at least since 12/31/1971, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 9.16 percent compared to the value the year prior.The 1 year change in percent is 9.16.The 3 year change in percent is 16.30.The 5 year change in percent is 17.11.The 10 year change in percent is 24.28.The Serie's long term average value is 161,541,400,372.72 Turkish Liras. It's latest available value, on 12/31/2023, is 209.42 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1970, to it's latest available value, on 12/31/2023, is +18,099.56%.The Serie's change in percent from it's maximum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is 0.0%.

Context

The card payments data is published by the Reserve Bank of India on a monthly basis. The statistics cover the methods of payment used in retail transactions and ATM transactions in India. It constitutes payments via debit cards, credit cards, ATMs etc, . It can can be used to check trend of card based payment in India.

Content

The data contains monthly statistics of the following information from Apr'2011 to Aug'2019 1. Number of ATM deployed on site by the bank. 1. Number of ATM deployed off site by the bank. 1. Number of POS deployed online by the bank 1. Number of POS deployed offline by the bank 1. Total number of credit cards issued outstanding (after adjusting the number of cards withdrawan/cancelled). 1. Total number of financial transactions done by the credit card issued by the bank at ATMs 1. Total number of financial transactions done by the credit card issued by the bank at POS terminals 1. Total value of financial transactions done by the credit card issued by the bank at ATMs 1. Total value of financial transactions done by the credit card issued by the bank at POS terminals. 1. Total number of debit cards issued outstanding (after adjusting the number of cards withdrawan/cancelled). 1. Total number of financial transactions done by the debit card issued by the bank at ATMs 1. Total number of financial transactions done by the debit card issued by the bank at POS terminals 1. Total value of financial transactions done by the debit card issued by the bank at ATMs 1. Total value of financial transactions done by the debit card issued by the bank at POS terminals.

Acknowledgements

The data is scraped from RBI monthly statistics https://www.rbi.org.in/scripts/ATMView.aspx More details on how this data is collected and cleaned is documented in this kernel https://www.kaggle.com/karvalo/indian-card-payment-data-gathering-and-analysis

Time series data for the statistic External_Debt_Stocks_Total_$ and country Paraguay. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 26,135,416,108.00 Paraguayan Guaranýs as of 12/31/2023, the highest value at least since 12/31/1971, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 5.45 percent compared to the value the year prior.The 1 year change in percent is 5.45.The 3 year change in percent is 26.06.The 5 year change in percent is 52.10.The 10 year change in percent is 58.45.The Serie's long term average value is 7,175,312,453.28 Paraguayan Guaranýs. It's latest available value, on 12/31/2023, is 264.24 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1970, to it's latest available value, on 12/31/2023, is +18,033.00%.The Serie's change in percent from it's maximum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is 0.0%.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Tunisia. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 41,278,696,730.20 Tunisian Dinars as of 12/31/2023. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.676 percent compared to the value the year prior.The 1 year change in percent is 0.676.The 3 year change in percent is -1.66.The 5 year change in percent is 17.05.The 10 year change in percent is 57.18.The Serie's long term average value is 14,872,267,056.57 Tunisian Dinars. It's latest available value, on 12/31/2023, is 177.55 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1970, to it's latest available value, on 12/31/2023, is +6,743.29%.The Serie's change in percent from it's maximum value, on 12/31/2021, to it's latest available value, on 12/31/2023, is -4.05%.

DESCRIPTION

Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement:

For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance

Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Content:

Dataset columns and definition:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.

purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

installment: The monthly installments owed by the borrower if the loan is funded.

log.annual.inc: The natural log of the self-reported annual income of the borrower.

dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

fico: The FICO credit score of the borrower.

days.with.cr.line: The number of days the borrower has had a credit line.

revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.

delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Steps to perform:

Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.

Tasks:

1. Feature Transformation

Transform categorical values into numerical values (discrete)

1. Exploratory data analysis of different factors of the dataset.

2. Additional Feature Engineering

You will check the correlation between features and will drop those features which have a strong correlation

This will help reduce the number of features and will leave you with the most relevant features

1. Modeling

After applying EDA and feature engineering, you are now ready to build the predictive models

In this part, you will create a deep learning model using Keras with Tensorflow backend

Description

This dataset contains current estimates of world countries based on the External Debt. it is the total public and private debt owed to nonresidents repayable in internationally accepted currencies, goods or services, where the public debt is the money or credit owed by any level of government, from central to local, and the private debt the money or credit owed by private households or private corporations based on the country under consideration.

Attribute Information

• SLNO : Rank of the country based on the Highest External Debt.
• Location : Name of the country.
• External debt (USD) Per capita : External Debt based on the Per Capita of each country.
• External debt (USD) Total : Total External Debt of the country.
• External debt (USD) % of GDP : Percentage of External Debt of the country
• External debt (USD) % of Total Wealth: Percentage of Total External Debt of the country
• Date: Date from when the Data is Available.

Acknowledgements

https://en.wikipedia.org/wiki/List_of_countries_by_external_debt

***Photo by Alexander Grey on Unsplash

Problem Statement You are working as a data scientist in a global finance company. Over the years, the company has collected basic bank details and gathered a lot of credit-related information. The management wants to build an intelligent system to segregate the people into credit score brackets to reduce the manual efforts.

Task Given a person’s credit-related information, build a machine learning model that can classify the credit score.

• Age: Represents the age of the person
• Annual_Income: Represents the annual income of the person
• Monthly_Inhand_Salary: Represents the monthly base salary of a person
• Num_Bank_Accounts:Represents the number of bank accounts a person holds
• Num_Credit_Card: Represents the number of other credit cards held by a person
• Interest_Rate: Represents the interest rate on credit card
• Num_of_Loan: Represents the number of loans taken from the bank
• Delay_from_due_date: Represents the average number of days delayed from the payment date
• Num_of_Delayed_Payment: Represents the average number of payments delayed by a person
• Changed_Credit_Limit: Represents the percentage change in credit card limit
• Num_Credit_Inquiries: Represents the number of credit card inquiries
• Credit_Mix: Represents the classification of the mix of credits
• Outstanding_Debt: Represents the remaining debt to be paid (in USD)
• Credit_Utilization_Ratio: Represents the utilization ratio of credit card
• Credit_History_Age: Represents the age of credit history of the person
• Payment_of_Min_Amount: Represents whether only the minimum amount was paid by the person
• Total_EMI_per_month: Represents the monthly EMI payments (in USD)
• Amount_invested_monthly: Represents the monthly amount invested by the customer (in USD)
• Monthly_Balance: Represents the monthly balance amount of the customer (in USD)
• Credit_Score: Represents the bracket of credit score (Poor, Standard, Good)

SME - Synthetic UK Businesses financial statistics

Contains the financial and basic information about the 1000 small and medium enterprises in the UK. It contains attributes as far-reaching as the profit and losses of the entities and even their credit scores. It can be used to analyze the survival and success prediction of the enterprise.

This sample data is part of the statistically accurate representation of the UK economy that can be found at https://nayaone.com/digital-twin/. Our mission is democratization and quality data governance in areas where the lack of data is a major hurdle for innovation and progress. To learn more, contact us: contact@nayaone.com

Content

All the Synthetic datasets have been generated with programmatic stimulation to represent the real-world data. Description of the datasets are as follows: - Account Receivable: Funds that customers owe your company for products or services that have been invoiced. - Businesses: List of enterprises and their information - Covid: Financial stats of the companies during the pandemic waves - Credit Account History: History of a credit account and usage of - Credit Card History: History of the credit card usage and debt amount of an enterprise - Credit Rating: credit rating of listed businesses which is a quantified assessment of the creditworthiness of a borrower in general terms or with respect to a financial obligation. - Director: UK Individual who is on the Director position in companies listed in Businesses - Factoring: Financial transaction and a type of debtor finance in which a business sells its accounts receivable to a third party at a discount. - Individual: UK Individuals information - Loan: Information of the paid and unpaid Loans by the enterprise

Acknowledgements

The real data stats used to generate synthetic data are mainly gathered from the ONS, Public datasets and Known statistics.

Inspiration

This data can be used to train Machine learning models for better accuracy.

Negative Equity in U.S. Housing Market

Measuring Home Values, Debt, and Credit Risk

By Zillow Data [source]

About this dataset

This dataset, Negative Equity in the US Housing Market, provides an in-depth look into the negative equity occurring across the United States during this single quarter. Included are metrics such as total amount of negative equity in millions of dollars, total number of homes in negative equity, percentage of homes with mortgages that are in negative equity and more. These data points provide helpful insights into both regional and national trends regarding the prevalence and rate of home mortgage delinquency stemming from a diminishment of value from peak levels.

Home types available for analysis include 'all homes', condos/co-ops, multifamily units containing five or more housing units as well as duplexes/triplexes. Additionally, Cash buyers rates for particular areas can also be determined by referencing this collection. Further metrics such as mortgage affordability rates and impacts on overall indebtedness are readily calculated using information related to Zillow's Home Value Index (ZHVI) forecast methodology and TransUnion data respectively.

Other variables featured within this dataset include characteristics like region type (i.e city, county ..etc), size rank based on population values , percentage change in ZHVI since peak levels as well as loan-to-value ratio greater than 200 across all regions constituted herein (NE). Moreover Zillow's own Secondary Mortgage Market Survey data is utilized to acquire average mortgage quote rates while correlative Census Bureau NCHS median household income figures represent typical assessable proportions between wages and debt obligations . So whether you're looking to assess effects along metro lines or detailed buffering through zip codes , this database should prove sufficient for insightful explorations! Nonetheless users must strictly adhere to all conditions encompassed within Terms Of Use commitments put forth by our lead provider before accessing any resources included herewith

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Analyzing regional and state trends in negative equity: Analyze geographic differences in the percentage of mortgages “underwater”, total amount of negative equity, number of homes at least 90 days late, and other key indicators to provide insight into the factors influencing negative equity across regions, states and cities.
• Tracking the recovery rate over time: Track short-term changes in numbers related to negative equity (e.g., region or area ZHVI Change from Peak) to monitor recovery rates over time as well as how different policy interventions are affecting homeownership levels in affected areas.
• Exploring best practices for promoting housing affordability: Compare affordability metrics (e.g., mortgage payments, price-to-income ratios) across different geographic locations over time to identify best practices for empowering homeowners and promoting stability within the housing market while reducing local inequality impacts related to availability of affordable housing options and access to credit markets like mortgages/loans etc

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: NESummary_2017Q1_Public.csv | Column name | Description | |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | RegionType | The type of region (e.g., city, county, metro etc.) (String) | | City | Name of the city (String) | | County | Name of the county (String) | | State | Name of the state (String) | | Metro ...

Time series data for the statistic External_Debt_Stocks_Total_$ and country Iraq. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 20,331,392,379.40 Iraqi Dinars as of 12/31/2023, the lowest value since 12/31/2016. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -10.54 percent compared to the value the year prior.The 1 year change in percent is -10.54.The 3 year change in percent is -22.78.The 5 year change in percent is -26.86.The Serie's long term average value is 24,512,204,559.07 Iraqi Dinars. It's latest available value, on 12/31/2023, is 17.06 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2015, to it's latest available value, on 12/31/2023, is +6.25%.The Serie's change in percent from it's maximum value, on 12/31/2017, to it's latest available value, on 12/31/2023, is -27.60%.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Papua New Guinea. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 15,320,623,208.20 Papua New Guinean Kinas as of 12/31/2023, the lowest value since 12/31/2013. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -21.85 percent compared to the value the year prior.The 1 year change in percent is -21.85.The 3 year change in percent is -15.11.The 5 year change in percent is -13.56.The 10 year change in percent is -29.10.The Serie's long term average value is 5,765,879,615.05 Papua New Guinean Kinas. It's latest available value, on 12/31/2023, is 165.71 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1970, to it's latest available value, on 12/31/2023, is +7,238.32%.The Serie's change in percent from it's maximum value, on 12/31/2013, to it's latest available value, on 12/31/2023, is -29.10%.

Time series data for the statistic External_Debt_Stocks_Total_$ and country Mauritania. Indicator Definition:Total external debt is debt owed to nonresidents repayable in currency, goods, or services. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. Data are in current U.S. dollars.The statistic "External Debt Stocks Total $" stands at 4,603,556,865.20 Mauritanian Ouguiyas as of 12/31/2023. Regarding the One-Year-Change of the series, the current value constitutes an increase of 0.3827 percent compared to the value the year prior.The 1 year change in percent is 0.3827.The 3 year change in percent is -19.46.The 5 year change in percent is -11.87.The 10 year change in percent is 1.79.The Serie's long term average value is 2,422,989,675.21 Mauritanian Ouguiyas. It's latest available value, on 12/31/2023, is 89.99 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1970, to it's latest available value, on 12/31/2023, is +17,393.95%.The Serie's change in percent from it's maximum value, on 12/31/2020, to it's latest available value, on 12/31/2023, is -19.46%.