By 2035, the gross federal debt of the United States is projected to be about 59.3 trillion U.S. dollars. This would be an increase of around 24 trillion U.S. dollars from 2024, when the federal debt was around 35 trillion U.S. dollars. The federal debt of the U.S. The federal debt, also called the national debt or public debt, is the amount of debt held by the United States government. This debt may be to other countries, or to different departments within the government itself. The public debt of the United States has increased significantly over the past 30 years, as it was around 3.2 trillion U.S. dollars in 1990 and surpassed 30 trillion dollars for the first time in 2022. When broken down per capita, the national debt amounted to about 80,885 U.S. dollars of debt per person in the United States in 2021. The problem of the federal debt Over the past decade, the federal debt limit in the United States has increased significantly. The U.S. debt ceiling can only be changed by an act of Congress which is then signed by the president. The raising of the ceiling has become a recurring political issue in recent years, especially during times when the Presidency and chambers of Congress are controlled by different parties. The debt ceiling is a tool that allows the Treasury to issue bonds without congressional approval, allowing for efficiency in the way that the government pays for programs and services. It is thought to be further valuable in that it keeps federal finances in check. However, when the two parties are unable to come to an agreement on raising the debt ceiling, the government comes to a shutdown because they can no longer fund themselves. The Republican Party in particular often positions itself against raising the federal debt ceiling, characterizing themselves as the party of fiscal conservativism. However, analyses have shown that both parties have contributed to the country's debt in almost equal measures.

Graph and download economic data for Delinquency Rate on All Loans, All Commercial Banks (DRALACBN) from Q1 1985 to Q3 2025 about delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

In the fiscal year of 2019, around 4.1 percent of students who went to private, for-profit public 2-year institutions in the United States were in default on their loans. The default rate for students in the FY 2019 cohort was 1.9 percent at 4-year degree-granting postsecondary institutions, and 3.8 percent at 2-year degree-granting postsecondary institutions.

In 2022, the student loan default rate in the United States was highest for Black borrowers, at **** percent. In comparison, Asian borrowers were least likely to default on their student loans.

Graph and download economic data for Delinquency Rate on Credit Card Loans, All Commercial Banks (DRCCLACBS) from Q1 1991 to Q3 2025 about credit cards, delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

(Banking And Systemic Crises)

prepared by (Mohamed Abd Al-mgyd)

https://github.com/1145267383/Systemic-And-Banking-Crises

• Web APP > https://crises.herokuapp.com/

Dataset

A)20160923_global_crisis_data:

https://www.hbs.edu/behavioral-finance-and-financial-stability/data/Pages/global.aspx

This data was collected over many years by Carmen Reinhart (with her coauthors Ken Rogoff, Christoph Trebesch, and Vincent Reinhart). This data contains the banking crises of 70 countries, from 1800 AD to 2016 AD, with a total of 15,190 records and 16 variables. But the data stabilized after cleaning and adjusting to 8642 records and 17 variables.

B)Label_Country: This data contains a description of the country whether it's Developing or Developed .

Variable: Description:

1-Case: ID Number for Country.

2-Cc3: ID String for Country.

3-Country : Name Country.

4-Year: The date from 1800 to 2016.

5-Banking_Crisis: Banking problems can often be traced to a decrease the value of banks' assets.

A) due to a collapse in real estate prices or When the bank asset values decrease substantially . B) if a government stops paying its obligations, this can trigger a sharp decline in value of bonds.

6-Systemic_Crisis : when many banks in a country are in serious solvency or liquidity problems at the same time—either:

A) because there are all hits by the same outside shock. B) or because failure in one bank or a group of banks spreads to other banks in the system.

7-Gold_Standard: The Country have crisis in Gold Standard.

8-Exch_Usd: Exch local currency in USD, Except exch USD currency in GBP.

9-Domestic_Debt_In_Default: The Country have domestic debt in default.

10-Sovereign_External_Debt_1: Default and Restructurings, -Does not include defaults on WWI debt to United States and United Kingdom and post-1975 defaults on Official External Creditors.

11-Sovereign_External_Debt_2: Default and Restructurings, -Does not include defaults on WWI debt to United States and United Kingdom but includes post-1975 defaults on Official External Creditors.

12-Gdp_Weighted_Default:GDP Weighted Default for country.

13-Inflation: Annual percentages of average consumer prices.

14-Independence: Independence for country.

15-Currency_Crises: The Country have crisis in Currency.

16-Inflation_Crises: The Country have crisis in Inflation.

17-Level_Country: The description of the country whether it's Developing or Developed.

Delinquency rates for credit cards picked up in 2025 in the United States, leading to the highest rates observed since 2008. This is according to a collection of one of the United States' federal banks across all commercial banks. The high delinquency rates were joined by the highest U.S. credit card charge-off rates since the Financial Crisis of 2008. Delinquency rates, or the share of credit card loans overdue a payment for more than ** days, can sometimes lead into charge-off, or a writing off the loan, after about six to 12 months. These figures on the share of credit card balances that are overdue developed significantly between 2021 and 2025: Delinquencies were at their lowest point in 2021 but increased to one of their highest points by 2025. This is reflected in the growing credit card debt in the United States, which reached an all-time high in 2023. As of Q2 2025, the delinquency rate stands at 3.05%.

Graph and download economic data for Number of Domestic Banks That Tightened and Reported That Increase in Defaults by Borrowers in Public Debt Markets Was a Somewhat Important Reason (SUBLPDCIRTDSNQ) from Q3 2000 to Q1 2011 about borrowings, public, debt, domestic, banks, depository institutions, and USA.

These tables provide additional detail on the loan assets of U.S. depository institutions by reporting mortgage and consumer loan portfolios broken down by the banks' estimates of the probability of default, as defined below. This information facilitates analysis of the potential concentration of risk in specific loan categories. The institutions reporting this information are generally those with $10 billion or more of assets.

Graph and download economic data for Delinquency Rate on Consumer Loans, All Commercial Banks (DRCLACBS) from Q1 1987 to Q3 2025 about delinquencies, commercial, loans, consumer, banks, depository institutions, rate, and USA.

Graph and download economic data for Number of Domestic Banks That Eased and Reported That Reduction in Defaults by Borrowers in Public Debt Markets Was Not an Important Reason (SUBLPDCIREDNNQ) from Q3 2000 to Q1 2011 about ease, borrowings, public, debt, domestic, banks, depository institutions, and USA.

United States Loan Officer Survey: DB Other Banks: Very Important data was reported at 11.100 % in Jan 2019. This records an increase from the previous number of 0.000 % for Oct 2018. United States Loan Officer Survey: DB Other Banks: Very Important data is updated quarterly, averaging 0.000 % from Jan 2008 (Median) to Jan 2019, with 45 observations. The data reached an all-time high of 50.000 % in Apr 2017 and a record low of 0.000 % in Oct 2018. United States Loan Officer Survey: DB Other Banks: Very Important data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.S028: Senior Loan Officer Opinion Survey: Lending Policies: Reason for Credit Tightening. Senior Loan Officer Survey Questionnaire: If your bank has tightened its credit standards or its terms for C&I loans or credit lines over the past three months, how important have been the increase in borrowers default in debt market for the change?

🏦 Synthetic Loan Approval Dataset

A Realistic, High-Quality Dataset for Credit Risk Modelling

🎯 Why This Dataset?

Most loan datasets on Kaggle have unrealistic patterns where:

1. ❌ Credit scores don't matter
2. ❌ Approval logic is backwards
3. ❌ Models learn nonsense patterns

Unlike most loan datasets available online, this one is built on real banking criteria from US and Canadian financial institutions. Drawing from 3 years of hands-on finance industry experience, the dataset incorporates realistic correlations and business logic that reflect how actual lending decisions are made. This makes it perfect for data scientists looking to build portfolio projects that showcase not just coding ability, but genuine understanding of credit risk modelling.

📊 Dataset Overview

🔑 Key Features

Identifier:

-Customer ID (unique identifier for each application)

Demographics:

-Age, Occupation Status, Years Employed

Financial Profile:

-Annual Income, Credit Score, Credit History Length -Savings/Assets, Current Debt

Credit Behaviour:

-Defaults on File, Delinquencies, Derogatory Marks

Loan Request:

-Product Type, Loan Intent, Loan Amount, Interest Rate

Calculated Ratios:

-Debt-to-Income, Loan-to-Income, Payment-to-Income

💡 What Makes This Dataset Special?

1️⃣ Real-World Approval Logic The dataset implements actual banking criteria: - DTI ratio > 50% = automatic rejection - Defaults on file = instant reject - Credit score bands match real lending thresholds - Employment verification for loans ≥$20K

2️⃣ Realistic Correlations - Higher income → Better credit scores - Older applicants → Longer credit history - Students → Lower income, special treatment for small loans - Loan intent affects approval (Education best, Debt Consolidation worst)

3️⃣ Product-Specific Rules - Credit Cards: More lenient, higher limits - Personal Loans: Standard criteria, up to $100K - Line of Credit: Capped at $50K, manual review for high amounts

4️⃣ Edge Cases Included - Young applicants (age 18) building first credit - Students with thin credit files - Self-employed with variable income - High debt-to-income ratios - Multiple delinquencies

🎓 Perfect For - Machine Learning Practice: Binary classification with real patterns - Credit Risk Modelling: Learn actual lending criteria - Portfolio Projects: Build impressive, explainable models - Feature Engineering: Rich dataset with meaningful relationships - Business Analytics: Understand financial decision-making

📈 Quick Stats

Approval Rates by Product - Credit Card: 60.4% more lenient) - Personal Loan: 46.9 (standard) - Line of Credit: 52.6% (moderate)

Loan Intent (Best → Worst Approval Odds) 1. Education (63% approved) 2. Personal (58% approved) 3. Medical/Home (52% approved) 4. Business (48% approved) 5. Debt Consolidation (40% approved)

Credit Score Distribution - Mean: 644 - Range: 300-850 - Realistic bell curve around 600-700

Income Distribution - Mean: $50,063 - Median: $41,608 - Range: $15K - $250K

🎯 Expected Model Performance

With proper feature engineering and tuning: - Accuracy: 75-85% - ROC-AUC: 0.80-0.90 - F1-Score: 0.75-0.85

Important: Feature importance should show: 1. Credit Score (most important) 2. Debt-to-Income Ratio 3. Delinquencies 4. Loan Amount 5. Income

If your model shows different patterns, something's wrong!

🏆 Use Cases & Projects

Beginner - Binary classification with XGBoost/Random Forest - EDA and visualization practice - Feature importance analysis

Intermediate - Custom threshold optimization (profit maximization) - Cost-sensitive learning (false positive vs false negative) - Ensemble methods and stacking

Advanced - Explainable AI (SHAP, LIME) - Fairness analysis across demographics - Production-ready API with FastAPI/Flask - Streamlit deployment with business rules

⚠️ Important Notes

This is SYNTHETIC Data - Generated based on real banking criteria - No real customer data was used - Safe for public sharing and portfolio use

Limitations - Simplified approval logic (real banks use 100+ factors) - No temporal component (no time series) - Single country/currency assumed (USD) - No external factors (economy, market conditions)

Educational Purpose This dataset is designed for: - Learning credit risk modeling - Portfolio projects - ML practice - Understanding lending criteria

NOT for: - Actual lending decisions - Financial advice - Production use without validation

🤝 Contributing

Found an issue? Have suggestions? - Open an issue on GitHub - Suggest i...

United States Loan Officer Survey: DB Large Banks: Not Important data was reported at 100.000 % in Apr 2018. This stayed constant from the previous number of 100.000 % for Jan 2018. United States Loan Officer Survey: DB Large Banks: Not Important data is updated quarterly, averaging 92.000 % from Jan 2008 (Median) to Apr 2018, with 40 observations. The data reached an all-time high of 100.000 % in Apr 2018 and a record low of 50.000 % in Apr 2011. United States Loan Officer Survey: DB Large Banks: Not Important data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.KA041: Senior Loan Officer Opinion Survey: Lending Policies: Reason for Credit Tightening. Senior Loan Officer Survey Questionnaire: If your bank has tightened its credit standards or its terms for C&I loans or credit lines over the past three months, how important have been the increase in borrowers default in debt market for the change?

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.

United States Loan Officer Survey: DB Large Banks: Somewhat Important data was reported at 0.000 % in Apr 2018. This stayed constant from the previous number of 0.000 % for Jan 2018. United States Loan Officer Survey: DB Large Banks: Somewhat Important data is updated quarterly, averaging 6.700 % from Jan 2008 (Median) to Apr 2018, with 40 observations. The data reached an all-time high of 50.000 % in Apr 2011 and a record low of 0.000 % in Apr 2018. United States Loan Officer Survey: DB Large Banks: Somewhat Important data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.KA041: Senior Loan Officer Opinion Survey: Lending Policies: Reason for Credit Tightening. Senior Loan Officer Survey Questionnaire: If your bank has tightened its credit standards or its terms for C&I loans or credit lines over the past three months, how important have been the increase in borrowers default in debt market for the change?

Loan Credit Default Swaps Market Outlook

According to our latest research, the global Loan Credit Default Swaps (CDS) market size reached USD 1.27 trillion in 2024. The market is experiencing robust expansion, propelled by evolving risk management strategies and the increasing complexity of global credit markets. The Loan Credit Default Swaps market is projected to grow at a CAGR of 7.8% from 2025 to 2033, reaching a forecasted market size of USD 2.51 trillion by 2033. This growth is primarily attributed to heightened demand for credit risk mitigation tools among financial institutions and the rising need for transparent, liquid, and efficient hedging instruments in the wake of global economic uncertainties.

One of the principal growth drivers for the Loan Credit Default Swaps market is the intensification of credit risk in both developed and emerging markets. As global debt levels rise and economic cycles become increasingly unpredictable, financial institutions and investors are seeking advanced instruments to hedge against potential defaults. The increasing sophistication of financial markets has led to a greater reliance on credit derivatives such as CDS to manage exposures and protect against losses. Furthermore, regulatory reforms post-2008 financial crisis have encouraged greater transparency and standardization in the CDS market, making these instruments more accessible and appealing to a wider array of market participants.

Another significant factor fueling the expansion of the Loan Credit Default Swaps market is the diversification of product offerings. Market participants are not only utilizing single-name CDS but are also increasingly engaging with index CDS and basket CDS to gain exposure to broader credit markets or specific segments. This diversification allows investors and institutions to tailor their risk management strategies more precisely, aligning with their unique risk appetites and investment objectives. The proliferation of customized CDS contracts, alongside the growth of standard contracts, is further enhancing the flexibility and appeal of these products, thereby driving market growth.

Technological advancements and digitalization are also playing a pivotal role in shaping the Loan Credit Default Swaps market. The adoption of advanced analytics, machine learning, and blockchain technology is streamlining the trading, pricing, and settlement of CDS contracts. These innovations are reducing operational risks, minimizing transaction costs, and improving overall market efficiency. Additionally, the integration of real-time data analytics is enabling market participants to make more informed decisions, thus increasing the attractiveness of CDS as a risk management tool. This digital transformation is expected to continue supporting the growth trajectory of the Loan Credit Default Swaps market over the forecast period.

From a regional perspective, North America remains the dominant market for Loan Credit Default Swaps, accounting for a substantial share of global volumes. The region’s mature financial infrastructure, coupled with the presence of major international banks and asset managers, underpins its leadership position. Europe follows closely, supported by a well-established regulatory framework and a high degree of market sophistication. Meanwhile, the Asia Pacific region is witnessing rapid growth, driven by financial sector liberalization and increasing adoption of risk management tools in emerging economies such as China and India. Latin America and the Middle East & Africa, though smaller in market size, are expected to register above-average growth rates as financial markets deepen and regulatory frameworks evolve to support derivative trading.

Product Type Analysis

The Loan Credit Default Swaps market is segmented by product type into Single-name CDS, Index CDS, Basket CDS, and Others. Single-name CDS remains the most widely used product type, accounting for the largest share of the market in 2024. These instruments allow investors to hedge or speculate on the credit risk associated with a specific reference entity, such as a corporation or sovereign government. The popularity of single-name CDS stems from their simplicity, liquidity, and the direct exposure they provide to individual credit events. Financial institutions, asset managers, and hedge funds frequently use single-name CDS to manage exposures in their loan portfolios or to express views on the creditworthiness of specific entities.

The Federal Perkins Loan Cohort Default Rates is a data collection that is part of the Federal Perkins Loan program; the most recent Federal Perkins Loan Cohort Default Rates are available https://ifap.ed.gov/ifap/byAwardYear.jsp?type=perkinscdrguide&display=single. Historical program data is available electronically since 2006 at https://ifap.ed.gov/ifap/byAwardYear.jsp?type=perkinscdrguide&set=archive&display=single. The data collection is conducted using a web-based entry system wherein postsecondary institutions must submit information electronically if they participate in the Federal Perkins Loan program. Key statistics produced from this data collection are the Federal Perkins Loan cohort default rates (previously known as the Orange Book).

A cohort default rate is the percentage of a school's borrowers who enter repayment on certain Federal Family Education Loan (FFEL) Program or William D. Ford Federal Direct Loan (Direct Loan) Program loans during a particular federal fiscal year (FY), October 1 to September 30, and default or meet other specified conditions prior to the end of the second following fiscal year, as calculated by Federal Student Aid using data derived from the National Student Loan Data System (NSLDS).

View quarterly updates and historical trends for US Auto Loans Delinquent by 90 or More Days. from United States. Source: Federal Reserve Bank of New York…