Provides snapshots of the outstanding federal student loan portfolio by loan program, loan type, loan status, repayment plan, delinquency, servicer, and various borrower demographics.

JPMorgan Chase and Bank of America were by far the banks with the largest consumer loan portfolios in the United States in 2023. The figures for UBS and Santander only refer to their activities in the U.S. and not their international loan portfolio. Consumer loans are those provided to individuals, such as mortgages, car loans, student loans, or personal loans. JPMorgan Chase and Bank of America were also the largest U.S. banks in terms of total assets in 2023.

Graph and download economic data for Student Loans Owned and Securitized (SLOAS) from Q1 2006 to Q4 2024 about student, securitized, owned, loans, and USA.

In the first half of 2023, the non-banking financial companies witnessed a growth of over 43 percent in their education loan book growth. Banks, on the other hand, registered growth of almost six percent. The education loan segment, although, primarily dominated by public sector banks, witnessed a spike in specialized NBFCs focused on the overseas education loan segment in the last five years. The higher loan book growth of NBFCs is mainly driven by this demand for overseas education loans.

The 2006 Federal Campus-Based Programs Data Book provides comprehensive program funding information for these federal student aid programs: Federal Supplemental Educational Opportunity Grants, Federal Work-Study, and Federal Perkins Loans. Program allocation data is presented for award year 2006-2007. Fiscal and Recipient data are presented for award year 2004-2005. This is the home page for the data book.

Ecuador Financial System: Private: Loan Portfolio: Multiple Banks: Current: Education data was reported at 399,460.190 USD th in Jul 2019. This records a decrease from the previous number of 401,724.017 USD th for Jun 2019. Ecuador Financial System: Private: Loan Portfolio: Multiple Banks: Current: Education data is updated monthly, averaging 399,460.190 USD th from Jan 2019 (Median) to Jul 2019, with 7 observations. The data reached an all-time high of 407,928.813 USD th in Apr 2019 and a record low of 396,256.082 USD th in Jan 2019. Ecuador Financial System: Private: Loan Portfolio: Multiple Banks: Current: Education data remains active status in CEIC and is reported by Superintendence of Banks. The data is categorized under Global Database’s Ecuador – Table EC.KB005: Financial System: Private: Loan Portfolio: Multiple Banks.

This dataset is about books and is filtered where the book subjects is Student loan funds, featuring 9 columns including author, BNB id, book, book publisher, and book subjects. The preview is ordered by publication date (descending).

Free yourself from student loan debt : get out from under once and for all is a book. It was written by Brian O'Connell and published by Dearborn in 2004.

In the financial year 2023, home loans dominated the retail loan market in India by portfolio outstanding or value with a share of 40.7 percent, followed by personal loans with over 14 percent. Consumer-durable loans had the lowest portfolio outstanding or value in the retail loan category. Retail loans are loans given to individual consumers for various reasons such as purchase of property, vehicles, consumer durables, funding education etc.

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 . Historical program data is available electronically since 2006 at . 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).

Financial System: Private: Loan Portfolio: Multiple Banks: Gross: Education在2019-07达411,306.440 美元 千，相较于2019-06的415,099.921 美元 千有所下降。Financial System: Private: Loan Portfolio: Multiple Banks: Gross: Education数据按月度更新，2019-01至2019-07期间平均值为411,306.440 美元 千，共7份观测结果。该数据的历史最高值出现于2019-04，达420,413.467 美元 千，而历史最低值则出现于2019-01，为399,010.630 美元 千。CEIC提供的Financial System: Private: Loan Portfolio: Multiple Banks: Gross: Education数据处于定期更新的状态，数据来源于Superintendence of Banks，数据归类于Global Database的厄瓜多尔 – Table EC.KB005: Financial System: Private: Loan Portfolio: Multiple Banks。

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Student Loan Meltdown? (SLM)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This dataset is about books and is filtered where the book is Financing university education : a study of university fees and loans to students in Great Britain, featuring 7 columns including author, BNB id, book, book publisher, and ISBN. The preview is ordered by publication date (descending).

This dataset is about book subjects and is filtered where the books is The complete university guide : student finance : your guide to loans, bursaries, grants, tuition fees and preparing your own budget, featuring 2 columns: book subject, and publication dates. The preview is ordered by number of books (descending).

This dataset is about book subjects and is filtered where the book subject is Medical education-United States-Finance, featuring one column called book subject. The preview is ordered by number of books (descending).