Due to the impact of the COVID-19 pandemic, starting on **************, the U.S. federal government paused payments on federal student loans, moving billions of dollars of student debt into forbearance. Federal student loans are in forbearance, meaning that no payments need to be made, and the interest rate has been set to zero percent until ******************. However, despite these measures, student debt increased in all states. The amount of student debt increased by ** percent in Alaska between 2019 and 2020, the most out of any state.

Concept: Average interest rate of individuals registered as MEI by type of credit - Other loans Source: Central Bank of Brazil - Department of Financial Education 82754285-cfe4-4780-9d9e-cdd1b8bfe491 27188-average-interest-rate-of-individuals-registered-as-mei-by-type-of-credit---other-loans

According to a survey conducted in 2022, ** percent of adults said that vocational training or other professional certification programs were definitely worth the price, more than other higher education institutions. Undergraduate education at private universities, for-profit, was perceived by adults as the least likely to be worth the price out of the other types. The student debt crisis In the United States, the amount of outstanding student loan debt has skyrocketed in the last few years, ultimately outpacing all other forms of household debt. As of the first quarter of 2024, Americans owed over **** trillion U.S. dollars in student loans, likely influenced by increasing college tuition prices at a time of rising living costs and little wage growth. By the 2020/21 academic year, the average cost of attending a four-year postsecondary institution in the U.S. reached over ****** U.S. dollars, a price which may triple for Americans attending private and non-profit schools. In that same year, the average student debt for a bachelor's degree in totaled almost ****** U.S. dollars, depicting an increase in the amount of Americans taking on larger debts to attend higher education - an agreement which ultimately leads to an even greater outstanding balance from accrued interest. Despite a three-and-a-half-year pause on monthly student loan payments during the COVID-19 pandemic which aimed to alleviate the economic burden faced by over ** million borrowers, most Americans still struggle to afford these payments. Cutting out college costs As the cost of college - and the resulting student debt - remains on the rise in the U.S., more and more university graduates have been found to be struggling financially, often having difficulty affording bills and other living expenses. Such financial hardships have also caused significant disruption to the lives of younger Americans, with a 2022 survey showing that around a ******* of Gen Z were unable to save for retirement or emergencies and had to delay homeownership and having children due to their student debt. Consequently, debates have arisen over whether the benefits of higher education still exceed the costs in the U.S., with many beginning to doubt that getting a college degree is worth the financial risk. While tuition costs remain at an all-time high, it is probable that financing a college degree may be detrimental for those Americans who have fewer resources and are unable to fund higher education without going into a significant amount of debt.

Concept: Average interest rate of credit operations with prefixed interest rates by source of funds and type of credit - individual microentrepreneur (MEI) - earmarked credit - Other loans Source: Central Bank of Brazil - Department of Financial Education 1d1bf5cc-0f06-4e98-ac68-00441e1fca15 26878-average-interest-rate-by-source-of-funds-and-type-of-credit---individual-microentrepreneur-me

This dataset was generated from a public earning's call (press release article). And used to generate examples of the way real humans would speak regarding the matters in the article, within real world scenarios. Here they are below:

Here are the linguistic variations for each of the queries in the dataset, based on the example article provided:

Here are five examples related to strong average loan growth in US Personal Banking (#5):

1. Mortgage Loans: An increase in demand for mortgage loans contributed to the strong average loan growth in US Personal Banking. Customers taking advantage of low interest rates led to a surge in mortgage applications and approvals.

2. Auto Loans: Robust consumer spending and increased car sales led to higher demand for auto loans, contributing to the strong loan growth in US Personal Banking. Customers seeking financing options for purchasing vehicles played a significant role in this growth.

3. Personal Loans: The availability of personal loans with favorable terms and competitive interest rates attracted borrowers, resulting in strong average loan growth in US Personal Banking. Customers availed personal loans for various purposes such as home improvements, debt consolidation, or financing other personal expenses.

4. Small Business Loans: US Personal Banking also witnessed strong loan growth due to increased lending to small businesses. As entrepreneurs and small business owners sought capital for expansion, equipment purchases, or working capital, the demand for small business loans rose, contributing to the growth.

5. Student Loans: The higher education sector continued to rely on student loans to finance tuition fees and related expenses. With the increasing cost of education, a rise in student loan applications and approvals contributed to the strong average loan growth in US Personal Banking.

General Queries Query: "What was the revenue for Personal Banking and Wealth Management (PBWM) in the last quarter?"

Variation 1: "What were the PBWM revenues in the previous quarter?" Variation 2: "Can you provide the revenue figure for PBWM in the last quarter?" Variation 3: "How much revenue did PBWM generate in the last quarter?" Variation 4: "What was the total revenue for PBWM in the most recent quarter?" Variation 5: "Could you tell me the revenue earned by PBWM in the last quarter?" Query: "What were the revenue figures for different divisions under US Personal Banking?"

Variation 1: "Can you provide the revenue breakdown for various divisions within US Personal Banking?" Variation 2: "What were the revenues generated by the different divisions in US Personal Banking?" Variation 3: "How did the revenue distribution look across different divisions in US Personal Banking?" Variation 4: "What were the individual revenue figures for each division within US Personal Banking?" Variation 5: "Could you give me a breakdown of the revenues for different divisions in US Personal Banking?" Query: "How did operating expenses change for PBWM?"

Variation 1: "What was the change in operating expenses for PBWM?" Variation 2: "Were there any fluctuations in the operating expenses of PBWM?" Variation 3: "How did the operating expenses for PBWM evolve over the specified period?" Variation 4: "Can you provide insights into the changes in operating expenses for PBWM?" Variation 5: "What was the percentage change in operating expenses for PBWM?" Query: "What factors contributed to the increase in PBWM's cost of credit?"

Variation 1: "What were the drivers behind the rise in PBWM's cost of credit?" Variation 2: "Which factors influenced the increase in PBWM's cost of credit?" Variation 3: "Can you identify the elements that led to the higher cost of credit for PBWM?" Variation 4: "What were the contributing factors to the cost of credit escalation in PBWM?" Variation 5: "What were the key reasons behind the growth in PBWM's cost of credit?" Query: "What led to the decrease in PBWM's net income?"

Variation 1: "What were the factors responsible for the decline in PBWM's net income?" Variation 2: "Can you identify the causes of the reduction in PBWM's net income?" Variation 3: "What influenced the decrease in net income for PBWM?" Variation 4: "Were there specific drivers that contributed to the decline in PBWM's net income?" Variation 5: "What were the primary reasons behind the decrease in PBWM's net income?" These linguistic variations provide different ways to ask the same questions, allowing for a more diverse and robust training dataset for the chatbot.

Here are the extracted entities from the provided article:

Account Line Entities:

Revenues Operating expenses Cost of credit Net income Business Line Entities:

Personal Banking and Wealth Management (PBWM) Branded Cards Retail Services Retail Banking Global Wealth Management Markets Banking Investment Banking Corporate Lending...

Concept: Average interest rate of credit operations with prefixed interest rates by source of funds and type of credit - individual microentrepreneur (MEI) - nonearmarked credit - Other loans Source: Central Bank of Brazil - Department of Financial Education 6f40f876-491d-4b6f-9fc9-f78befd37b3f 26913-average-interest-rate-by-source-of-funds-and-type-of-credit---individual-microentrepreneur-me

Concept: Average interest rate of individuals registered as MEI by type of credit - Personal loan - Payroll-deducted Source: Central Bank of Brazil - Department of Financial Education ae2bd991-fa7b-4bda-9ac4-27af6d6b028e 27170-average-interest-rate-of-individuals-registered-as-mei-by-type-of-credit---personal-loan---pa

Annual data on the structure of general government gross debt - Maastricht debt (ESA table 28A) is provided according to the European System of Accounts ESA 2010 Transmission Programme (Regulation (EU) No 549/2013, as amended by Regulation (EU) No 2023/734) and covers all EU Member States.

The majority of the 2330 collected data series are provided on a voluntary basis, but 36 series are to be provided on a compulsory basis (subject to derogations provided for in Commission implementing decision 2014/403).

The data is structured into the following data sets:

General government gross debt by subsector, by initial (original) maturity, by financial instrument and by sector of debt holder (gov_10dd_ggd): Containing data for general government and the subsectors by financial instrument, original maturity and counterpart sector. A breakdown for gross debt at variable interest is also included. A few series on transactions rather than balance sheets complement this collection.

• General government gross debt by currency of issuance (denomination) (gov_10dd_dcur): Containing data for general government and central government by currency denomination. It also includes the few series on transactions by currency denomination.
• Remaining maturity and average remaining maturity of debt (gov_10dd_rmd): The dataset contains data for general government and central government gross debt broken down by remaining maturity. Data for general government gross debt at variable interest by remaining maturity are also included here. The average remaining maturity of general government and central government gross debt is a part of this collection.
• Apparent cost of the debt (gov_10dd_acd): This may be defined as the interest rate applicable to the total Maastricht debt (AF.2, AF.3, and AF.4). It is a rate published on the basis of data supplied by compilers for general government and central government.
• Government guarantees (contingent liabilities) (gov_10dd_guar): Containing data for general government and central government on guarantees broken down by counterpart sector. Guarantees consist of on-off and standardised guarantees. Standardised guarantees reported within total guarantee amounts are guarantees that are usually issued in large numbers and for fairly small amounts, with similar features, for example mortgage loan guarantees, student loan guarantees etc. An estimate for expected loss can be made for standardised guarantees - this is expensed when the standardised guarantees are granted. The stock of guarantees are contingent liabilities and they are not part of general government gross debt.

Annual data on the structure of general government gross debt - Maastricht debt (ESA table 28A) is provided according to the European System of Accounts ESA 2010 Transmission Programme (Regulation (EU) No 549/2013, as amended by Regulation (EU) No 2023/734) and covers all EU Member States.

The majority of the 2330 collected data series are provided on a voluntary basis, but 36 series are to be provided on a compulsory basis (subject to derogations provided for in Commission implementing decision 2014/403).

The data is structured into the following data sets:

General government gross debt by subsector, by initial (original) maturity, by financial instrument and by sector of debt holder (gov_10dd_ggd): Containing data for general government and the subsectors by financial instrument, original maturity and counterpart sector. A breakdown for gross debt at variable interest is also included. A few series on transactions rather than balance sheets complement this collection.

• General government gross debt by currency of issuance (denomination) (gov_10dd_dcur): Containing data for general government and central government by currency denomination. It also includes the few series on transactions by currency denomination.
• Remaining maturity and average remaining maturity of debt (gov_10dd_rmd): The dataset contains data for general government and central government gross debt broken down by remaining maturity. Data for general government gross debt at variable interest by remaining maturity are also included here. The average remaining maturity of general government and central government gross debt is a part of this collection.
• Apparent cost of the debt (gov_10dd_acd): This may be defined as the interest rate applicable to the total Maastricht debt (AF.2, AF.3, and AF.4). It is a rate published on the basis of data supplied by compilers for general government and central government.
• Government guarantees (contingent liabilities) (gov_10dd_guar): Containing data for general government and central government on guarantees broken down by counterpart sector. Guarantees consist of on-off and standardised guarantees. Standardised guarantees reported within total guarantee amounts are guarantees that are usually issued in large numbers and for fairly small amounts, with similar features, for example mortgage loan guarantees, student loan guarantees etc. An estimate for expected loss can be made for standardised guarantees - this is expensed when the standardised guarantees are granted. The stock of guarantees are contingent liabilities and they are not part of general government gross debt.