The Banking Bureau of the Department of Insurance Securities and Banking (DISB) regulates District of Columbia Chartered Banks, mortgage companies, and consumer finance companies. The Bureau strives to ensure a sound and thriving financial services community that provides the products, credit, and capital vital to the needs of District of Columbia residents and businesses. DISB charters and regulates District of Columbia banks and other DC depository financial institutions. DISB also regulates non-depository financial institutions such as mortgage lenders and brokers, money transmitters, consumer finance companies, and check cashers. The data is updated as needed.

These data are derived from returns submitted to the Australian Prudential Regulation Authority (APRA) by banks authorised under the Banking Act 1959. APRA assumed responsibility for the supervision and regulation of banks on 1 July 1998. Data prior to that date were submitted to the RBA.

Up to and including June 2000, data are averages of weekly (Wednesday) figures. From July 2000, data are for the last business day of every month. Up to and including March 2002, banks submitted Form D (Statement of Liabilities and Assets on the Australian Books). In March 2002, APRA implemented new reporting forms for banks. The data, dating from April 2002, are derived from ARF 320.0 Statement of Financial Position (Domestic Books).

ARF 320.0 covers the domestic books of the licensed bank and is an unconsolidated report of the Australian bank’s operations/transactions that are booked or recorded inside Australia (with Australian residents and non-residents). ARF 320.0 does not consolidate Australian and offshore-controlled entities (thus offshore branches of the Australian bank are excluded). ARF 320.0 includes transactions of Australian-based offshore banking units of the licensed ADI but excludes transactions of overseas-based offshore banking units.

An Australian ‘resident’ is any individual, business or other organisation domiciled in Australia. Australian branches and subsidiaries of foreign businesses are regarded as Australian residents. A ‘non-resident’ is any individual, business or other organisation domiciled overseas. Foreign branches and subsidiaries of Australian businesses are regarded as non-residents.

‘Resident assets – notes and coins, and deposits due from RBA’ includes: Australian and foreign currency notes and coins; settlement account balances with the RBA and any other central bank; and any other funds held at the RBA.

‘Resident assets – bills receivables’ refers to assets arising from undertakings by customers to pay bills of exchange drawn by the banks. From April 2002, this item includes Australian dollar- and foreign currency-denominated (AUD equivalent) bill receivables. Prior to that date, foreign currency-denominated (AUD equivalent) bill receivables are included in ‘resident assets – other assets’.

‘Resident assets – loans and advances – residential’ include: owner-occupied and investment housing loans. ‘Resident assets – loans and advances – personal’ include: revolving credit; credit cards; personal lease financing; and other personal term loans. ‘Resident assets – loans and advances – commercial’ include: loans to community service organisations and non-profit institutions; loans to non-financial corporations; loans to general government; and loans to financial corporations. The loans and advances data are net of specific provisions for bad and doubtful debts, but gross of general provisions for bad and doubtful debts. Loans and advances exclude: bills of exchange, commercial paper, promissory notes, certificates of deposit, and some other debt securities. From April 2002, loans and advances refer to Australian dollar- and foreign currency-denominated (AUD equivalent) loans and advances. Prior to that date, foreign currency-denominated (AUD equivalent) loans and advances are included in ‘resident assets – other assets’.

‘Resident assets – other assets’ refers to all other resident assets not included in the above items. Prior to April 2002, this item includes: shares; bullion; past-due bills; accounts receivable; prepayments made; public sector securities; and all other resident assets other than accrued interest not yet receivable and intangible assets. From April 2002, this item includes: cash and liquid assets other than notes and coins and deposits due from RBA; trading and investment securities; fixed assets; intangible assets; other investments and all other assets not reported above. Note that, from April 2002, this item also includes unrealised gains on trading derivatives – prior to that date, these were excluded.

‘Resident assets – total’ refers to total assets on the Australian books of banks that are due from residents, and is the sum of the above items. ‘Resident assets – of which: denominated in foreign currency’ refers to the Australian dollar equivalent of ‘resident assets – total’ on the Australian books of banks that are denominated in foreign currency.

‘Non-resident assets – total’ refers to total assets on the Australian books of banks that are due from non-residents, though from April 2002, this series excludes the total amount due from banks’ overseas operations, which have been separately identified on the new reporting form. ‘Non-resident assets – of which: denominated in foreign currency’ refers to the Australian dollar equivalent of ‘non-resident assets – total’ on the Australian books of banks that are denominated in foreign currency.

‘Total assets’ is the sum of ‘resident assets – total’ and ‘non-resident assets – total’. From April 2002, this item also includes the ‘amount due from overseas operations’, which is identified separately from ‘resident assets – total’ and ‘non-resident assets – total’. The ‘amount due from overseas operations’ refers to domestic book on-balance sheet assets due from overseas operations of banks which have not been included in the above items.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

About this dataset

Context

The dataset tabulates the Banks population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Banks across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Banks was 80, a 1.23% decrease year-by-year from 2021. Previously, in 2021, Banks population was 81, a decline of 2.41% compared to a population of 83 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Banks decreased by 38. In this period, the peak population was 122 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Banks is shown in this column.
• Year on Year Change: This column displays the change in Banks population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Banks Population by Year. You can refer the same here

Concept: Number of correspondents of banks in Brazil Source: Information System on Entities Related to the BCB 24945-number-of-correspondents-of-banks-in-brazil 24945-number-of-correspondents-of-banks-in-brazil

Analysis of ‘🍐 FDIC Failed Bank List’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/fdic-failed-bank-liste on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

The FDIC is often appointed as receiver for failed banks. This list includes banks which have failed since October 1, 2000.

Source: https://catalog.data.gov/dataset/fdic-failed-bank-list

This dataset was created by Finance and contains around 500 samples along with Acquiring Institution, Bank Name, technical information and other features such as: - Updated Date - St - and more.

How to use this dataset

• Analyze Closing Date in relation to City
• Study the influence of Acquiring Institution on Bank Name
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Finance

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Context

A retail bank would like to hire you to build a credit default model for their credit card portfolio. The bank expects the model to identify the consumers who are likely to default on their credit card payments over the next 12 months. This model will be used to reduce the bank’s future losses. The bank is willing to provide you with some sample datathat they can currently extract from their systems. This data set (credit_data.csv) consists of 13,444 observations with 14 variables.

Content

Based on the bank’s experience, the number of derogatory reports is a strong indicator of default. This is all that the information you are able to get from the bank at the moment. Currently, they do not have the expertise to provide any clarification on this data and are also unsure about other variables captured by their systems

About this dataset

Context

The dataset tabulates the Banks population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Banks. The dataset can be utilized to understand the population distribution of Banks by age. For example, using this dataset, we can identify the largest age group in Banks.

Key observations

The largest age group in Banks, AL was for the group of age 20 to 24 years years with a population of 76 (25.50%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Banks, AL was the 65 to 69 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Banks is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Banks total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Banks Population by Age. You can refer the same here

Overview This dataset contains 45,000 records of loan applicants, with various attributes related to personal demographics, financial status, and loan details. The dataset can be used for predictive modeling, particularly in credit risk assessment and loan default prediction.

Dataset Content The dataset includes 14 columns representing different factors influencing loan approvals and defaults:

Personal Information

person_age: Age of the applicant (in years). person_gender: Gender of the applicant (male, female). person_education: Educational background (High School, Bachelor, Master, etc.). person_income: Annual income of the applicant (in USD). person_emp_exp: Years of employment experience. person_home_ownership: Type of home ownership (RENT, OWN, MORTGAGE). Loan Details

loan_amnt: Loan amount requested (in USD). loan_intent: Purpose of the loan (PERSONAL, EDUCATION, MEDICAL, etc.). loan_int_rate: Interest rate on the loan (percentage). loan_percent_income: Ratio of loan amount to income. Credit & Loan History

cb_person_cred_hist_length: Length of the applicant's credit history (in years). credit_score: Credit score of the applicant. previous_loan_defaults_on_file: Whether the applicant has previous loan defaults (Yes or No). Target Variable

loan_status: 1 if the loan was repaid successfully, 0 if the applicant defaulted. Use Cases Loan Default Prediction: Build a classification model to predict loan repayment. Credit Risk Analysis: Analyze the relationship between income, credit score, and loan defaults. Feature Engineering: Extract new insights from employment history, home ownership, and loan amounts. Acknowledgments This dataset is synthetic and designed for machine learning and financial risk analysis.

The dataset shows Quarter wise International Liabilities and Claims of Banks Classified According to the Country of Incorporation of Banks-All Sector, Position with respect to Banks

United Kingdom: Excludes Guernsey, Isle of Man, and Jersey United States of America: Includes Midway Island and Wake Island

Note: 1. The sum of components may not add up due to rounding off. 2. Q1, Q2, Q3 and Q4 denote quarters ended March, June, September and December, respectively. 3. Based on the latest BIS guidelines, MTM derivatives have been introduced in this statement from September 2022 quarter 4. Based on Locational Banking Statistics(LBS)

Banks Balance Sheet in Taiwan increased to 65425955 TWD Million in May from 64584198 TWD Million in April of 2025. This dataset provides - Taiwan Banks Balance Sheet - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Banks. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Banks. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Banks, where there exist only two delineated age groups, the median household income is $61,250 for householders within the 25 to 44 years age group, compared to $59,000 for the 45 to 64 years age group.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Banks median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the Banks population by age. The dataset can be utilized to understand the age distribution and demographics of Banks.

Content

The dataset constitues the following three datasets

• Banks, OR Age Group Population Dataset: A complete breakdown of Banks age demographics from 0 to 85 years, distributed across 18 age groups
• Banks, OR Age Cohorts Dataset: Children, Working Adults, and Seniors in Banks - Population and Percentage Analysis
• Banks, OR Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

The dataset shows Quarter wise International Liabilities/Claims of Banks Classified According to the Currency and Sector

Note: 1. The sum of components may not add up due to rounding off. 2. Q1, Q2, Q3 and Q4 denote quarters ended March, June, September and December, respectively. 3. Based on the latest BIS guidelines, MTM derivatives have been introduced in this statement from September 2022 quarter 4. Based on Locational Banking Statistics(LBS)

Analysis of ‘Overall Loan Payments by banks by year to 2006’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-data-usmart-io-org-ae1d5c14-c392-4c3f-9705-537427eeb413-dataset-viewdiscovery-datasetguid-1dd5873e-3abf-4604-9961-da2376b49d9b on 14 January 2022.

--- Dataset description provided by original source is as follows ---

Source: From lending institutions This data contains an unquantified element of refinancing of existing mortgages (e.g. involving the redemption of an existing Mortgage and its replacement with a Mortgage from a different lender). This data is not directly comparable with post 2007 data from IBF The most current data is published on these Sheets. Previously published data may be subject to revision. Any change from the Originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

--- Original source retains full ownership of the source dataset ---

Many diagnostic datasets suffer from the adverse effects of spikes that are embedded in data and noise. For example, this is true for electrical power system data where the switches, relays, and inverters are major contributors to these effects. Spikes are mostly harmful to the analysis of data in that they throw off real-time detection of abnormal conditions, and classification of faults. Since noise and spikes are mixed together and embedded within the data, removal of the unwanted signals from the data is not always easy and may result in losing the integrity of the information carried by the data. Additionally, in some applications noise and spikes need to be filtered independently. The proposed algorithm is a multi-resolution filtering approach based on Haar wavelets that is capable of removing spikes while incurring insignificant damage to other data. In particular, noise in the data, which is a useful indicator that a sensor is healthy and not stuck, can be preserved using our approach. Presented here is the theoretical background with some examples from a realistic testbed.

Coups d'Ètat are important events in the life of a country. They constitute an important subset of irregular transfers of political power that can have significant and enduring consequences for national well-being. There are only a limited number of datasets available to study these events (Powell and Thyne 2011, Marshall and Marshall 2019). Seeking to facilitate research on post-WWII coups by compiling a more comprehensive list and categorization of these events, the Cline Center for Advanced Social Research (previously the Cline Center for Democracy) initiated the Coup d’État Project as part of its Societal Infrastructures and Development (SID) project. More specifically, this dataset identifies the outcomes of coup events (i.e., realized, unrealized, or conspiracy) the type of actor(s) who initiated the coup (i.e., military, rebels, etc.), as well as the fate of the deposed leader. Version 2.1.3 adds 19 additional coup events to the data set, corrects the date of a coup in Tunisia, and reclassifies an attempted coup in Brazil in December 2022 to a conspiracy. Version 2.1.2 added 6 additional coup events that occurred in 2022 and updated the coding of an attempted coup event in Kazakhstan in January 2022. Version 2.1.1 corrected a mistake in version 2.1.0, where the designation of “dissident coup” had been dropped in error for coup_id: 00201062021. Version 2.1.1 fixed this omission by marking the case as both a dissident coup and an auto-coup. Version 2.1.0 added 36 cases to the data set and removed two cases from the v2.0.0 data. This update also added actor coding for 46 coup events and added executive outcomes to 18 events from version 2.0.0. A few other changes were made to correct inconsistencies in the coup ID variable and the date of the event. Version 2.0.0 improved several aspects of the previous version (v1.0.0) and incorporated additional source material to include: • Reconciling missing event data • Removing events with irreconcilable event dates • Removing events with insufficient sourcing (each event needs at least two sources) • Removing events that were inaccurately coded as coup events • Removing variables that fell below the threshold of inter-coder reliability required by the project • Removing the spreadsheet ‘CoupInventory.xls’ because of inadequate attribution and citations in the event summaries • Extending the period covered from 1945-2005 to 1945-2019 • Adding events from Powell and Thyne’s Coup Data (Powell and Thyne, 2011)
Items in this Dataset 1. Cline Center Coup d'État Codebook v.2.1.3 Codebook.pdf - This 15-page document describes the Cline Center Coup d’État Project dataset. The first section of this codebook provides a summary of the different versions of the data. The second section provides a succinct definition of a coup d’état used by the Coup d'État Project and an overview of the categories used to differentiate the wide array of events that meet the project's definition. It also defines coup outcomes. The third section describes the methodology used to produce the data. Revised February 2024 2. Coup Data v2.1.3.csv - This CSV (Comma Separated Values) file contains all of the coup event data from the Cline Center Coup d’État Project. It contains 29 variables and 1000 observations. Revised February 2024 3. Source Document v2.1.3.pdf - This 325-page document provides the sources used for each of the coup events identified in this dataset. Please use the value in the coup_id variable to identify the sources used to identify that particular event. Revised February 2024 4. README.md - This file contains useful information for the user about the dataset. It is a text file written in markdown language. Revised February 2024
Citation Guidelines 1. To cite the codebook (or any other documentation associated with the Cline Center Coup d’État Project Dataset) please use the following citation: Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Scott Althaus. 2024. “Cline Center Coup d’État Project Dataset Codebook”. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7 2. To cite data from the Cline Center Coup d’État Project Dataset please use the following citation (filling in the correct date of access): Peyton, Buddy, Joseph Bajjalieh, Dan Shalmon, Michael Martin, Jonathan Bonaguro, and Emilio Soto. 2024. Cline Center Coup d’État Project Dataset. Cline Center for Advanced Social Research. V.2.1.3. February 27. University of Illinois Urbana-Champaign. doi: 10.13012/B2IDB-9651987_V7

Banks Balance Sheet in Poland increased to 3627225.10 PLN Million in May from 3606936.10 PLN Million in April of 2025. This dataset provides the latest reported value for - Poland Banks Balance Sheet - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Banks Balance Sheet in Kazakhstan increased to 63363783443 KZT Thousand in June from 62615847217 KZT Thousand in May of 2025. This dataset provides - Kazakhstan Central Bank Balance Sheet - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Banks Balance Sheet in Finland increased to 181356 EUR Million in May from 181213 EUR Million in April of 2025. This dataset provides the latest reported value for - Finland Banks Balance Sheet - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.