Xtract.io's bank location data delivers a comprehensive geographical snapshot of the United States banking infrastructure. This dataset provides financial institutions, market researchers, and business strategists with granular insights into the distribution of top banks and their ATM networks. By mapping precise locations, organizations can analyze market penetration, identify potential expansion opportunities, and develop targeted marketing strategies. The data supports competitive intelligence, demographic studies, and strategic planning across the financial services landscape.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

-Retail -Restaurants -Healthcare -Automotive -Public utilities (e.g., ATMs, park-and-ride locations) -Shopping malls, and more

Why Choose LocationsXYZ? At LocationsXYZ, we: -Deliver POI data with 95% accuracy -Refresh POIs every 30, 60, or 90 days to ensure the most recent information -Create on-demand POI datasets tailored to your specific needs -Handcraft boundaries (geofences) for locations to enhance accuracy -Provide POI and polygon data in multiple file formats

Unlock the Power of POI Data With our point-of-interest data, you can: -Perform thorough market analyses -Identify the best locations for new stores -Gain insights into consumer behavior -Achieve an edge with competitive intelligence

LocationsXYZ has empowered businesses with geospatial insights, helping them scale and make informed decisions. Join our growing list of satisfied customers and unlock your business's potential with our cutting-edge POI data.

Commercial and Savings Banks Across US

FDIC: Commercial and Savings Banks Across US

By Finance [source]

About this dataset

This dataset from the Federal Deposit Insurance Corporation (FDIC) and Office of Thrift Supervision (OTS) contains deposit data for branches and offices of all FDIC-insured institutions in the United States as of June 30th, 2012. Featuring a wide range of detailed fields such as branch names, zip codes, total deposits, metropolitan statistical area and more—this dataset offers insight into the financial health and performance of US banks. With this data in hand, anyone with an interest in banking can gain knowledge about bank industry trends through time-tested figures associated with each institution. No matter what you're looking to learn about our nation's banks—from consolidated or non-consolidated status to office numbers or FDIC certificate numbers — this comprehensive summary is sure to give you valuable insight into the state of banking across America!

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How to use the dataset

This dataset provides information on the deposits of all FDIC-insured institutions as of June 30th, 2012. The data includes: branch name, institution name, street address, city/state/country, zip code, FDIC certificate number, total deposits in millions and total offices. It also includes the geographical coordinates of branches and offices.

Research Ideas

• Create a risk management system for FDIC-insured institutions by analyzing data regarding deposit trends and identifying areas of potential risk.
• Utilize geographic information of the branches and offices to create a visualization tool showing the spacial distribution of deposits per city, state, or metropolitan statistical area.
• Analyze branch name variables as they relate to total deposits across different banks and evaluate naming patterns that have been successful in driving increased amounts of deposits at certain locations or branches

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: ALL_2012.csv | Column name | Description | |:----------------------------------------|:---------------------------------------| | 2012 | Year of the deposit data (Integer) | | 19048 | FDIC Institution Number (Integer) | | 152 | Office Number (Integer) | | 286690 | Zip Code (Integer) | | Compass Bank | Name of the Bank (String) | | 3805 A1a South | Street Address (String) | | Saint Augustine | City (String) | | St. Johns | County (String) | | FL | State (String) | | 32080 | Zip Code (Integer) | | BRCENM | Branch Name (String) | | CONSOLD | Consolidated/Non-Consolidated (String) | | 11 | Number of Offices/Branches (Integer) | | 33,317 | Deposit Balances in Millions (Integer) | | Los Angeles-Long Beach-Glendale, CA | Metropolitan Statistical Area (String) | | Saint Augustine.1 | City (String) | | United States | Country (String) | | 109 | FDIC Region Number (Integer) | | 300 | Latitude (Integer) |

Acknowledgements

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

Graph and download economic data for Number of Bank Branches for United States (DDAI02USA643NWDB) from 2004 to 2019 about banks, depository institutions, and USA.

Comprehensive FDIC Public Data on U.S. Bank Institutions and Failures

Short Description

A rich dataset that provides detailed information about FDIC-insured bank institutions, their locations, and historical bank failures in the United States from 1934 to present.

Long Description

Overview

This dataset is compiled from public data provided by the Federal Deposit Insurance Corporation (FDIC). It offers a comprehensive look at FDIC-insured banking institutions, their various events, locations, and a historical account of bank failures in the United States from 1934 to the present day.

Data Files

See the dataset-metadata for file and column descriptions.

Potential Use-Cases

• Risk Assessment: Evaluate the risk factors associated with various banks.
• Historical Analysis: Perform a time-series analysis on bank failures.
• Geographical Insights: Investigate the distribution of bank institutions across different states or regions.
• Regulatory Compliance: Monitor banks that are FDIC insured for compliance with federal laws.

Update Schedule

This dataset is updated daily to reflect the most current data available from the FDIC.

Reporting Issues

If you encounter any issues or discrepancies with the dataset, please report them at our GitHub Issues Page.

Data Source

All data is sourced from the Federal Deposit Insurance Corporation (FDIC).

Licensing

This licensed apache-2.0. Please attribute the source when using this data.

Acknowledgements

We would like to thank the FDIC for making this data publicly available.

Comprehensive dataset containing 24,716 verified Food bank businesses in United States with complete contact information, ratings, reviews, and location data.

The estimated number of banks and thrifts in the United States fell from around ****** in 1920 to ****** in 1929, when the onset of the Great Depression would then see it fall further, below ****** in 1933. This marks a cumulative decline of over ****** banks and thrifts, which is equal to a drop of more than ** percent in 13 years. Tumultuous Twenties Despite the economic prosperity associated with the Roarin' 1920s in the U.S., it was a tumultuous decade in financial terms, with more separate recessions than any other decade. However, the ***** was also privy to frivolous lending policies among many banks, which saw the banking sector collapse in the wake of the Wall Street Crash in 1929. Many banks failed as the Great Depression and unemployment spread across the country, and customers or businesses could not afford to repay their loans. It was only after this financial crisis where the federal government began keeping more stringent and accurate records on its banking sector, therefore precise figures and the reasons behind these bank failures are not always clear. Franklin D. Roosevelt Just two days after assuming office in 1933, Franklin D. Roosevelt drastically declared a bank holiday, and all banks in the country were closed from ******* until ********. This break allowed Congress to pass the Emergency Banking Act on *******, which saw the Federal Reserve provide deposit insurance for all reopened banks thereafter. Through his first fireside chat, Roosevelt then encouraged Americans to re-deposit their money in the banks again, which successfully restored much of the public's faith in the banking system - it is estimated that over half of the cash withdrawn during the Great Depression was then returned to the banks by ********.

🏦 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...

This dataset, identified by the series ID RSXFS, is sourced from the U.S. Census Bureau and is available through the Federal Reserve Economic Data (FRED) system of the St. Louis Fed. It provides a monthly measure of retail sales across the United States. The data represents the total value of sales at retail and food services stores, measured in millions of dollars and adjusted for seasonal variations. It is important to note that the most recent month's value is an advance estimate, which is subject to revision in subsequent months as more comprehensive data becomes available. As a key economic indicator, this series is widely used by economists and analysts to gauge consumer spending and assess the overall health of the U.S. economy.

Suggested Use Cases: - This dataset is highly valuable for economic analysis and can be used to: - Conduct time series analysis and modeling. - Track consumer spending patterns. - Forecast future retail sales. - Analyze the impact of economic events on the retail sector.

License The RSXFS dataset is sourced from the U.S. Census Bureau and is considered Public Domain: Citation Requested. This means the data is freely available for use, but you must cite the source and acknowledge that the data was obtained from FRED. If you plan on using any copyrighted series from other data providers on FRED for commercial purposes, you would need to contact the original data owner for permission.

Data Fields: The dataset primarily contains two columns: - observation_date: The date of the monthly data point, recorded as the first day of each month from January 1992 to July 2025. - RSXFS: The value of advance retail sales in millions of dollars.

Citation and Provenance:
Source: U.S. Census Bureau
Release: Advance Monthly Sales for Retail and Food Services
FRED Link: https://fred.stlouisfed.org/series/RSXFS
Citation: U.S. Census Bureau, Advance Retail Sales: Retail Trade [RSXFS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/RSXFS, September 8, 2025.

Overview This dataset contains 5,000 meticulously generated banking transaction records from 2023 to 2024. It includes essential details such as transaction IDs, amounts, timestamps, payment methods, and customer demographics. Designed with realistic variability, it mimics real-world financial data to provide an authentic experience for financial analysis and machine learning applications.

Features A. Transaction Details: - Transaction_ID: Unique identifier for each transaction. - Transaction_Date: Date and time of the transaction. - Transaction_Amount: Monetary value of the transaction. - Transaction_Type: Type of transaction (Debit or Credit).

B. Customer Information: - Customer_Age: Age of the customer (18–70). - Customer_Gender: Gender of the customer (Male, Female, Others). - Customer_Income: Annual income of the customer. - Account_Balance: Account balance after the transaction.

C. Categorization: - Category: Categorized transactions into relevant sectors such as Food, Transport, Entertainment, Grocery, Electronics, and more.

D. Merchant and Payment Information: - Merchant_Name: The name of the merchant or vendor. - Payment_Method: Method of payment (Credit Card, Debit Card, Cash, Online Transfer, etc.).

E. Additional Details: - City: Location of the transaction (major US cities). - Fraud_Flag: Indicates whether the transaction is flagged as fraudulent. - Transaction_Status: Status of the transaction (Success, Failed, Pending). - Loyalty_Points_Earned: Rewards points earned from the transaction. - Discount_Applied: Boolean indicating if a discount was applied.

Over the course of the 1920s, the value of money deposited in commercial banks grew at a fairly steady rate, rising from around 19 billion U.S. dollars in 1921 (the initial dip was due to the post-WWI recession), to 25 billion at the end of the decade. However, the onset of the Great Depression saw these figures drop drastically, and the value of deposits fell from around 26 to 16 billion dollars between 1930 and 1933. This was not only due to high unemployment and lower wages, but many Americans also lost faith in the banks during the Depression - many blamed the banks for the Depression as frivolous lending practices had contributed to the Wall Street Crash; banks demanded early repayment of debts and often repossessed the property of those who could not afford to do so (also leading to evictions), and many banks failed after the Crash and were not perceived as safe. It was not until 1936 where deposits in commercial banks returned to their pre-Depression levels, after the Roosevelt administration put a number of safeguards in place and helped restore public faith in the American banking system.

In contrast to commercial banks, the total amount of money deposited in savings accounts continued to rise throughout the Great Depression, albeit at a much slower rate than in the 1920s. The reason for continued increase was due to the disproportionate impact the Depression had across socioeconomic groups - most working and middle-class Americans did not have the means to have a savings account

Topographic data were collected along six reaches (study reach 1, study reach 2, study reach 3, study reach 4, study reach 5, and study reach 6) along Caulks Creek in Wildwood, Missouri, on multiple dates, using terrestrial light detection and ranging (t-lidar), Global Navigation Satellite System (GNSS), and conventional surveying techniques (Rydlund and Densmore, 2012).These data are high-resolution topography in laser scan format (LAS), collected using a tripod mounted t-lidar at multiple scan setups. Data collection software was used to integrate and store the range and angular measurements from the t-lidar equipment. Computer software was used to process the raw data, align the various scans in reference to one another, classify the data, and extract the topography data in a useable format. The total station data were collected for study reach 3 using a tripod mounted Trimble M3 Total Station are stored in comma-separated value (csv) format. The collected data points represent the channel, bank, and near overbank surface at select locations in the study reach.The t-lidar and total station topographic data are available for each study reach within the data release Child Items. Bank erosion pins (2-foot lengths of 0.38-inch steel rebar) were placed at twelve locations throughout the Caulks Creek study area. Most of the bank erosion pins were located outside of the six study reaches, though one was located within study reach 4 and two were located within study reach 5. The tip of the pin represents a datum from which a change in the bank position can be measured. The distance from the tip of the pin to the bank face was measured on the top, bottom, upstream side, and downstream side of the pin, and these measurements were averaged to obtain a final measurement value. The bank pins were measured six times between February 2022 and July 2023 including installation and removal. The data are provided in (csv) format in the Bank Erosion Pin Child Item. References Cited: Rydlund, P.H., Jr., and Densmore, B.K., 2012, Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey: U.S. Geological Survey Techniques and Methods, book 11, chap. D1, 102 p. with appendixes, https://doi.org/10.3133/tm11D1.

Graph and download economic data for Deposits, All Commercial Banks (DPSACBW027SBOG) from 1973-01-03 to 2025-11-19 about deposits, banks, depository institutions, and USA.

Graph and download economic data for Commercial Banks in the U.S. (DISCONTINUED) (USNUM) from Q1 1984 to Q3 2020 about commercial, banks, depository institutions, and USA.

Money Supply M2 in the United States increased to 22298.10 USD Billion in October from 22212.50 USD Billion in September of 2025. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Modern capital requirements can appear to be overly complex, but they reflect centuries of practical experience, compromises between different regulators, and legal and financial systems that developed over time. This Commentary provides a historical perspective on current discussions of capital requirements by looking at how the understanding of bank capital and the regulations regarding its use have changed over time.

Graph and download economic data for 5-Bank Asset Concentration for United States (DDOI06USA156NWDB) from 2000 to 2021 about assets, banks, depository institutions, and USA.

Content

This report lists each failure of a commercial bank, savings association, and savings bank since the establishment of the FDIC in 1933. Each record includes the institution name and FIN number, institution and charter types, location of headquarters (city and state), effective date, insurance fund and certificate number, failure transaction type, total deposits and total assets last reported prior to failure (in thousands of dollars), and the estimated cost of resolution. Data on estimated losses are not available for FDIC insured failures prior to 1986 or for FSLIC insured failures from 1934-88.

Acknowledgements

The bank failure report was downloaded from the FDIC website.

Inspiration

What type of banking institution is the most likely to fail? How have bank failure rates changed over time? What commercial bank failure cost the federal government the most to resolve?

These data are depth contours (isobaths) derived at 50 meters for most islands and reefs in the Mariana Archipelago, American Samoa, and the Pacific Remote Island Areas. Contours at 10- or 20-meter depths have also been derived for a subset of the same locations. These contours are derived from bathymetry sources including multibeam data collected by Coral Reef Ecosystem Program (CREP) at the NOAA Pacific Islands Fisheries Science Center (PIFSC) since 2003, NOAA nautical charts, estimated depths derived from satellite images, and other sources of bathymetry collected by various agencies and entities.

Bank erosion pins (2-foot lengths of 0.38-inch steel rebar) were placed at 12 locations throughout the Caulks Creek study area in Wildwood, Missouri. Most of the bank erosion pins were located outside of the six study reaches, though one was located within study reach 4 and two were located within study reach 5. The bank erosion pin locations were largely determined by site access and the feasibility of inserting the pin into the bank face and are not intended to be a statistically representative sampling of the channel. The tip of the pin represents a datum from which a change in the bank position can be measured. The distance from the tip of the pin to the bank face was measured on the top, bottom, upstream side, and downstream side of the pin, and these measurements were averaged to obtain a final measurement value. The bank pins were measured six times between February 2022 and July 2023 including during installation and removal. The data are provided in comma-separated value (csv) format.

In the second quarter of 2025, TD Bank's U.S. operations distinguished itself with the highest common equity tier 1 (CET1) capital ratio among major U.S. banks by total assets. The bank's CET1 ratio of 17.38 percent significantly surpassed the regulatory minimum of 4.5 percent. By comparison, JPMorgan Chase, the largest U.S. bank, recorded a CET1 ratio of 15.08 percent during the same period. What is CET1 capital ratio? The Basel III framework, established by the Basel Committee on Banking Supervision, sets international standards for bank capital requirements to ensure global financial stability. Developed in response to the 2007-2009 financial crisis, these regulations require banks to maintain adequate capital to withstand unexpected losses and economic downturns. The framework mandates a total capital requirement of eight percent of risk-weighted assets, with Common Equity Tier 1 (CET1)—the highest quality capital—comprising at least 4.5 percent of that total. In 2024, JPMorgan Chase had the highest Tier 1 capital among all banks in the United States. Worldwide Tier 1 capital levels of banks JPMorgan Chase, while leading U.S. banks in Tier 1 capital, ranked fifth globally in 2024. Four Chinese banks outperformed it: Industrial and Commercial Bank of China (ICBC), China Construction Bank, Agricultural Bank of China, and Bank of China. Among these, ICBC emerged as the world's top bank in Tier 1 capital.