Open Data Library

CSEC, derived from CSDB, provides key stats on debt securities and listed shares in the euro area and non-euro EU states. It includes data on outstanding stocks, gross issues, redemptions, net issues, and revaluations. Breakdowns cover issuer sector, issuance maturity, coupon type, and currency. The dataset introduces sustainability indicators (green, social, etc.) for the euro area and EU. It spans debt securities and listed shares with ISIN codes from EU residents. Sectors include the total economy, corporations, central banks, funds, insurance, pension funds, and others. Time coverage starts December 2020, with monthly data following the ESA 2010 system.

This dataset contains statistics on the African continent, specific to Agriculture, Debt, Economic, Energy, Financial Flows, Governance, ICT, Infrastructure, National Accounts, Prices, Production, Public finance, Socio economic data. This data is updated annually, and covers periods between 1960 and 2023

This dataset contains transactions data from normal and phishing accounts, de-anonymized for analytics in Ethereum fraud detection

The dataset involves predicting online transaction fraud (isFraud) through two linked files, identity and transaction, joined by TransactionID, containing categorical features like ProductCD, card details, and addresses. Some transactions lack associated identity information. The dataset includes training (train_{transaction, identity}.csv) and test sets (test_{transaction, identity}.csv) for model development and predictions, respectively.

This dataset contains values of total gross loans, non-performing loans, and net non-performing loans in the Philippines' banking system. This dataset features values from 1964 to 2023, updated on a quarterly basis.

This dataset contains the consolidation of the balance sheets of the Bangko Sentral ng Pilipinas (BSP) and the Deposit Money Banks (DMBs), used to assess system liquidity and discerning sources of money growth; provide vital macroeconomic policy-oriented monetary statistics; and contribute inputs for early warning system models pertaining to the macroeconomy. This dataset is updated monthly

This dataset is used alongside the "Depository Corporations Survey - Financial System Accounts" dataset, to asess system liquidity and discerning sources of money growth; provide vital macroeconomic policy-oriented monetary statistics; and contribute inputs for early warning system models pertaining to the macroeconomy. This dataset is updated monthly

" This dataset encompasses the Domestic Consolidated Statements (DCS), consolidating the balance sheets of the Bangko Sentral ng Pilipinas (BSP) and Other Depository Corporations (ODCs). It serves three main purposes: firstly, assessing system liquidity and discerning sources of money growth; secondly, providing vital macroeconomic policy-oriented monetary statistics; and thirdly, contributing inputs for early warning system models pertaining to the macroeconomy. This dataset is updated on a monthly basis"

The dataset includes network information pertaining to financial institutions under BSP supervision, detailing the count of offices for BSP-supervised entities.

This dataset contains Reserve Requirement ratios of Commercial Banks, Thrift Banks, Rural Banks, Non-Banks with Quasi-Banking Functions (NBQBs), and Digital Banks, against peso deposits and other deposit substitutes in the Philippines.

This dataset contains outstanding loans, denoting the unpaid loans provided by universal and commercial banks. The dataset differentiates between loans for production purposes and those for household consumption.

This dataset contains credit card receivables from the Philippine Banking system including universal/ commercial banks, and thrift banks. The dataset is provided in both in static and interactive formats

This dataset contains value and volume of transactions in the Philippine payment & settlement system plus. It features data such as customer/ corporate and government payments, FI to FI payments, intra account transfer, government collections and payments, transactions with BPS units, through FMIs and CSOs. This dataset is updated quarterly

This dataset contains loan accounts data from the Philippines - inclusive of historical and current data

This dataset provides an overview of complaints reported on financial services firms across the UK, covering motor & transport, credit cards, property, and savings products among others.

The Monetary Statistics - Other Financial Corporations Survey contains data from financial institutions and investment funds not considered as broad money issuers. It encompasses information on assets and liabilities of entities like pension funds, insurance corporations, and capitalization societies. The dataset provides end-of-period balances for assets and liabilities, following international standards to represent key groups of these entities' financial positions. The source of the data is the SGS (Monetary and Financial Statistics) with series categorized under Monetary Statistics, Financial and Monetary Accounts Statistics, and the Other Financial Corporations Survey.

The Monetary Statistics - Banco Central do Brasil Survey focuses on the monetary base, encompassing Central Bank monetary liabilities, including currency in circulation and bank reserves reflecting money substitutes and financial assets. The dataset covers end-of-period balances for the monetary base and broad monetary base, along with assets and liabilities of the monetary authority survey. Series are classified under Monetary Statistics, Monetary Aggregates, and Financial and Monetary Accounts Statistics, sourced from SGS (Monetary and Financial Statistics). Data is captured in local currency

The Monetary Statistics – Depository Corporations Survey consolidates data from units classified as broad money issuers, including the Central Bank and Other Depository Corporations (ODC). Broad money components include M1, M2, M3, and M4, each encompassing various financial instruments. The Other Depository Corporations (ODC) category includes diverse entities such as commercial banks, credit unions, and money market funds, excluding exchange banks categorized under Other Financial Corporations. he dataset covers end-of-period balances for broad money components, assets, and liabilities. Series are classified under Monetary Statistics, Monetary Aggregates, and Financial and Monetary Accounts Statistics, with the source being SGS (Monetary and Financial Statistics). Data is represented in local currency.

The dataset contains quarterly foreign currency flows from institutions with foreign exchange operations. Currency flows refer to contracted and non-invalidated foreign exchange operations, excluding cancellations and write-offs and including write-off reversals.

This dataset contains data on assets, liabilities, and incomes of financial institutions operating in Brazil. This dataset is updated annually

This payments (RTGS) dataset is updated weekly by the central bank of Peru. The data comprises various financial transaction metrics, including payments through RTGS, Multibank Security Settlement System, and Electronic Clearing House, covering transfers in domestic and foreign currency, check transactions, and credit transfers in both domestic and foreign currencies. Additionally, it includes data on cashless payment instruments, ATMs, and virtual banking operations, both in terms of value and volume, for domestic and foreign currency transactions

This payments (RTGS) dataset is updated weekly by the central bank of Peru. The data comprises various financial transaction metrics, including payments through RTGS, Multibank Security Settlement System, and Electronic Clearing House, covering transfers in domestic and foreign currency, check transactions, and credit transfers in both domestic and foreign currencies. Additionally, it includes data on cashless payment instruments, ATMs, and virtual banking operations, both in terms of value and volume, for domestic and foreign currency transactions

This payments (RTGS) dataset is updated weekly by the central bank of Peru. The data comprises various financial transaction metrics, including payments through RTGS, Multibank Security Settlement System, and Electronic Clearing House, covering transfers in domestic and foreign currency, check transactions, and credit transfers in both domestic and foreign currencies. Additionally, it includes data on cashless payment instruments, ATMs, and virtual banking operations, both in terms of value and volume, for domestic and foreign currency transactions

This payments (RTGS) dataset is updated weekly by the central bank of Peru. The data comprises various financial transaction metrics, including payments through RTGS, Multibank Security Settlement System, and Electronic Clearing House, covering transfers in domestic and foreign currency, check transactions, and credit transfers in both domestic and foreign currencies. Additionally, it includes data on cashless payment instruments, ATMs, and virtual banking operations, both in terms of value and volume, for domestic and foreign currency transactions

This payments (RTGS) dataset is updated weekly by the central bank of Peru. The data comprises various financial transaction metrics, including payments through RTGS, Multibank Security Settlement System, and Electronic Clearing House, covering transfers in domestic and foreign currency, check transactions, and credit transfers in both domestic and foreign currencies. Additionally, it includes data on cashless payment instruments, ATMs, and virtual banking operations, both in terms of value and volume, for domestic and foreign currency transactions

This payments (RTGS) dataset is updated weekly by the central bank of Peru. The data comprises various financial transaction metrics, including payments through RTGS, Multibank Security Settlement System, and Electronic Clearing House, covering transfers in domestic and foreign currency, check transactions, and credit transfers in both domestic and foreign currencies. Additionally, it includes data on cashless payment instruments, ATMs, and virtual banking operations, both in terms of value and volume, for domestic and foreign currency transactions

This dataset contains social media data from September 2021, captured in a twitter scrape focussing on four South African Banks

This payments (RTGS) dataset is updated weekly by the central bank of Peru. The data comprises various financial transaction metrics, including payments through RTGS, Multibank Security Settlement System, and Electronic Clearing House, covering transfers in domestic and foreign currency, check transactions, and credit transfers in both domestic and foreign currencies. Additionally, it includes data on cashless payment instruments, ATMs, and virtual banking operations, both in terms of value and volume, for domestic and foreign currency transactions

This payments (RTGS) dataset is updated weekly by the central bank of Peru. The data comprises various financial transaction metrics, including payments through RTGS, Multibank Security Settlement System, and Electronic Clearing House, covering transfers in domestic and foreign currency, check transactions, and credit transfers in both domestic and foreign currencies. Additionally, it includes data on cashless payment instruments, ATMs, and virtual banking operations, both in terms of value and volume, for domestic and foreign currency transactions

This payments (RTGS) dataset is updated weekly by the central bank of Peru. The data comprises various financial transaction metrics, including payments through RTGS, Multibank Security Settlement System, and Electronic Clearing House, covering transfers in domestic and foreign currency, check transactions, and credit transfers in both domestic and foreign currencies. Additionally, it includes data on cashless payment instruments, ATMs, and virtual banking operations, both in terms of value and volume, for domestic and foreign currency transactions

FRASER is a digital library of U.S. economic, financial, and banking history—particularly the history of the Federal Reserve System.Processed through (OCR) software to provide machine-readable text and to assist in full-text searching. Strict scanning and image correction standards are followed to ensure the best possible OCR results, but the resulting text or data are kept as they are and are not corrected or modified in any way. Therefore, users should check any text or data copied directly from a PDF for inaccuracies.

These data contain information on the aggregate consolidated profitability balance sheets asset quality liquidity funding capital adequacy solvency of EU banks and refer to all EU Member States. The banks are divided into three size groups: small, medium-sized and large. Information on foreign-controlled institutions active in EU countries is also provided.

The dataset contains the balance of payments (b.o.p.) and the international investment position (i.i.p.) of the euro area compiled by the ECB, as well as European union aggregates compiled by Eurostat and individual EU country data. The b.o.p. is a statistical statement that summarises, for a specific period of time, the economic transactions of an economy with the rest of the world. The different accounts within the b.o.p. are distinguished according to the nature of the economic resources (e.g. goods, services, income or financial resources) provided and received. The i.i.p. is a statistical statement that shows, at a specific point in time, the value and composition of financial assets of residents of an economy that are claims on non-residents and gold bullion held as reserve assets and liabilities of residents of an economy to non-residents; The current account and capital account main components are broken down by counterpart countries. Financial transactions and positions are presented by type of investment, resident and counterpart sector, type of instrument and country of residency of the counterpart

The Bank Lending Survey (BLS) conducted by the Eurosystem is a valuable tool for assessing and understanding bank lending conditions in the euro area. The survey collects information from senior loan officers of a representative sample of euro area banks, providing insights into various aspects of lending policies and practices. Here's how the BLS data and its presentation are utilized in enhancing the Eurosystem's knowledge and influencing monetary policy decisions: Assessment of Monetary and Economic Developments: The BLS serves as input for the ECB Governing Council's assessment of monetary and economic developments. By analysing the survey results, the Council gains insights into the lending conditions in the euro area, which is crucial for formulating effective monetary policy. Loan Supply and Demand Information: The BLS complements existing statistics on loans and bank lending rates by providing additional information on loan supply and demand for enterprises and households. This comprehensive data helps in understanding the dynamics of credit in the economy. Credit Standards and Terms: The survey addresses critical issues such as credit standards for approving loans and the terms and conditions applied to new loans for enterprises and households. This information is essential for assessing the accessibility of credit and the conditions under which it is provided. Frequency and Sample Group: Conducted four times a year, the BLS captures the evolving trends in lending conditions. The sample group of around 150 entities includes banks from all euro area countries, ensuring a comprehensive representation of the diverse banking structures across the region. Net Percentage and Diffusion Index: The analysis of the aggregate BLS results focuses on the "net percentage," which measures the difference between the share of banks tightening credit standards and those easing them. The "net demand" assesses the difference between increased and decreased loan demand. These metrics provide a concise summary of the overall lending environment. Weighted Difference (Diffusion Index): The weighted difference, or diffusion index, provides a nuanced understanding of the survey results. It takes into account the intensity of responses by assigning different weights to responses indicating a considerable tightening or easing of credit standards. Reference Area and Sector Coverage: The survey covers all euro area member states and focuses on Monetary Financial Institutions (MFIs), ensuring a broad representation of the entire euro area banking sector. Policy Implications: The findings from the BLS guide the ECB Governing Council in making informed decisions on monetary policy. Changes in credit standards, loan demand, and lending conditions can influence the direction of monetary policy measures.

Banknotes statistics refer to data on outstanding amounts (stocks) and transactions (flows) of euro banknotes and coins, divided into: issued into circulation, returned from circulation, sorted to check authenticity & fitness and sorted to unfit during the reporting period. The dataset includes also data on collector coins issued net of collector coins returned. Data on the number of NCBs and commercial banks branches which provides cash related services are also included.

The Annual Macro-Economic Data (AMECO) is compiled by DG-ECFIN in the European Commission. It contains annual macro-economic time series submitted by the Member States and “filtered” by the Commission. These data are used in the forecast exercises done two times per year (Spring and Autumn). It is the official source for some variables, especially those related to public finances.

Tier by tier bank performance ratios all FDIC-insured institutions for each quarter back to 1984

Aggregate loans data for all FDIC-insured institutions for each quarter back to 1984

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

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

The database contains complaints data received from consumers. Complaints are only published after a company responds via CFPB, confirming a commercial relationship or after 15 days, whichever comes first. The database does not include complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets. The database only includes a consumer’s description of what happened in their own words. The database generally updates daily.