This dataset provides a structured and machine-readable collection of financial statements filed with the Companies Registration Office (CRO) in Ireland. It currently includes financial statements for the year 2022, with additional years to be added as they become available. The dataset aligns with the European Union’s Open Data Directive (Directive (EU) 2019/1024) and the Implementing Regulation (EU) 2023/138, which designates company and company ownership data as a high-value dataset. It is available for bulk download and API access under the Creative Commons Attribution 4.0 (CC BY 4.0) licence, allowing unrestricted reuse with appropriate attribution. By increasing transparency and enabling data-driven insights, this dataset supports public sector initiatives, financial analysis, and digital services development. The API endpoints can be accessed using these links - Query - https://opendata.cro.ie/api/3/action/datastore_search Query (via SQL) - https://opendata.cro.ie/api/3/action/datastore_search_sql

The data sets provide the text and detailed numeric information in all financial statements and their notes extracted from exhibits to corporate financial reports filed with the Commission using eXtensible Business Reporting Language (XBRL).

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

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Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

Consulting Firms: Deliver informed recommendations to clients with access to detailed financial datasets and key stakeholder profiles.

Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

The Financial Statements of U.S. Nonbank Subsidiaries of U.S. Holding Companies (FR Y-11; FR Y-11S) reporting forms collect financial information for individual nonfunctional regulated U.S. nonbank subsidiaries of domestic holding companies, which is essential for monitoring the subsidiaries' potential impact on the condition of the holding company or its subsidiary banks. Holding companies file the FR Y-11 on a quarterly or annual basis or the FR Y-11S on an annual basis, predominantly based on whether the organization meets certain asset size thresholds. The FR Y-11 data are used with other holding company data to assess the condition of holding companies that are heavily engaged in nonbanking activities and to monitor the volume, nature, and condition of their nonbanking operations.

Get detailed American Express Company Financial Statements 2020-2024. Find the income statements, balance sheet, cashflow, profitability, and other key ratios.

Financial data service providers offer financial market data and related services, primarily real-time feeds, portfolio analytics, research, pricing and valuation data, to financial institutions, traders and investors. Companies aggregate data and content from stock exchange feeds, broker and dealer desks and regulatory filings to distribute financial news and business information to the investment community. Recent globalization of the world capital market has benefited the financial sector and increased trading speed. Businesses rely on real-time data more than ever to help them make informed decisions. When considering a data service provider, an easy-to-use interface that shows customized, relevant information is vital for clients. During times of economic uncertainty, this information becomes more crucial than ever. Clients want information as soon and as frequently as possible, causing providers to prioritize efficiency and delivery. This was evident during the pandemic, the high interest rate environment in the latter part of the period and as the Fed cuts rates in 2024. Increased automation has helped industry players process large volumes of financial data, reducing analysis and reporting times. In addition, automation has reduced operational costs and reduced human data errors. These trends have resulted in growing revenue, which has risen at a CAGR of 3.2% to $21.9 billion over the past five years, including a 3.5% uptick in 2024 alone. Corporate profit will continue to expand as inflationary concerns begin to wane slowly. This will lead many companies to take on new clients as financial data helps them gain insight into operating their business amid ongoing trends and economic shakeups. With technology constantly advancing, service providers will continue investing in research and development to improve their products and services and best serve their clients. As technological advances continue, smaller players will be able to better compete with larger industry players. While this may lead to new companies joining the industry, larger providers will resume consolidation activity to expand their customer base. Overall, revenue is expected to swell at a CAGR of 2.7% to $25.0 billion by the end of 2029.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that includes summary financial information. The dataset consists of information from the statement of net assets and the statement of revenues, expenses and change in net assets reported by Local Development Corporations that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

Dataset Financial Report of 437 Company in Indonesia

Francis Financial is a reputable financial services company that provides a range of products and services to its clients. The company's data holdings are vast and varied, encompassing financial market data, economic trends, and industry insights. With a strong focus on serving its clients' needs, Francis Financial's data repository is a treasure trove of valuable information for anyone looking to gain a deeper understanding of the financial world.

From company reports and financial statements to market analysis and industry news, Francis Financial's data collection is a comprehensive archive of important financial information. By leveraging this data, users can gain valuable insights into market trends, spot emerging patterns, and make informed decisions. With its extensive data holdings and commitment to providing high-quality information, Francis Financial is an important player in the financial data landscape.

Get detailed Ford Motor Company Financial Statements 2020-2024. Find the income statements, balance sheet, cashflow, profitability, and other key ratios.

The Russian Financial Statements Database (RFSD) is an open, harmonized collection of annual unconsolidated financial statements of the universe of Russian firms:

• 🔓 First open data set with information on every active firm in Russia.

• 🗂️ First open financial statements data set that includes non-filing firms.

• 🏛️ Sourced from two official data providers: the Rosstat and the Federal Tax Service.

• 📅 Covers 2011-2023 initially, will be continuously updated.

• 🏗️ Restores as much data as possible through non-invasive data imputation, statement articulation, and harmonization.

The RFSD is hosted on 🤗 Hugging Face and Zenodo and is stored in a structured, column-oriented, compressed binary format Apache Parquet with yearly partitioning scheme, enabling end-users to query only variables of interest at scale.

The accompanying paper provides internal and external validation of the data: http://arxiv.org/abs/2501.05841.

Here we present the instructions for importing the data in R or Python environment. Please consult with the project repository for more information: http://github.com/irlcode/RFSD.

Importing The Data

You have two options to ingest the data: download the .parquet files manually from Hugging Face or Zenodo or rely on 🤗 Hugging Face Datasets library.

Python

🤗 Hugging Face Datasets

It is as easy as:

from datasets import load_dataset import polars as pl

This line will download 6.6GB+ of all RFSD data and store it in a 🤗 cache folder

RFSD = load_dataset('irlspbru/RFSD')

Alternatively, this will download ~540MB with all financial statements for 2023# to a Polars DataFrame (requires about 8GB of RAM)

RFSD_2023 = pl.read_parquet('hf://datasets/irlspbru/RFSD/RFSD/year=2023/*.parquet')

Please note that the data is not shuffled within year, meaning that streaming first n rows will not yield a random sample.

Local File Import

Importing in Python requires pyarrow package installed.

import pyarrow.dataset as ds import polars as pl

Read RFSD metadata from local file

RFSD = ds.dataset("local/path/to/RFSD")

Use RFSD_dataset.schema to glimpse the data structure and columns' classes

print(RFSD.schema)

Load full dataset into memory

RFSD_full = pl.from_arrow(RFSD.to_table())

Load only 2019 data into memory

RFSD_2019 = pl.from_arrow(RFSD.to_table(filter=ds.field('year') == 2019))

Load only revenue for firms in 2019, identified by taxpayer id

RFSD_2019_revenue = pl.from_arrow( RFSD.to_table( filter=ds.field('year') == 2019, columns=['inn', 'line_2110'] ) )

Give suggested descriptive names to variables

renaming_df = pl.read_csv('local/path/to/descriptive_names_dict.csv') RFSD_full = RFSD_full.rename({item[0]: item[1] for item in zip(renaming_df['original'], renaming_df['descriptive'])})

R

Local File Import

Importing in R requires arrow package installed.

library(arrow) library(data.table)

Read RFSD metadata from local file

RFSD <- open_dataset("local/path/to/RFSD")

Use schema() to glimpse into the data structure and column classes

schema(RFSD)

Load full dataset into memory

scanner <- Scanner$create(RFSD) RFSD_full <- as.data.table(scanner$ToTable())

Load only 2019 data into memory

scan_builder <- RFSD$NewScan() scan_builder$Filter(Expression$field_ref("year") == 2019) scanner <- scan_builder$Finish() RFSD_2019 <- as.data.table(scanner$ToTable())

Load only revenue for firms in 2019, identified by taxpayer id

scan_builder <- RFSD$NewScan() scan_builder$Filter(Expression$field_ref("year") == 2019) scan_builder$Project(cols = c("inn", "line_2110")) scanner <- scan_builder$Finish() RFSD_2019_revenue <- as.data.table(scanner$ToTable())

Give suggested descriptive names to variables

renaming_dt <- fread("local/path/to/descriptive_names_dict.csv") setnames(RFSD_full, old = renaming_dt$original, new = renaming_dt$descriptive)

Use Cases

🌍 For macroeconomists: Replication of a Bank of Russia study of the cost channel of monetary policy in Russia by Mogiliat et al. (2024) — interest_payments.md

🏭 For IO: Replication of the total factor productivity estimation by Kaukin and Zhemkova (2023) — tfp.md

🗺️ For economic geographers: A novel model-less house-level GDP spatialization that capitalizes on geocoding of firm addresses — spatialization.md

FAQ

Why should I use this data instead of Interfax's SPARK, Moody's Ruslana, or Kontur's Focus?hat is the data period?

To the best of our knowledge, the RFSD is the only open data set with up-to-date financial statements of Russian companies published under a permissive licence. Apart from being free-to-use, the RFSD benefits from data harmonization and error detection procedures unavailable in commercial sources. Finally, the data can be easily ingested in any statistical package with minimal effort.

What is the data period?

We provide financials for Russian firms in 2011-2023. We will add the data for 2024 by July, 2025 (see Version and Update Policy below).

Why are there no data for firm X in year Y?

Although the RFSD strives to be an all-encompassing database of financial statements, end users will encounter data gaps:

We do not include financials for firms that we considered ineligible to submit financial statements to the Rosstat/Federal Tax Service by law: financial, religious, or state organizations (state-owned commercial firms are still in the data).

Eligible firms may enjoy the right not to disclose under certain conditions. For instance, Gazprom did not file in 2022 and we had to impute its 2022 data from 2023 filings. Sibur filed only in 2023, Novatek — in 2020 and 2021. Commercial data providers such as Interfax's SPARK enjoy dedicated access to the Federal Tax Service data and therefore are able source this information elsewhere.

Firm may have submitted its annual statement but, according to the Uniform State Register of Legal Entities (EGRUL), it was not active in this year. We remove those filings.

Why is the geolocation of firm X incorrect?

We use Nominatim to geocode structured addresses of incorporation of legal entities from the EGRUL. There may be errors in the original addresses that prevent us from geocoding firms to a particular house. Gazprom, for instance, is geocoded up to a house level in 2014 and 2021-2023, but only at street level for 2015-2020 due to improper handling of the house number by Nominatim. In that case we have fallen back to street-level geocoding. Additionally, streets in different districts of one city may share identical names. We have ignored those problems in our geocoding and invite your submissions. Finally, address of incorporation may not correspond with plant locations. For instance, Rosneft has 62 field offices in addition to the central office in Moscow. We ignore the location of such offices in our geocoding, but subsidiaries set up as separate legal entities are still geocoded.

Why is the data for firm X different from https://bo.nalog.ru/?

Many firms submit correcting statements after the initial filing. While we have downloaded the data way past the April, 2024 deadline for 2023 filings, firms may have kept submitting the correcting statements. We will capture them in the future releases.

Why is the data for firm X unrealistic?

We provide the source data as is, with minimal changes. Consider a relatively unknown LLC Banknota. It reported 3.7 trillion rubles in revenue in 2023, or 2% of Russia's GDP. This is obviously an outlier firm with unrealistic financials. We manually reviewed the data and flagged such firms for user consideration (variable outlier), keeping the source data intact.

Why is the data for groups of companies different from their IFRS statements?

We should stress that we provide unconsolidated financial statements filed according to the Russian accounting standards, meaning that it would be wrong to infer financials for corporate groups with this data. Gazprom, for instance, had over 800 affiliated entities and to study this corporate group in its entirety it is not enough to consider financials of the parent company.

Why is the data not in CSV?

The data is provided in Apache Parquet format. This is a structured, column-oriented, compressed binary format allowing for conditional subsetting of columns and rows. In other words, you can easily query financials of companies of interest, keeping only variables of interest in memory, greatly reducing data footprint.

Version and Update Policy

Version (SemVer): 1.0.0.

We intend to update the RFSD annualy as the data becomes available, in other words when most of the firms have their statements filed with the Federal Tax Service. The official deadline for filing of previous year statements is April, 1. However, every year a portion of firms either fails to meet the deadline or submits corrections afterwards. Filing continues up to the very end of the year but after the end of April this stream quickly thins out. Nevertheless, there is obviously a trade-off between minimization of data completeness and version availability. We find it a reasonable compromise to query new data in early June, since on average by the end of May 96.7% statements are already filed, including 86.4% of all the correcting filings. We plan to make a new version of RFSD available by July.

Licence

Creative Commons License Attribution 4.0 International (CC BY 4.0).

Copyright © the respective contributors.

Citation

Please cite as:

@unpublished{bondarkov2025rfsd, title={{R}ussian {F}inancial {S}tatements {D}atabase}, author={Bondarkov, Sergey and Ledenev, Victor and Skougarevskiy, Dmitriy}, note={arXiv preprint arXiv:2501.05841}, doi={https://doi.org/10.48550/arXiv.2501.05841}, year={2025}}

Acknowledgments and Contacts

Data collection and processing: Sergey Bondarkov, sbondarkov@eu.spb.ru, Viktor Ledenev, vledenev@eu.spb.ru

Project conception, data validation, and use cases: Dmitriy Skougarevskiy, Ph.D.,

This sample dataset includes information for five different SMEs across various locations. The data includes the SME's capital, turnover, pre-tax profit, tax, and number of employees for both 2018 and 2019.

Please note that the data provided is completely random and is for illustrative purposes only. In practice, the data for a particular SME would need to be based on actual financial and business metrics.

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: All Manufacturing: Net Sales, Receipts, and Operating Revenues (QFR101MFGUSNO) from Q4 2000 to Q1 2025 about operating, receipts, revenue, finance, Net, corporate, sales, manufacturing, industry, and USA.

Snapshot img

Financial Analytics Market Size 2025-2029

The financial analytics market size is forecast to increase by USD 9.09 billion at a CAGR of 12.7% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for advanced risk management tools in today's complex financial landscape. With the exponential rise in data generation across various industries, financial institutions are seeking to leverage analytics to gain valuable insights and make informed decisions. However, this data-driven approach comes with its own challenges. Data privacy and security concerns are becoming increasingly prominent as financial institutions grapple with the responsibility of safeguarding sensitive financial information. Ensuring data security and maintaining regulatory compliance are essential for businesses looking to capitalize on the opportunities presented by financial analytics.
As the market continues to evolve, companies must navigate these challenges while staying abreast of the latest trends and technologies to remain competitive. Effective implementation of robust data security measures, adherence to regulatory requirements, and continuous innovation will be key to success in the market. Data visualization tools enable effective communication of complex financial data, while financial advisory services offer expert guidance on financial modeling and regulatory compliance.

What will be the Size of the Financial Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, sensitivity analysis plays a crucial role in assessing the impact of various factors on financial models. Data lakes serve as vast repositories for storing and processing large volumes of financial data, enabling advanced quantitative analysis. Financial regulations mandate strict data compliance regulations, ensuring data privacy and security. Data analytics platforms integrate statistical software, machine learning libraries, and prescriptive analytics to deliver actionable insights. Financial reporting software and business intelligence tools facilitate descriptive analytics, while diagnostic analytics uncovers hidden trends and anomalies. On-premise analytics and cloud-based analytics cater to diverse business needs, with data warehouses and data pipelines ensuring seamless data flow.
Scenario analysis and stress testing help financial institutions assess risks and make informed decisions. Data engineering and data governance frameworks ensure data accuracy, consistency, and availability. Data architecture, data compliance regulations, and auditing standards maintain transparency and trust in financial reporting. Predictive modeling and financial modeling software provide valuable insights into future financial performance. Data security measures protect sensitive financial data, safeguarding against potential breaches.

How is this Financial Analytics Industry segmented?

The financial analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solution
  Services


Deployment

  On-premises
  Cloud


Sector

  Large enterprises
  Small and medium-sized enterprises (SMEs)


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period. Financial analytics solutions play a pivotal role in assessing and managing various financial risks for organizations. These tools help identify potential risks, such as credit risks, market risks, and operational risks, and enable proactive risk mitigation measures. Compliance with stringent regulations, including Basel III, Dodd-Frank, and GDPR, necessitates robust data analytics and reporting capabilities. Data visualization, machine learning, statistical modeling, and predictive analytics are integral components of financial analytics solutions. Machine learning and statistical modeling enable automated risk analysis and prediction, while predictive analytics offers insights into future trends and potential risks.

Data governance and data compliance help organizations maintain data security and privacy. Data integration and ETL processes facilitate seamless data flow between various systems, ensuring data consistency and accuracy. Time series analysis and ratio analysis offer insights into historical financial trends and performance. Customer segmentation and sensitivity analysis provide val

List of companies in the S&P 500 (Standard and Poor's 500). The S&P 500 is a free-float, capitalization-weighted index of the top 500 publicly listed stocks in the US (top 500 by market cap). The ...

Get detailed Delta Air Lines Financial Statements 2020-2024. Find the income statements, balance sheet, cashflow, profitability, and other key ratios.

The bankruptcy financial company income statement information is data that allows you to check profitability and operating performance based on the income and loss items of a financial company undergoing bankruptcy procedures. It provides item codes, item names, current amount, previous amount, and financial statement classification (operating revenue, operating expenses, corporate tax expenses, etc.) based on the base date and company name. The data consists of a single operation, and the details are as follows. ① Bankruptcy financial company income statement inquiry: Bankruptcy financial company income statement inquiry function that searches report code names, account subject names, current account subject amounts, etc. through the base date, corporate registration number, and fiscal year. This data can be used to understand the management performance flow of a bankrupt financial company, whether it is in deficit, and its accounting performance structure.

The Corporate Financial Fraud project is a study of company and top-executive characteristics of firms that ultimately violated Securities and Exchange Commission (SEC) financial accounting and securities fraud provisions compared to a sample of public companies that did not. The fraud firm sample was identified through systematic review of SEC accounting enforcement releases from 2005-2010, which included administrative and civil actions, and referrals for criminal prosecution that were identified through mentions in enforcement release, indictments, and news searches. The non-fraud firms were randomly selected from among nearly 10,000 US public companies censused and active during at least one year between 2005-2010 in Standard and Poor's Compustat data. The Company and Top-Executive (CEO) databases combine information from numerous publicly available sources, many in raw form that were hand-coded (e.g., for fraud firms: Accounting and Auditing Enforcement Releases (AAER) enforcement releases, investigation summaries, SEC-filed complaints, litigation proceedings and case outcomes). Financial and structural information on companies for the year leading up to the financial fraud (or around year 2000 for non-fraud firms) was collected from Compustat financial statement data on Form 10-Ks, and supplemented by hand-collected data from original company 10-Ks, proxy statements, or other financial reports accessed via Electronic Data Gathering, Analysis, and Retrieval (EDGAR), SEC's data-gathering search tool. For CEOs, data on personal background characteristics were collected from Execucomp and BoardEx databases, supplemented by hand-collection from proxy-statement biographies.

Information on Over-the-Counter Company Financial Reports (Financial Industry) (GreTai Securities Market)

The Financial Statements of Holding Companies (FR Y-9 Reports) collects standardized financial statements from domestic holding companies (HCs). This is pursuant to the Bank Holding Company Act of 1956, as amended (BHC Act), and the Home Owners Loan Act (HOLA). The FR Y-9C is used to identify emerging financial risks and monitor the safety and soundness of HC operations. HCs file the FR Y-9C and FR Y-9LP quarterly, the FR Y-9SP semiannually, the FR Y-9ES annually, and the FR Y-9CS on a schedule that is determined when this supplement is used.