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.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

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...

Financial statement filings from banks and credit unions.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

Localities with less than 100 inhabitants were excluded from the sample. The excluded areas represent approximately 4 percent of the total population

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Ghana is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

NCUA: All Inst: Assets: Inv: Others data was reported at 7,511,090.414 USD th in Jun 2018. This records an increase from the previous number of 7,375,582.417 USD th for Mar 2018. NCUA: All Inst: Assets: Inv: Others data is updated quarterly, averaging 5,420,447.103 USD th from Mar 2005 (Median) to Jun 2018, with 54 observations. The data reached an all-time high of 7,566,152.636 USD th in Dec 2017 and a record low of 2,250,360.965 USD th in Dec 2005. NCUA: All Inst: Assets: Inv: Others data remains active status in CEIC and is reported by National Credit Union Administration. The data is categorized under Global Database’s USA – Table US.KB044: Financial Data: National Credit Union Administration: All Institutions.

• Data from Dec 1989 to Dec 2020.
• Includes Income Statement, Balance Sheet, and Cashflow statement.
• Adjusted for restatements.
• Includes valuation metrics such as enterprise valuation and market capitalization.
• Over 30 ratios such as p/e ratio, EBITDA/sales, gross margin etc..
• Standardized categories for comparison between companies.

Detailed current and historical financial data on the global Financial Institutions (FIG), Metals & Mining, Energy, Real Estate and Media sectors.

Success.ai’s Company Financial Data for European Financial Professionals provides a comprehensive dataset tailored for businesses looking to connect with financial leaders, analysts, and decision-makers across Europe. Covering roles such as CFOs, accountants, financial consultants, and investment managers, this dataset offers verified contact details, firmographic insights, and actionable professional histories.

With access to over 170 million verified professional profiles, Success.ai ensures your outreach, market research, and partnership strategies are driven by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is indispensable for navigating the fast-paced European financial landscape.

Why Choose Success.ai’s Company Financial Data?

1. Verified Contact Data for Precision Targeting

   • Access verified work emails, phone numbers, and LinkedIn profiles of financial professionals across Europe.
   • AI-driven validation ensures 99% accuracy, reducing communication inefficiencies and improving engagement rates.

2. Comprehensive Coverage Across Europe

   • Includes financial professionals from key markets such as the United Kingdom, Germany, France, Italy, and the Netherlands.
   • Gain insights into regional financial trends, industry dynamics, and regulatory landscapes.

3. Continuously Updated Datasets

   • Real-time updates capture changes in professional roles, company structures, and market conditions.
   • Stay ahead of industry shifts and capitalize on emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 170M+ Verified Professional Profiles: Access detailed profiles of European financial professionals across industries and sectors.
• Verified Contact Details: Gain work emails, phone numbers, and LinkedIn profiles for precise targeting.
• Firmographic Data: Understand company sizes, revenue ranges, and geographic footprints to inform your outreach strategy.
• Leadership Insights: Connect with CFOs, financial controllers, and investment managers driving financial strategies.

Key Features of the Dataset:

1. Comprehensive Financial Professional Profiles

   • Identify and connect with key players in finance, including financial analysts, accountants, and consultants.
   • Target professionals responsible for budgeting, investment strategies, regulatory compliance, and financial planning.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (banking, fintech, asset management), geographic location, or job function.
   • Tailor campaigns to align with specific financial needs, such as software solutions, advisory services, or compliance tools.

3. Regional and Industry Insights

   • Leverage data on European financial trends, regulatory challenges, and market opportunities.
   • Refine your approach to align with industry-specific demands and geographic preferences.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Lead Generation

   • Design targeted campaigns to promote financial software, advisory services, or compliance solutions to European financial professionals.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media.

2. Partnership Development and Collaboration

   • Build relationships with financial firms, fintech companies, and investment organizations exploring strategic partnerships.
   • Foster collaborations that enhance financial efficiency, innovation, or regulatory compliance.

3. Market Research and Competitive Analysis

   • Analyze financial trends across Europe to refine product offerings, marketing strategies, and business expansion plans.
   • Benchmark against competitors to identify growth opportunities and emerging demands.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for financial roles, from analysts to CFOs.
   • Provide workforce optimization platforms or training solutions tailored to the financial sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality financial data at competitive prices, ensuring strong ROI for your marketing, sales, and partnership initiatives.

2. Seamless Integration

   • Integrate verified financial data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven decisions, refine targeting, and boost conversion rates in financial ca...

NCUA: All Inst: Saving: Share Draft data was reported at 180,613,594.766 USD th in Mar 2018. This records an increase from the previous number of 168,465,557.974 USD th for Dec 2017. NCUA: All Inst: Saving: Share Draft data is updated quarterly, averaging 99,268,729.203 USD th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 180,613,594.766 USD th in Mar 2018 and a record low of 68,861,474.380 USD th in Sep 2007. NCUA: All Inst: Saving: Share Draft data remains active status in CEIC and is reported by National Credit Union Administration. The data is categorized under Global Database’s USA – Table US.KB017: Financial Data: National Credit Union Administration: All Institutions.

Standardized and As Reported financial data for global public companies as well as thousands of private companies and private companies with public debt.

The statistic presents the leading financial data service companies in the United States in 2015, by revenue. In that year, Visa was ranked second with the revenue of approximately 13.88 billion U.S. dollars.

NCUA: All Inst: Interest Expense data was reported at 2,100,364.168 USD th in Mar 2018. This records a decrease from the previous number of 7,558,131.261 USD th for Dec 2017. NCUA: All Inst: Interest Expense data is updated quarterly, averaging 5,433,344.421 USD th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 20,466,720.730 USD th in Dec 2007 and a record low of 1,432,141.917 USD th in Mar 2014. NCUA: All Inst: Interest Expense data remains active status in CEIC and is reported by National Credit Union Administration. The data is categorized under Global Database’s USA – Table US.KB017: Financial Data: National Credit Union Administration: All Institutions.

Discover verified Company Financial Data with Success.ai. Includes profiles of CFOs, financial analysts, and corporate treasurers with work emails and phone numbers. Continuously updated and AI-validated. Best price guaranteed.

Compilation of several financial data (revenues, perceptions, etc.) on French Copyright Societies. The figures are based on a series of annual reports of the Court of Auditors.

Dataset of the paper entitled "Variable selection for classification and forecasting of the family firm's socioemotional wealth"

description: This dataset provides annual summary financial data for Iowa state-chartered credit unions starting with year ending 12/31/2005.; abstract: This dataset provides annual summary financial data for Iowa state-chartered credit unions starting with year ending 12/31/2005.

NCUA: All Inst: Assets: Land & BLDG data was reported at 22,935,708.364 USD th in Mar 2018. This records an increase from the previous number of 22,637,135.651 USD th for Dec 2017. NCUA: All Inst: Assets: Land & BLDG data is updated quarterly, averaging 17,070,895.324 USD th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 22,935,708.364 USD th in Mar 2018 and a record low of 10,296,257.876 USD th in Mar 2005. NCUA: All Inst: Assets: Land & BLDG data remains active status in CEIC and is reported by National Credit Union Administration. The data is categorized under Global Database’s USA – Table US.KB017: Financial Data: National Credit Union Administration: All Institutions.

NCUA: All Inst: Equity: Net Income data was reported at 979,115.391 USD th in Mar 2018. This records an increase from the previous number of 0.000 USD th for Dec 2017. NCUA: All Inst: Equity: Net Income data is updated quarterly, averaging 615,093.126 USD th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 2,266,403.473 USD th in Sep 2017 and a record low of -1,321,021.174 USD th in Mar 2009. NCUA: All Inst: Equity: Net Income data remains active status in CEIC and is reported by National Credit Union Administration. The data is categorized under Global Database’s USA – Table US.KB017: Financial Data: National Credit Union Administration: All Institutions.

NCUA: All Inst: Interest Income: Income from Trading data was reported at -11,324.091 USD th in Mar 2018. This records a decrease from the previous number of 22,139.014 USD th for Dec 2017. NCUA: All Inst: Interest Income: Income from Trading data is updated quarterly, averaging 3,809.539 USD th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 22,139.014 USD th in Dec 2017 and a record low of -16,868.599 USD th in Dec 2008. NCUA: All Inst: Interest Income: Income from Trading data remains active status in CEIC and is reported by National Credit Union Administration. The data is categorized under Global Database’s USA – Table US.KB017: Financial Data: National Credit Union Administration: All Institutions.

NCUA: State: Non Interest Income: Other Operating Income data was reported at 1,317,125.021 USD th in Mar 2018. This records a decrease from the previous number of 4,496,281.533 USD th for Dec 2017. NCUA: State: Non Interest Income: Other Operating Income data is updated quarterly, averaging 1,317,125.021 USD th from Mar 2005 (Median) to Mar 2018, with 53 observations. The data reached an all-time high of 4,496,281.533 USD th in Dec 2017 and a record low of 230,369.214 USD th in Mar 2005. NCUA: State: Non Interest Income: Other Operating Income data remains active status in CEIC and is reported by National Credit Union Administration. The data is categorized under Global Database’s USA – Table US.KB019: Financial Data: National Credit Union Administration: State Institutions.

United States BNY: Net Income Due to Bank & NCI data was reported at 588,000.000 USD th in Dec 2019. This records a decrease from the previous number of 661,000.000 USD th for Sep 2019. United States BNY: Net Income Due to Bank & NCI data is updated quarterly, averaging 485,000.000 USD th from Mar 2009 (Median) to Dec 2019, with 44 observations. The data reached an all-time high of 905,000.000 USD th in Sep 2014 and a record low of -2,414,000.000 USD th in Sep 2009. United States BNY: Net Income Due to Bank & NCI data remains active status in CEIC and is reported by Federal Deposit Insurance Corporation. The data is categorized under Global Database’s United States – Table US.KB061: Financial Data: Federal Deposit Insurance Corporation: Bank of New York Mellon.