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...

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).

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling 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. 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 by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Afghanistan was 1,000 individuals. Gender-matched sampling was used during the final stage of selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Extract of data from financial transaction system to enable detailed financial reporting across all business units in the Civil Service

This data contains the latest State and Local Government Finance data from the U.S. Census. A detailed description of the project can be found in: Pierson K., Hand M., and Thompson F. (2015). The Government Finance Database: A Common Resource for Quantitative Research in Public Financial Analysis. PLoS ONE doi: 10.1371/journal.pone.0130119

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

National coverage

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 Argentina is 1003.

Mode of data collection

Landline and mobile telephone

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.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

Financial Data Services Market size was valued at USD 23.3 Billion in 2023 and is projected to reach USD 42.6 Billion by 2031, growing at a CAGR of 8.1% during the forecast period 2024-2031.Global Financial Data Services Market DriversThe market drivers for the Financial Data Services Market can be influenced by various factors. These may include:The need for real-time analytics is growing: Real-time analytics are becoming more and more necessary in the financial sector due to the acceleration of data consumption. To reduce risks, make wise decisions, and enhance customer service, organizations need quick insights. Stakeholders are giving priority to solutions that enable quick data processing and analysis due to the increase in market volatility and complexity. The need for sophisticated analytical skills is driving providers of financial data services to modernize their products. As companies come to realize that using real-time data is crucial for keeping a competitive edge in a fast-paced financial climate, the competition among them to provide timely insights also boosts market growth.Growing Machine Learning and AI Adoption: Data analysis has been profoundly changed by the incorporation of AI and machine learning technology into financial data services. By enabling predictive analytics, these technologies help financial organizations make better decisions and reduce risk. Businesses can find trends that were previously invisible by automating data processing operations. This leads to more precise forecasts and improved investment plans. Furthermore, sophisticated algorithms are flexible enough to adjust to shifting circumstances, keeping organizations flexible. The increasing intricacy of financial markets necessitates the use of AI and machine learning, which in turn drives demand for sophisticated financial data services and promotes innovation in the sector.Global Financial Data Services Market RestraintsSeveral factors can act as restraints or challenges for the Financial Data Services Market. These may include:Difficulties in Regulatory Compliance: Regulations controlling data management, privacy, and financial transactions place heavy restrictions on the financial data services market. Regulations like the GDPR, CCPA, and banking industry standards like Basel III and SOX must all be complied with by organizations. Complying with these requirements frequently necessitates a significant investment in staff and compliance systems, which can be taxing, especially for smaller businesses. Regulations are dynamic, and different locations have different needs, which adds to the complexity and expense. Noncompliance not only results in monetary fines but also has the potential to harm an entity's image, so impeding market expansion.Dangers to Data Security: Threats to data security are a major impediment to the financial data services market. Because they manage sensitive data, financial institutions are often the targets of cyberattacks. Breach can lead to significant monetary losses, legal repercussions, and long-term harm to one's image. Although they can greatly increase operating expenses, investments in strong security measures like encryption, safe access protocols, and continual monitoring are crucial. Moreover, the dynamic strategies employed by cybercriminals need continuous adjustment, placing a burden on resources and detracting from the main operations of businesses. The evolution of security threats poses a challenge to preserving consumer trust, hence impeding industry expansion.

Data Analytics in Financial Market Outlook

The global data analytics in financial market size was valued at approximately USD 10.5 billion in 2023 and is projected to reach around USD 34.8 billion by 2032, growing at a robust CAGR of 14.4% during the forecast period. This remarkable growth is driven by the increasing adoption of advanced analytics technologies, the need for real-time data-driven decision-making, and the rising incidence of financial fraud.

One of the primary growth factors for the data analytics in the financial market is the burgeoning volume of data generated from diverse sources such as transactions, social media, and online banking. Financial institutions are increasingly leveraging data analytics to process and analyze this vast amount of data to gain actionable insights. Additionally, technological advancements in artificial intelligence (AI) and machine learning (ML) are significantly enhancing the capabilities of data analytics tools, enabling more accurate predictions and efficient risk management.

Another driving factor is the heightened focus on regulatory compliance and security management. In the wake of stringent regulations imposed by financial authorities globally, organizations are compelled to adopt robust analytics solutions to ensure compliance and mitigate risks. Moreover, with the growing threat of cyber-attacks and financial fraud, there is a heightened demand for sophisticated analytics tools capable of detecting and preventing fraudulent activities in real-time.

Furthermore, the increasing emphasis on customer-centric strategies in the financial sector is fueling the adoption of data analytics. Financial institutions are utilizing analytics to understand customer behavior, preferences, and needs more accurately. This enables them to offer personalized services, improve customer satisfaction, and drive revenue growth. The integration of advanced analytics in customer management processes helps in enhancing customer engagement and loyalty, which is crucial in the competitive financial landscape.

Regionally, North America has been the dominant player in the data analytics in financial market, owing to the presence of major market players, technological advancements, and a high adoption rate of analytics solutions. However, the Asia Pacific region is anticipated to witness the highest growth during the forecast period, driven by the rapid digitalization of financial services, increasing investments in analytics technologies, and the growing focus on enhancing customer experience in emerging economies like China and India.

Component Analysis

In the data analytics in financial market, the components segment is divided into software and services. The software segment encompasses various analytics tools and platforms designed to process and analyze financial data. This segment holds a significant share in the market owing to the continuous advancements in software capabilities and the growing need for real-time analytics. Financial institutions are increasingly investing in sophisticated software solutions to enhance their data processing and analytical capabilities. The software segment is also being propelled by the integration of AI and ML technologies, which offer enhanced predictive analytics and automation features.

On the other hand, the services segment includes consulting, implementation, and maintenance services provided by vendors to help financial institutions effectively deploy and manage analytics solutions. With the rising complexity of financial data and analytics tools, the demand for professional services is on the rise. Organizations are seeking expert guidance to seamlessly integrate analytics solutions into their existing systems and optimize their use. The services segment is expected to grow significantly as more institutions recognize the value of professional support in maximizing the benefits of their analytics investments.

The software segment is further categorized into various types of analytics tools such as descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analytics tools are used to summarize historical data to identify patterns and trends. Predictive analytics tools leverage historical data to forecast future outcomes, which is crucial for risk management and fraud detection. Prescriptive analytics tools provide actionable recommendations based on predictive analysis, aiding in decision-making processes. The growing need for advanced predictive and prescriptive analytics is driving the demand for specialized software solut

Abstract copyright UK Data Service and data collection copyright owner.The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material. The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files. Main Topics: The files in this dataset relate to the datafiles held in the Leicester database in the directory /rjb/.. File Information g135sld1.* Average annual estimates in tonnes of the movement of silver and silver equivalent, 1501-1800 Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. No sampling (total universe) Compilation or synthesis of existing material

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

The regions of Gao, Kidal, Mopti, and Tombouctou were excluded for security reasons. Quartiers and villages with less than 50 inhabitants were also excluded from the sample. The excluded areas represent 23 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 Mali 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.

Market Size statistics on the Financial Data Service Providers industry in the US

Information on price volatility and forecast uncertainty for crude oil and natural gas as well as an analysis of 7 key factors that may influence oil prices, physical market factors and factors related to trading and financial markets.

DOI Abstract copyright UK Data Service and data collection copyright owner.The European State Finance Database (ESFD) is an international collaborative research project for the collection of data in European fiscal history. There are no strict geographical or chronological boundaries to the collection, although data for this collection comprise the period between c.1200 to c.1815. The purpose of the ESFD was to establish a significant database of European financial and fiscal records. The data are drawn from the main extant sources of a number of European countries, as the evidence and the state of scholarship permit. The aim was to collect the data made available by scholars, whether drawing upon their published or unpublished archival research, or from other published material. The ESFD project at the University of Leicester serves also to assist scholars working with the data by providing statistical manipulations of data and high quality graphical outputs for publication. The broad aim of the project was to act as a facilitator for a general methodological and statistical advance in the area of European fiscal history, with data capture and the interpretation of data in key publications as the measurable indicators of that advance. The data were originally deposited at the UK Data Archive in SAS transport format and as ASCII files; however, data files in this new edition have been saved as tab delimited files. Furthermore, this new edition features documentation in the form of a single file containing essential data file metadata, source details and notes of interest for particular files. Main Topics: The files in this dataset relate to the datafiles held in the Leicester database in the directory /frindic/.. The data were compiled for the purpose of calculating real increases in taxation over time. File Information g098fin1. European exchange rates, 1600-1775: Paris livres tournois to 100 pounds sterling g098ind1. The value of the livre tournois in grams of fine silver, 1513-1785 g098ind2. French mint output expressed in terms of livres tournois, 1308-1791 g098ind7. Paris wheat prices, harvest years, 1521-1698 g098ind8. French wheat prices, calendar years, 1701-1817 g098ind9. Wheat prices at Paris, calendar years, 1431-1788 Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research. No sampling (total universe) Compilation or synthesis of existing material

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.

This dataset reports summary state-by-state total expenditures by program for the Medicaid Program, Medicaid Administration and CHIP programs. These state expenditures are tracked through the automated Medicaid Budget and Expenditure System/State Children's Health Insurance Program Budget and Expenditure System (MBES/CBES). For more information, visit https://medicaid.gov/medicaid/finance/state-expenditure-reporting/expenditure-reports/index.html.

Finance Data Fusion Market Outlook

The global finance data fusion market size is projected to grow at a CAGR of 12.5% from 2024 to 2032, with the market value increasing from USD 2.5 billion in 2023 to an estimated USD 7.4 billion by 2032. This impressive growth is driven by an intensifying demand for real-time analytics, the increasing complexity of financial transactions, and the need for improved risk management and fraud detection mechanisms in the financial sector.

One of the primary growth factors propelling the finance data fusion market is the rising necessity for robust risk management solutions. Financial institutions are increasingly recognizing the importance of integrating diverse data sources to gain comprehensive insights into potential risks. With the advent of big data and advanced analytics, data fusion technologies enable organizations to synthesize information from multiple datasets, including market data, transactional data, and social media feeds, thereby enhancing their ability to predict and manage risks in a dynamic market environment. This capability is particularly critical in an era where financial stability and regulatory compliance are paramount.

Another significant driver of market growth is the surging demand for enhanced fraud detection systems. Financial fraud has become increasingly sophisticated, necessitating the adoption of advanced technologies that can detect and mitigate fraudulent activities in real-time. Data fusion solutions allow for the integration of diverse data points, providing a holistic view of customer behavior and transaction patterns. This multi-dimensional analysis significantly improves the accuracy of fraud detection systems, enabling financial institutions to safeguard their assets and maintain customer trust. The growing reliance on digital payment systems further underscores the need for advanced fraud detection technologies.

Furthermore, the growing importance of customer analytics in the financial sector is contributing to the market's expansion. Financial institutions are leveraging data fusion technologies to gain deeper insights into customer preferences, behavior, and needs. By integrating data from various sources, such as transaction histories, social media interactions, and demographic information, organizations can create detailed customer profiles that drive personalized marketing strategies and improve customer satisfaction. The ability to deliver tailored financial products and services based on comprehensive data analysis is a key competitive advantage in the financial industry.

Regionally, North America is expected to dominate the finance data fusion market, owing to its advanced financial infrastructure and the early adoption of innovative technologies. The presence of major financial institutions and a highly developed regulatory framework further supports market growth in this region. Europe and Asia Pacific are also anticipated to witness substantial growth, driven by increasing investments in financial technology and the rising demand for advanced data analytics solutions. In contrast, Latin America and the Middle East & Africa are projected to experience moderate growth, influenced by varying levels of technological adoption and economic development.

Component Analysis

The finance data fusion market can be segmented by component into software, hardware, and services. The software segment is expected to hold the largest market share, driven by the increasing adoption of advanced analytic tools and platforms that enable the integration and analysis of diverse data sources. Financial institutions are investing heavily in software solutions that provide real-time insights and predictive analytics, facilitating more informed decision-making and enhancing operational efficiency. The proliferation of cloud-based software solutions is also contributing to the segment's growth, as they offer scalable and cost-effective alternatives to traditional on-premises systems.

The hardware segment, although smaller in comparison to software, plays a crucial role in supporting data fusion activities. High-performance computing systems, storage solutions, and network infrastructure are essential for managing and processing the vast amounts of data generated in the financial sector. As financial institutions continue to expand their data capabilities, the demand for robust and scalable hardware solutions is expected to rise. Innovations in hardware technology, such as advanced processors and high-speed storage devices, are further driving the segment's growth.

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies. The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

The Australian Government general government sector Monthly Financial Statements are officially available from July to May for each month at www.finance.gov.au.

This dataset provides an historical series of a collection of published Australian Government general government sector monthly financial statements from 2005-06, including the:

• Aggregates tables

• Operating Statement

• Balance Sheet

• Cash Flow Statement

• Taxation tables

• Function tables

Monthly Financial Statements are not published for the month of June. These figures can be sourced from the Final Budget Outcome (www.budget.gov.au) or the Consolidated Financial Statements (www.finance.gov.au).

The Historical Monthly Financial Statements series is provided to assist those who wish to analyse, visualise and programmatically access this data.

The Australian Government Monthly Financial Statements are prepared on a basis consistent with the Budget as required under section 47 of the Public Governance, Performance and Accountability Act 2013 (formerly section 54 of the Financial Management and Accountability Act 1997).

Since 2008-09 the statements have been prepared in accordance with Australian Accounting Standard AASB 1049 – Whole of Government and General Government Sector Financial Reporting, which requires accounting treatment based on the Australian Bureau of Statistics’ (ABS) Government Finance Statistics (GFS) except where Australian Accounting Standards (AAS) provide a better conceptual treatment for specific items. Departures are limited to complying with either ABS GFS or AAS. The change in 2008-09 represented a significant change in the format and content of the monthly financial statements.

The Monthly Financial Statements estimates dataset is based solely upon the published monthly profiles. Where no year-to-date profile was published the year-to-date actual figure has been used in its place.

Please note that this dataset represents published information and will not be recast. Figures may not be directly comparable over time due to changes of classification, accounting standards or budget treatments.

This data is released by the Department of Finance.

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems. By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.