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...

Enterprise Financial Management Software Market size was valued at USD 9.33 Billion in 2024 and is projected to reach USD 26.25 Billion by 2031, growing at a CAGR of 13.80% from 2024 to 2031.

Enterprise Financial Management Software Market Drivers

Digital Transformation Initiatives: Organizations across various sectors are increasingly adopting digital solutions to streamline operations. Enterprise Financial Management Software plays a crucial role in automating financial processes, improving accuracy, and enhancing decision-making, thereby driving market demand.

Growing Need for Real-Time Financial Data: As businesses become more data-driven, the demand for real-time financial insights is rising. Enterprise Financial Management Software enables companies to access up-to-date financial information, facilitating quicker and more informed decision-making, which is a significant market driver.

Regulatory Compliance and Risk Management: The increasing complexity of financial regulations and the need for robust risk management solutions are pushing companies to adopt sophisticated financial management software. These tools help organizations comply with regulations, mitigate risks, and avoid costly penalties, thus boosting market growth.

According to Cognitive Market Research, the global Financial Data Service market size will be USD 24152.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 8.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 9661.00 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 7245.75 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 5555.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 1207.63 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 483.05 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2031.
Datafeed/API solutions are the dominant segment, as they allow seamless data integration into existing systems and platforms, making them ideal for companies requiring real-time data across multiple applications

Market Dynamics of Financial Data Service Market

Key Drivers for Financial Data Service Market

Increased Data-Driven Decision-Making to Boost Market Growth

As digital transformation sweeps through financial services, data-driven decision-making has become essential for businesses to remain competitive. Institutions, both financial and non-financial, are increasingly leveraging financial data to guide strategic investments, manage risks, and streamline operations. By utilizing real-time data and predictive analytics, companies gain actionable insights to optimize their investment portfolios and financial planning. With the enhanced capability to analyze data trends and assess market scenarios, businesses can mitigate risks more effectively, making this driver critical to the growth of the financial data service market. For instance, in September 2022, Alibaba Cloud, the digital technology and intellectual backbone of Alibaba Group launched a comprehensive suite of Alibaba Cloud for Financial Services solutions. Comprising over 70 products, these solutions are designed to help financial services institutions of all sizes across banking, FinTech, insurance, and securities, digitalize their operations

Advancements in Analytics Technology to Drive Market Growth

The integration of advanced analytics technologies like artificial intelligence (AI) and machine learning (ML) in financial data services has significantly enhanced the accuracy and scope of market insights. AI and ML enable companies to process vast amounts of financial data, identify patterns, and make predictions, thus facilitating strategic planning and investment optimization. These technologies also allow for real-time insights, giving firms a competitive advantage in rapidly evolving markets. With continuous improvements in AI and ML, the demand for advanced data services is expected to grow, positioning this as a key driver of market expansion.

Restraint Factor for the Financial Data Service Market

High Cost of Data Services, will Limit Market Growth

The high cost associated with premium financial data services is a significant restraint, particularly for small and medium-sized enterprises (SMEs). Many advanced platforms and data feeds come with substantial subscription fees, limiting their accessibility to larger organizations with more considerable budgets. This cost barrier restricts smaller firms from fully integrating advanced data insights into their operations. As a result, high subscription costs prevent widespread adoption among SMEs, hindering the financial data service market’s overall growth potential.

Impact of Covid-19 on the Financial Data Service Market

Covid-19 significantly impacted the Financial Data Service Market as companies increasingly relied on accurate data analytics for rapid decision-making amid market volatility. During the pandemic, financial data providers observed heightened demand for real-time and historical data to model economic scenarios and assess risks accurately. This shift spurred technological advancements a...

The Relational Database Software Market size was estimated at USD 21.97 Billion in 2024 and is projected to reach USD 45.23 Billion by 2031, growing at a CAGR of 9.4 % from 2024 to 2031

Global Relational Database Software Market Drivers

Rising Demand for Efficient Data Management: Organizations across industries are generating and collecting ever-increasing volumes of data. This necessitates efficient and secure data management solutions. Relational databases, with their structured format and robust querying capabilities, offer a valuable tool to organize, manage, and analyze this data, leading to increased demand for this software.

Cloud Adoption and Scalability: The proliferation of cloud computing has significantly impacted the relational database market. Cloud-based database solutions offer scalability, flexibility, and reduced IT infrastructure burden for businesses. This makes them particularly attractive for small and medium-sized enterprises (SMEs) and facilitates easier data access for geographically dispersed teams.

Growing Importance of Data Security and Compliance: Data breaches and cyberattacks pose significant threats to businesses. Relational database software vendors are constantly innovating to enhance security features like encryption and access controls. Additionally, stringent data privacy regulations like GDPR and CCPA are driving the need for compliant data storage and management solutions, further propelling the market for secure relational databases.

IPEDS collects data on postsecondary education in the United States in seven areas: institutional characteristics, institutional prices, enrollment, student financial aid, degrees and certificates conferred, student persistence and success, and institutional human and fiscal resources. IPEDS collects institutional data on human resources and finances. Finance data includes institutional revenues by source, expenditures by category, and assets and liabilities. This information provides context for understanding the cost of providing postsecondary education. It is used to calculate the contribution of postsecondary education to the gross national product. IPEDS collects finance data conforming to the accounting standards that govern public and private institutions. Generally, private institutions use standards established by the Financial Accounting Standards Board (FASB) and public institutions use standards established by the Governmental Accounting Standards Board (GASB).

[198+ Pages Report] Global graph database market size & share estimated to be worth USD 5.2 Billion in the year 2026, growing at a CAGR value of 21.7% during the forecast period of 2021-2026.

This data is pulled from the annual Financial Trends Monitoring System (FTMS). The KCSTAT dashboards for Finance and Governance provide visualizations of this data, along with narrative to explain what these indicators mean.

The FTMS report itself goes also provides a great deal of narrative, and can be viewed by visiting: https://kcstat.kcmo.org/Finance/2013-FTMS-7-29-14-Revised/n7p4-kkha

Phoenix is a commercial off-the-shelf, web-based financial management system configured for USAID. Phoenix provides information about commitments, obligations, and expenditures. It is based on Momentum, CGI Federal's financial software. Phoenix is the Agency's integrated core financial system used by USAID/Washington and 51 Missions. Phoenix is USAID's core financial management system, which manages and tracks more than $15 billion each year in funding. It provides critical business functions such as general ledger, accounts payable, accounts receivable, cost accounting, budgeting, program operations and reporting. Phoenix is used to record accounting transactions and make payments for goods or services to small businesses, educational or non-profit institutions, and USAID contractors.

IPEDS collects data on postsecondary education in the United States in seven areas: institutional characteristics, institutional prices, enrollment, student financial aid, degrees and certificates conferred, student persistence and success, and institutional human and fiscal resources. IPEDS collects institutional data on human resources and finances. Finance data includes institutional revenues by source, expenditures by category, and assets and liabilities. This information provides context for understanding the cost of providing postsecondary education. It is used to calculate the contribution of postsecondary education to the gross national product. IPEDS collects finance data conforming to the accounting standards that govern public and private institutions. Generally, private institutions use standards established by the Financial Accounting Standards Board (FASB) and public institutions use standards established by the Governmental Accounting Standards Board (GASB).

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...

The Global Financial Development Database is an extensive dataset of financial system characteristics for 203 economies. The database includes measures of (1) size of financial institutions and markets (financial depth), (2) degree to which individuals can and do use financial services (access), (3) efficiency of financial intermediaries and markets in intermediating resources and facilitating financial transactions (efficiency), and (4) stability of financial institutions and markets (stability).

For a complete description of the dataset and a discussion of the underlying literature, see: Martin Čihák, Aslı Demirgüç-Kunt, Erik Feyen, and Ross Levine, 2012. "Benchmarking Financial Systems Around the World." World Bank Policy Research Working Paper 6175, World Bank, Washington, D.C.

Problem Statement

👉 Download the case studies here

A financial services firm faced inefficiencies in generating accurate and timely financial reports. The manual reporting process was labor-intensive, prone to errors, and delayed decision-making. With increasing data complexity and regulatory requirements, the firm sought an automated solution to streamline financial reporting while maintaining high accuracy.

Challenge

Implementing an automated financial reporting system involved addressing the following challenges:

Aggregating and consolidating large volumes of financial data from disparate sources in real time.

Ensuring compliance with regulatory standards and industry practices.

Generating detailed, accurate reports with contextual analysis and insights.

Solution Provided

An AI-powered financial reporting system was developed using advanced data aggregation tools and Natural Language Generation (NLG) technology. The solution was designed to:

Collect and consolidate financial data from multiple systems and databases.

Analyze data to detect trends, anomalies, and key performance indicators (KPIs).

Generate professional-quality financial reports in real time with contextual narratives.

Development Steps

Data Collection

Connected to financial systems, ERP platforms, and databases to aggregate data related to revenue, expenses, assets, and liabilities.

Preprocessing

Standardized data formats, resolved inconsistencies, and ensured compliance with financial reporting standards.

Model Development

Built AI models to analyze financial data, identify patterns, and calculate KPIs. Integrated NLG algorithms to transform data into coherent and contextually accurate narratives.

Validation

Tested the system by comparing generated reports with manually created ones to ensure accuracy and reliability.

Deployment

Implemented the solution across the organization, providing real-time reporting capabilities to finance teams and executives.

Continuous Monitoring & Improvement

Established a feedback loop to refine algorithms based on user inputs and evolving reporting requirements.

Results

Reduced Reporting Time

Automated workflows reduced the time required to generate financial reports by 70%, enabling faster decision-making.

Minimized Human Errors

AI-driven data aggregation and analysis eliminated manual errors, ensuring consistent and accurate reporting.

Real-Time Financial Insights

The system provided instant access to financial metrics and trends, supporting proactive business strategies.

Improved Compliance

Automated checks ensured compliance with regulatory standards and reduced the risk of reporting inaccuracies.

Enhanced Productivity

Finance teams were freed from repetitive tasks, allowing them to focus on strategic financial planning and analysis.

Financial Research Software Market size was valued at USD 1.23 Billion in 2024 and is projected to reach USD 1.82 Billion by 2031, growing at a CAGR of 3.5% during the forecast period 2024-2031.

Global Financial Research Software Market Drivers

Growing Demand for Data Analytics: Increasing demand for data-driven insights and analytics in the financial sector drives the adoption of financial research software to analyze market trends, investment opportunities, risk factors, and financial performance metrics.

Technological Advancements: Ongoing advancements in financial research software, including artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and big data analytics, enhance data processing capabilities, improve accuracy, and enable predictive modeling for investment decision-making.

Regulatory Compliance Requirements: Stringent regulatory requirements and compliance standards in the financial industry drive the adoption of financial research software to ensure regulatory compliance, risk management, and transparency in reporting and disclosure practices.

Investment Management and Portfolio Optimization: Financial research software enables investment professionals, portfolio managers, and asset allocators to conduct comprehensive research, perform quantitative analysis, and optimize investment portfolios to maximize returns and mitigate risks.

Rise of Robo-Advisors and Fintech Solutions: The rise of robo-advisors, digital wealth management platforms, and fintech solutions drives demand for financial research software with automated investment algorithms, portfolio rebalancing tools, and personalized financial advice for retail investors and wealth management clients.

Globalization and Market Integration: Globalization of financial markets and increased market integration drive the need for financial research software that provides real-time market data, news feeds, and economic indicators to support informed decision-making in a dynamic and interconnected marketplace.

Shift Towards ESG Investing: The growing focus on environmental, social, and governance (ESG) factors in investment decision-making drives demand for financial research software with ESG data integration, sustainability metrics, and impact analysis tools to support responsible investing strategies.

Risk Management and Stress Testing: Financial research software enables financial institutions and investment firms to conduct risk assessments, scenario analysis, and stress testing to evaluate portfolio resilience, liquidity risk, credit risk, and market volatility in various market conditions.

Alternative Data Sources and Quantitative Analysis: Financial research software integrates alternative data sources, such as social media sentiment, satellite imagery, and consumer behavior data, into quantitative models and analytical frameworks to gain insights into market trends and investment opportunities.

Demand for Customization and Integration: Financial institutions and investment professionals seek customizable financial research software solutions that can be tailored to their specific needs, integrated with existing systems and workflows, and scalable to accommodate future growth and expansion.

Abstract

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.

Geographic coverage

National Coverage. Sample excludes Northeast states and remote islands. In addition, some districts in Assam, Bihar, Jammu and Kashmir, Jharkhand, and Uttar Pradesh were replaced because of security concerns. The excluded areas represent less than 10% of the population.

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. 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 by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in India was 3,000 individuals.

Mode of data collection

Computer Assisted Personal Interview [capi]

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 multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

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.

This data collection contains information on finances for a sample of postsecondary institutions in the United States. Data on financial characteristics of postsecondary institutions are taken from Finance and Consolidated surveys, collected annually. The finance data are used for reporting and projecting capital outlays of two-year and four-year colleges and universities, trends in replacements of plant assets, and performance of endowment funds. Part 1, Institutional Characteristics, includes variables on control and level of institution, religious affiliation, highest level of offering, Carnegie classification, and state FIPS codes and abbreviations. Part 2, Current Funds Revenues by Source (Part A of the survey), provides each institution's current fund revenues by source (e.g., tuition and fees, government, gifts). Part 3, Current Funds Expenditures by Function (Part B), covers expenditures for instruction, research, and plant maintenance. Part 4, Clarifying Questions (Part C), contains information on total E&G revenues and expenditures to determine what is included/excluded from reported current fund expenditures. Part 5, Clarifying Question 5 (Part C5), lists excluded financial activities by subentities. Part 6, Utility Expenditures (Part D), reports all expenditures for utilities in the operation and maintenance of the plant, auxiliary enterprises, and independent operations, excluding expenditures for hospitals. Part 7, Scholarships and Fellowship Expenditures (Part E), covers scholarships, defined as grant-in-aid, trainee stipends, tuition and fee waivers, prizes to undergraduate students, and fellowships given to graduate students. Part 8, Expenditures for Library Acquisitions (Part F), covers costs involved in acquisition of library materials. Part 9, Indebtedness on Physical Plant (Part G), reports data on indebtedness liability against the physical plant, including auxiliary enterprises facilities as well as educational and general facilities, and excluding debt issued and backed by the state government. Part 10, Details of Endowment Assets (Part H), provides information on the amounts of gross investments of endowment, term endowment, and funds functioning as endowment for the institution, and any of its foundations and other affiliated organizations. Part 11, Selected Funds Balances (Part I), includes both unrestricted and restricted funds balances. Part 12, Hospital Revenues (Part J), reports the revenues for, or generated by, major public service hospitals over which the institution has fiscal control (excluding medical schools). Part 13, Physical Plant Assets (Part K), reports the values of land, buildings, and equipment owned, rented, or used by the institution. Part 14, Consolidated Form (CN) data (Part CN), includes revenues from tuition and fees, federal, state, and local grants, contracts, and sales of educational services. It also includes instructional expenditures, scholarships, and fellowships by source of financial aid.

IPEDS collects data on postsecondary education in the United States in seven areas: institutional characteristics, institutional prices, enrollment, student financial aid, degrees and certificates conferred, student persistence and success, and institutional human and fiscal resources. IPEDS collects institutional data on human resources and finances. Finance data includes institutional revenues by source, expenditures by category, and assets and liabilities. This information provides context for understanding the cost of providing postsecondary education. It is used to calculate the contribution of postsecondary education to the gross national product. IPEDS collects finance data conforming to the accounting standards that govern public and private institutions. Generally, private institutions use standards established by the Financial Accounting Standards Board (FASB) and public institutions use standards established by the Governmental Accounting Standards Board (GASB).

Abstract

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.

Geographic coverage

National coverage

Analysis unit

Individual

Universe

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

Kind of data

Observation data/ratings [obs]

Sampling procedure

The indicators in the 2017 Global Findex database are drawn from survey data covering almost 150,000 people in 144 economies-representing more than 97 percent of the world's population (see Table A.1 of the Global Findex Database 2017 Report). The survey was carried out over the 2017 calendar year by Gallup, Inc., as part of its Gallup World Poll, which since 2005 has annually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 150 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. Interview procedure Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or where this is the customary methodology. In most economies the fieldwork is completed in two to four weeks.

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 handheld 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 economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or household enumeration method. At least three attempts are made to reach a person in each household, spread over different days and times of day.\

The sample size was 1005.

Mode of data collection

Other [oth]

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 multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in more than 140 languages upon request.

Questions on cash on delivery, saving using an informal savings club or person outside the family, domestic remittances, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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, and Jake Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank

Ecuador Pvt Financial System Loan Portfolio: Financial Companies data was reported at 13,868.000 USD th in Sep 2017. This records a decrease from the previous number of 22,759.000 USD th for Aug 2017. Ecuador Pvt Financial System Loan Portfolio: Financial Companies data is updated monthly, averaging 1,129,219.000 USD th from Oct 2006 (Median) to Sep 2017, with 132 observations. The data reached an all-time high of 1,426,432.000 USD th in Mar 2015 and a record low of 13,868.000 USD th in Sep 2017. Ecuador Pvt Financial System Loan Portfolio: Financial Companies data remains active status in CEIC and is reported by Superintendence of Banks and Insurance of Ecuador. The data is categorized under Global Database’s Ecuador – Table EC.KB004: Private Financial System: Loan Portfolio.

United States US: Financial Corporations Survey: Foreign Assets: Net data was reported at 359.175 USD bn in 2008. This records a decrease from the previous number of 983.947 USD bn for 2007. United States US: Financial Corporations Survey: Foreign Assets: Net data is updated yearly, averaging 38.874 USD bn from Dec 1952 (Median) to 2008, with 57 observations. The data reached an all-time high of 983.947 USD bn in 2007 and a record low of -39.257 USD bn in 1994. United States US: Financial Corporations Survey: Foreign Assets: Net data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s United States – Table US.IMF.IFS: Financial System: Annual.