100+ datasets found
  1. w

    Global Financial Inclusion (Global Findex) Database 2021 - United Kingdom

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Dec 16, 2022
    + more versions
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    Development Research Group, Finance and Private Sector Development Unit (2022). Global Financial Inclusion (Global Findex) Database 2021 - United Kingdom [Dataset]. https://microdata.worldbank.org/index.php/catalog/4723
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    Dataset updated
    Dec 16, 2022
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2021
    Area covered
    United Kingdom
    Description

    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 United Kingdom is 1000.

    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.

  2. Financial Data Service Providers in the US - Market Research Report...

    • ibisworld.com
    Updated Jan 15, 2025
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    IBISWorld (2025). Financial Data Service Providers in the US - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/united-states/industry/financial-data-service-providers/5491/
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    Dataset updated
    Jan 15, 2025
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2015 - 2030
    Area covered
    United States
    Description

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

  3. w

    Global Financial Inclusion (Global Findex) Database 2021 - Eswatini

    • microdata.worldbank.org
    • datacatalog.ihsn.org
    Updated Jun 8, 2023
    + more versions
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    Development Research Group, Finance and Private Sector Development Unit (2023). Global Financial Inclusion (Global Findex) Database 2021 - Eswatini [Dataset]. https://microdata.worldbank.org/index.php/catalog/5852
    Explore at:
    Dataset updated
    Jun 8, 2023
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2022
    Area covered
    Eswatini
    Description

    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 almost 145,000 people in 139 economies, representing 97 percent of the world’s population. 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

    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. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

    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 Eswatini 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.

  4. Data compromises in the U.S. financial services sector 2019-2023

    • statista.com
    Updated Dec 10, 2024
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    Statista (2024). Data compromises in the U.S. financial services sector 2019-2023 [Dataset]. https://www.statista.com/statistics/1318486/us-number-of-data-loss-incidents-in-financial-sector/
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    Dataset updated
    Dec 10, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, the number of data compromises in the financial services industry in the United States reached 744, up from 138 such incidents in 2020. The financial services sector was the second-most targeted industry by cyber security incidents resulting in data compromise. The number of data compromises includes data breaches, as well as exposure and leakage of private data.

  5. w

    Global Financial Inclusion (Global Findex) Database 2021 - Afghanistan

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Dec 16, 2022
    + more versions
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    Development Research Group, Finance and Private Sector Development Unit (2022). Global Financial Inclusion (Global Findex) Database 2021 - Afghanistan [Dataset]. https://microdata.worldbank.org/index.php/catalog/4608
    Explore at:
    Dataset updated
    Dec 16, 2022
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2021
    Area covered
    Afghanistan
    Description

    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

    Gender-matched sampling was used during the final stage of selection.

    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 Afghanistan is 1002.

    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.

  6. f

    LBV Financial Services | Tourist Attractions Data | Travel & Hospitality...

    • datastore.forage.ai
    Updated Sep 24, 2024
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    (2024). LBV Financial Services | Tourist Attractions Data | Travel & Hospitality Data [Dataset]. https://datastore.forage.ai/searchresults/?resource_keyword=Financial%20Data
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    Dataset updated
    Sep 24, 2024
    Description

    LBV Financial Services, a renowned provider of financial solutions, offers a wide range of data that can be valuable for market analysis and research. From stock prices to economic indicators, their database caters to the needs of finance enthusiasts and professionals alike.

    With a strong focus on providing accurate and up-to-date information, LBV Financial Services has established itself as a trusted source for financial data. Their collection includes various financial metrics, market trends, and company profiles, making it an essential resource for those seeking to stay ahead in the ever-changing financial landscape.

  7. f

    Olympus Financial Services | Arts & Entertainment | Media & Entertainment...

    • datastore.forage.ai
    Updated Sep 24, 2024
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    (2024). Olympus Financial Services | Arts & Entertainment | Media & Entertainment Data [Dataset]. https://datastore.forage.ai/searchresults/?resource_keyword=Financial%20Data
    Explore at:
    Dataset updated
    Sep 24, 2024
    Description

    Olympus Financial Services is a leading provider of financial data, offering a wealth of information on market trends, economic indicators, and company financials. The company's website provides a robust platform for accessing this data, making it an essential resource for financial analysts, researchers, and investors.

    With a strong focus on delivering accurate and timely data, Olympus Financial Services has established itself as a trusted name in the financial industry. Its comprehensive data sets cover a wide range of topics, from stock market performance to macroeconomic indicators, providing valuable insights for decision-making.

  8. T

    Denmark - Insurance And Financial Services (% Of Commercial Service Imports)...

    • tradingeconomics.com
    csv, excel, json, xml
    Updated May 29, 2017
    + more versions
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    TRADING ECONOMICS (2017). Denmark - Insurance And Financial Services (% Of Commercial Service Imports) [Dataset]. https://tradingeconomics.com/denmark/insurance-and-financial-services-percent-of-commercial-service-imports-wb-data.html
    Explore at:
    json, excel, csv, xmlAvailable download formats
    Dataset updated
    May 29, 2017
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 1976 - Dec 31, 2025
    Area covered
    Denmark
    Description

    Insurance and financial services (% of commercial service imports) in Denmark was reported at 1.6661 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Denmark - Insurance and financial services (% of commercial service imports) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

  9. Most popular AI workloads in financial services globally 2023-2024

    • statista.com
    Updated Jun 20, 2025
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    Statista (2025). Most popular AI workloads in financial services globally 2023-2024 [Dataset]. https://www.statista.com/statistics/1374567/top-ai-use-cases-in-financial-services-global/
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    Dataset updated
    Jun 20, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    Data analytics maintained its position as the leading AI application among financial services firms in 2024. A 2024 industry survey indicated that ** percent of companies leveraged AI for data analytics, showing modest growth from the previous year. Generative AI experienced the strongest year-over-year adoption increase, becoming the second most widely used AI technology, with more than half of firms either implementing or evaluating the technology. Reflecting this growing embrace of AI solutions, the financial sector's investment in AI technologies continues to surge, with spending projected to reach over ** billion U.S. dollars in 2025 and more than double to *** billion U.S. dollars by 2028. The main benefits of AI in the financial services sector Financial services firms reported that AI delivered the greatest value through operational efficiencies, according to a 2024 industry survey. The technology also provided significant competitive advantages, cited by ** percent of respondents as a key benefit. Enhanced customer experience emerged as the third most important advantage of AI adoption in the sector. Adoption across business segments The integration of AI varies across different areas of financial services. In 2023, operations lead the way with a ** percent adoption rate, closely followed by risk and compliance at ** percent. In customer experience and marketing, voice assistants, chatbots, and conversational AI are the most common AI applications. Meanwhile, financial reporting and accounting dominate AI use in operations and finance.

  10. f

    Francis Financial | Finance | Finance & Banking Data

    • datastore.forage.ai
    Updated Sep 24, 2024
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    (2024). Francis Financial | Finance | Finance & Banking Data [Dataset]. https://datastore.forage.ai/searchresults/?resource_keyword=Financial%20Data
    Explore at:
    Dataset updated
    Sep 24, 2024
    Description

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

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

  11. e

    Big Data Analytics in BFSI Market Size USD 58.7 Billion by 2034 | Big Data...

    • emergenresearch.com
    pdf,excel,csv,ppt
    Updated Jul 9, 2025
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    Emergen Research (2025). Big Data Analytics in BFSI Market Size USD 58.7 Billion by 2034 | Big Data Analytics in Banking, financial services and insurance Market Growth 12.1% CAGR [Dataset]. https://www.emergenresearch.com/industry-report/big-data-analytics-in-bfsi-market
    Explore at:
    pdf,excel,csv,pptAvailable download formats
    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Emergen Research
    License

    https://www.emergenresearch.com/privacy-policyhttps://www.emergenresearch.com/privacy-policy

    Area covered
    Global
    Variables measured
    Base Year, No. of Pages, Growth Drivers, Forecast Period, Segments covered, Historical Data for, Pitfalls Challenges, 2034 Value Projection, Tables, Charts, and Figures, Forecast Period 2024 - 2034 CAGR, and 1 more
    Description

    The big data analytics in BFSI market size was USD 18.9 Billion in 2024 and is expected to reach USD 58.7 Billion in 2034 and register a CAGR of 12.1%. Big Data Analytics in Banking, financial services and insurance industry report classifies global market by share, trend, and on the basis of type,...

  12. w

    Global Financial Inclusion (Global Findex) Database 2021 - Bolivia

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Dec 16, 2022
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    Development Research Group, Finance and Private Sector Development Unit (2022). Global Financial Inclusion (Global Findex) Database 2021 - Bolivia [Dataset]. https://microdata.worldbank.org/index.php/catalog/4618
    Explore at:
    Dataset updated
    Dec 16, 2022
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2021
    Area covered
    Bolivia
    Description

    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 Bolivia is 1000.

    Mode of data collection

    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.

  13. F

    U.S. Imports of Services: Financial Services

    • fred.stlouisfed.org
    json
    Updated Sep 4, 2025
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    (2025). U.S. Imports of Services: Financial Services [Dataset]. https://fred.stlouisfed.org/series/ITMFISM133S
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Sep 4, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    Graph and download economic data for U.S. Imports of Services: Financial Services (ITMFISM133S) from Jan 1999 to Jul 2025 about imports, financial, services, and USA.

  14. p

    Professional Financial Services Locations Data for United States

    • poidata.io
    csv, json
    Updated Sep 9, 2025
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    Business Data Provider (2025). Professional Financial Services Locations Data for United States [Dataset]. https://poidata.io/brand-report/professional-financial-services/united-states
    Explore at:
    csv, jsonAvailable download formats
    Dataset updated
    Sep 9, 2025
    Dataset authored and provided by
    Business Data Provider
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2025
    Area covered
    United States
    Variables measured
    Website URL, Phone Number, Review Count, Business Name, Email Address, Business Hours, Customer Rating, Business Address, Brand Affiliation, Geographic Coordinates
    Description

    Comprehensive dataset containing 43 verified Professional Financial Services locations in United States with complete contact information, ratings, reviews, and location data.

  15. w

    Global Financial Inclusion (Global Findex) Database 2021 - Honduras

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Dec 16, 2022
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    Development Research Group, Finance and Private Sector Development Unit (2022). Global Financial Inclusion (Global Findex) Database 2021 - Honduras [Dataset]. https://microdata.worldbank.org/index.php/catalog/4649
    Explore at:
    Dataset updated
    Dec 16, 2022
    Dataset authored and provided by
    Development Research Group, Finance and Private Sector Development Unit
    Time period covered
    2021
    Area covered
    Honduras
    Description

    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 Honduras 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.

  16. B

    Brazil GOI: Financial Services

    • ceicdata.com
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    CEICdata.com, Brazil GOI: Financial Services [Dataset]. https://www.ceicdata.com/en/brazil/global-opportunity-index/goi-financial-services
    Explore at:
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2016 - Dec 1, 2023
    Area covered
    Brazil
    Variables measured
    Economic Outlook Survey
    Description

    Brazil GOI: Financial Services data was reported at 35.000 NA in 2023. This records a decrease from the previous number of 39.000 NA for 2022. Brazil GOI: Financial Services data is updated yearly, averaging 37.000 NA from Dec 2016 (Median) to 2023, with 8 observations. The data reached an all-time high of 45.000 NA in 2021 and a record low of 9.000 NA in 2016. Brazil GOI: Financial Services data remains active status in CEIC and is reported by Milken Institute. The data is categorized under Global Database’s Brazil – Table BR.Milken: Global Opportunity Index.

  17. B2B Email Data | US Financial Services | Verified Profiles & Key Contact...

    • datarade.ai
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    Success.ai, B2B Email Data | US Financial Services | Verified Profiles & Key Contact Details | Best Price Guaranteed [Dataset]. https://datarade.ai/data-products/b2b-email-data-us-financial-services-verified-profiles-success-ai
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset provided by
    Area covered
    United States
    Description

    Success.ai’s B2B Email Data for US Financial Services offers businesses comprehensive access to verified email addresses and contact details of key decision-makers across the financial services industry in the United States.

    Sourced from over 170 million verified professional profiles and enriched with detailed firmographic data, this dataset is ideal for sales teams, marketers, and strategic planners looking to engage with banking executives, wealth managers, insurance specialists, and fintech leaders.

    Backed by our Best Price Guarantee, Success.ai ensures that your outreach is guided by accurate, continuously updated, and AI-validated data.

    Why Choose Success.ai’s Financial Services Email Data?

    1. Verified B2B Email Data for Precision Outreach

      • Access verified work emails of decision-makers in banking, insurance, wealth management, investment firms, and fintech startups.
      • AI-driven validation ensures 99% accuracy, reducing bounce rates and ensuring high deliverability for your campaigns.
    2. Focus on the US Financial Market

      • Includes profiles of professionals across major US financial hubs like New York, Chicago, San Francisco, and Miami, as well as regional banks, credit unions, and fintech disruptors.
      • Gain insights into industry trends, regulatory impacts, and market dynamics specific to the US financial ecosystem.
    3. Continuously Updated Datasets

      • Real-time updates ensure that your data remains relevant, reflecting leadership changes, mergers, acquisitions, and new market entrants.
      • Stay aligned with evolving industry demands and customer needs.
    4. Ethical and Compliant

      • Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible data usage and legal compliance for your campaigns.

    Data Highlights:

    • 170M+ Verified Professional Profiles: Engage with executives, financial advisors, compliance officers, and analysts across the US financial services sector.
    • 50M Work Emails: AI-validated email data ensures precise communication and minimized email bounce rates.
    • Firmographic Insights: Understand company sizes, revenue ranges, service offerings, and geographic presence to refine your targeting strategies.
    • Decision-Maker Contact Details: Connect directly with key influencers and leaders shaping the US financial landscape.

    Key Features of the Dataset:

    1. Decision-Maker Email Profiles

      • Identify and engage with CEOs, CFOs, financial planners, compliance managers, and marketing directors responsible for driving financial strategies and regulatory compliance.
      • Target professionals overseeing technology adoption, customer engagement, and portfolio growth.
    2. Advanced Filters for Tailored Campaigns

      • Filter contacts by industry segment (banking, insurance, investment management), company size, geographic location, or revenue bracket.
      • Tailor outreach efforts to align with specific financial services challenges, regulatory pressures, or customer preferences.
    3. AI-Driven Enrichment

      • Profiles enriched with actionable data provide deeper insights, enabling personalized messaging and improving engagement outcomes with financial services stakeholders.

    Strategic Use Cases:

    1. Sales and Lead Generation

      • Offer SaaS solutions, compliance tools, or digital transformation services to financial services providers aiming to modernize operations and enhance customer experiences.
      • Build relationships with decision-makers in charge of vendor selection, procurement, and operational strategies.
    2. Marketing and Outreach Campaigns

      • Target marketing teams and customer experience professionals to promote data-driven marketing tools, CRM platforms, or loyalty programs tailored to financial clients.
      • Leverage verified email data for multi-channel campaigns, driving higher engagement rates and conversions.
    3. Fintech and Innovation Partnerships

      • Engage with fintech executives and banking leaders exploring digital payments, blockchain, AI-driven financial products, or open banking solutions.
      • Foster partnerships that accelerate innovation and enhance competitive positioning.
    4. Regulatory Compliance and Risk Management

      • Connect with compliance officers and risk managers to present regulatory reporting tools, fraud detection systems, or cybersecurity solutions.
      • Address key pain points related to evolving compliance requirements and risk mitigation.

    Why Choose Success.ai?

    1. Best Price Guarantee

      • Access premium-quality B2B email data at competitive rates, ensuring maximum ROI for your outreach, marketing, and sales campaigns in the US financial sector.
    2. Seamless Integration

      • Incorporate verified email data into CRM systems or marketing automation platforms via APIs or downloadable formats, streamlining data management and campaign execution.
    3. Data Accuracy with AI Validation
      ...

  18. G

    Alternative Data Integration for Financial Services Market Research Report...

    • growthmarketreports.com
    csv, pdf, pptx
    Updated Aug 22, 2025
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    Growth Market Reports (2025). Alternative Data Integration for Financial Services Market Research Report 2033 [Dataset]. https://growthmarketreports.com/report/alternative-data-integration-for-financial-services-market
    Explore at:
    csv, pdf, pptxAvailable download formats
    Dataset updated
    Aug 22, 2025
    Dataset authored and provided by
    Growth Market Reports
    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Alternative Data Integration for Financial Services Market Outlook



    According to our latest research, the global market size for Alternative Data Integration for Financial Services reached USD 5.4 billion in 2024, with a robust CAGR of 18.7% projected through the forecast period. By 2033, the market is expected to attain a value of USD 27.5 billion, highlighting the extraordinary momentum in the adoption of alternative data sources across financial services. This growth is primarily driven by the increasing demand for actionable insights, competitive differentiation, and the need for advanced analytics in an evolving regulatory and technological landscape.




    One of the key growth factors propelling the Alternative Data Integration for Financial Services market is the surging volume and variety of non-traditional data sources. Financial institutions are increasingly leveraging data from social media, geospatial sensors, satellite imagery, and web scraping to gain deeper and more nuanced insights into market trends, consumer behavior, and macroeconomic environments. This shift is motivated by the limitations of conventional financial data, which often fails to provide real-time or predictive analytics. As a result, firms are investing heavily in data integration platforms that can process and harmonize these disparate data sets, enabling more informed decision-making and risk assessment. The growing sophistication of machine learning and artificial intelligence technologies further enhances the ability to extract value from alternative data, making it an indispensable asset for modern financial strategies.




    Another significant driver is the heightened focus on risk management and fraud detection within the financial sector. With increasing regulatory scrutiny and the rising complexity of financial crimes, institutions are turning to alternative data to detect anomalies and patterns that traditional data might miss. For example, geospatial and satellite data can help assess the physical risks to assets, while social media analytics can provide early warning signals for reputational risks or market sentiment shifts. The integration of these data types into risk management frameworks allows for more proactive and dynamic responses to emerging threats. Moreover, alternative data is being used to refine credit scoring models, especially for underbanked populations, thereby expanding access to financial products and services and opening new avenues for growth.




    Cloud-based deployment models are playing a pivotal role in the market’s expansion, offering scalability, flexibility, and cost-efficiency for financial institutions of all sizes. The cloud enables seamless integration and real-time processing of vast volumes of alternative data, which is critical for applications such as algorithmic trading and portfolio management. Additionally, cloud platforms facilitate collaboration and data sharing across global teams, accelerating innovation and reducing time-to-market for new financial products. The growing adoption of cloud solutions is also democratizing access to alternative data analytics, enabling smaller banks, hedge funds, and asset management firms to compete with larger players. This trend is expected to intensify as cloud security and compliance capabilities continue to mature, further boosting market growth.




    Regionally, North America remains the dominant market for alternative data integration in financial services, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is at the forefront due to its advanced financial ecosystem, early adoption of big data technologies, and a vibrant fintech sector. Europe is witnessing rapid growth, driven by stringent regulatory requirements and increasing investments in digital transformation. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by the proliferation of digital banking, expanding fintech startups, and a large underbanked population that benefits from alternative credit scoring models. The regional landscape is further enriched by the growing participation of Latin America and the Middle East & Africa, where financial innovation is accelerating in response to unique market challenges and opportunities.



  19. a

    Financial Services Acquisitions Database

    • acquirezy.com
    Updated Sep 14, 2025
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    Acquirezy (2025). Financial Services Acquisitions Database [Dataset]. https://acquirezy.com/acquisitions/industry/financial-services
    Explore at:
    Dataset updated
    Sep 14, 2025
    Dataset authored and provided by
    Acquirezy
    Description

    Comprehensive database of mergers and acquisitions in the Financial Services industry

  20. d

    Banking and Financial Services POI Data

    • datarade.ai
    Updated Jan 1, 2022
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    Lepton Software (2022). Banking and Financial Services POI Data [Dataset]. https://datarade.ai/data-products/banking-and-financial-services-poi-data-lepton-software
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Jan 1, 2022
    Dataset authored and provided by
    Lepton Software
    Area covered
    India
    Description

    The Banking and Financial Services collection of data includes locations and attributive information. Interest such as Commercial banks, Credit and Leasing companies.

    Attributes:

    Name, Address, Latitude, Longitude, Category, Type of BSFI

    The Banking and Financial Services collection of data includes locations and attributive information. Interest such as Commercial banks, Credit and Leasing companies.

    Attributes:

    Name, Address, Latitude, Longitude, Category, Type of BSFI

Share
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Click to copy link
Link copied
Close
Cite
Development Research Group, Finance and Private Sector Development Unit (2022). Global Financial Inclusion (Global Findex) Database 2021 - United Kingdom [Dataset]. https://microdata.worldbank.org/index.php/catalog/4723

Global Financial Inclusion (Global Findex) Database 2021 - United Kingdom

Explore at:
Dataset updated
Dec 16, 2022
Dataset authored and provided by
Development Research Group, Finance and Private Sector Development Unit
Time period covered
2021
Area covered
United Kingdom
Description

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 United Kingdom is 1000.

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.

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