Mexico MX: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Male: % of Population Aged 15+ data was reported at 41.099 % in 2017. This records an increase from the previous number of 39.400 % for 2014. Mexico MX: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Male: % of Population Aged 15+ data is updated yearly, averaging 39.400 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 41.099 % in 2017 and a record low of 33.192 % in 2011. Mexico MX: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Male: % of Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (male, % age 15+).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Togo TG: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15+ data was reported at 45.289 % in 2017. This records an increase from the previous number of 18.251 % for 2014. Togo TG: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15+ data is updated yearly, averaging 18.251 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 45.289 % in 2017 and a record low of 10.185 % in 2011. Togo TG: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Togo – Table TG.World Bank.WDI: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (% age 15+).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Data Access Policy Management Market Outlook

According to our latest research, the global Data Access Policy Management market size in 2024 stands at USD 2.3 billion, reflecting the growing prioritization of data security and compliance across industries. The market is experiencing robust expansion, with a projected CAGR of 13.2% from 2025 to 2033. By 2033, the market is forecasted to reach an impressive USD 6.7 billion. This growth is primarily driven by increasing regulatory requirements, the rapid adoption of cloud technologies, and the ever-expanding digital footprint of organizations worldwide. As per our latest research, organizations are investing heavily in advanced data access policy management solutions to ensure secure, compliant, and efficient access to critical data assets.

A key growth factor for the Data Access Policy Management market is the intensifying regulatory landscape. With the introduction and enforcement of data protection regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA), organizations are under immense pressure to manage and monitor data access efficiently. These regulations mandate strict controls over who can access sensitive data, how access is granted, and how access activities are audited. Non-compliance can result in severe financial penalties and reputational damage, prompting organizations across sectors to invest in comprehensive data access policy management solutions. The demand for automated policy enforcement, real-time monitoring, and detailed audit trails is higher than ever, spurring innovation and adoption in this market.

Another significant driver is the accelerated adoption of cloud computing and hybrid IT environments. As organizations migrate their workloads to public and private clouds, the complexity of managing data access policies across diverse platforms increases exponentially. Traditional access management approaches often fall short in these dynamic environments, necessitating more sophisticated, centralized solutions that can enforce consistent policies regardless of where data resides. The need to support remote workforces and facilitate secure collaboration further amplifies the demand for robust data access policy management tools. These solutions not only help organizations maintain control over their data but also enhance operational agility by enabling secure, role-based access to information assets.

Furthermore, the proliferation of digital transformation initiatives is fueling market growth. Enterprises are leveraging big data, artificial intelligence, and Internet of Things (IoT) technologies to gain competitive advantage, resulting in a dramatic increase in data volume and diversity. Managing access to this expanding data landscape requires scalable and flexible policy management frameworks. Organizations are seeking solutions that can integrate seamlessly with existing identity and access management (IAM) systems, support granular policy definition, and provide real-time insights into access activities. The integration of advanced analytics and machine learning capabilities into data access policy management solutions is enabling proactive risk identification and policy optimization, further driving market expansion.

From a regional perspective, North America continues to dominate the Data Access Policy Management market, owing to the presence of leading technology providers, stringent regulatory requirements, and high awareness of data security best practices. Europe follows closely, driven by strong regulatory enforcement and increasing digitalization across industries. The Asia Pacific region is witnessing the fastest growth, propelled by rapid economic development, increasing digital adoption, and evolving regulatory frameworks. Latin America and the Middle East & Africa are also emerging as promising markets, as organizations in these regions ramp up their investments in data security and compliance infrastructure. The global nature of data flows and the interconnectedness of business ecosystems underscore the importance of robust data access policy management across all regions.

Component Analysis

The Data Access Policy Management market is segmented by component into software and services, each playing a pivotal role in the overall value proposition. The software segment encompasses standalone policy management platforms as well

Slovakia SK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15-24 data was reported at 54.582 % in 2017. This records an increase from the previous number of 37.638 % for 2014. Slovakia SK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15-24 data is updated yearly, averaging 54.582 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 59.480 % in 2011 and a record low of 37.638 % in 2014. Slovakia SK: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovakia – Table SK.World Bank.WDI: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

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

Portugal PT: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Secondary Education Or More: % of Population Aged 15+ data was reported at 95.502 % in 2017. This records a decrease from the previous number of 95.641 % for 2014. Portugal PT: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Secondary Education Or More: % of Population Aged 15+ data is updated yearly, averaging 95.502 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 95.641 % in 2014 and a record low of 86.092 % in 2011. Portugal PT: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: Secondary Education Or More: % of Population Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Portugal – Table PT.World Bank: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (secondary education or more, % of population ages 15+).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted Average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Data Access Control for Analytics Market Outlook

According to our latest research, the global Data Access Control for Analytics market size reached USD 4.7 billion in 2024, with a robust year-on-year expansion fueled by rapid digital transformation across industries. The market is projected to grow at a CAGR of 15.2% from 2025 to 2033, culminating in a forecasted value of USD 16.4 billion by 2033. The primary growth driver is the increasing need for robust data security and compliance frameworks as organizations harness analytics for strategic decision-making.

The surge in digital data generation, coupled with the proliferation of advanced analytics platforms, is significantly driving the growth of the Data Access Control for Analytics market. As enterprises collect and process vast volumes of sensitive information, the risk of data breaches and unauthorized access has escalated. This heightened risk landscape has compelled organizations to invest in sophisticated data access control solutions that ensure only authorized personnel can access critical analytics resources. Moreover, the adoption of cloud-based analytics platforms has introduced new complexities to data governance, further amplifying the demand for granular access control mechanisms. The convergence of these factors is expected to sustain the market’s upward trajectory over the forecast period.

Another key growth factor is the tightening regulatory environment across major economies. Governments and industry regulators are imposing stringent data privacy and security mandates, such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and similar frameworks in Asia Pacific. These regulations require organizations to implement comprehensive data access control policies to ensure compliance and avoid hefty penalties. Consequently, enterprises are increasingly prioritizing investments in access control technologies that offer audit trails, policy management, and real-time monitoring of data access activities. This regulatory push is not only driving market growth but also fostering innovation among solution providers.

The growing integration of artificial intelligence (AI) and machine learning (ML) into analytics workflows is also shaping the Data Access Control for Analytics market. As organizations leverage AI-driven analytics to extract actionable insights from complex datasets, the need to secure sensitive models and data pipelines becomes paramount. AI-powered access control solutions are emerging, capable of dynamically adjusting permissions based on user behavior, risk profiles, and contextual factors. This evolution is particularly relevant for sectors like BFSI, healthcare, and government, where data sensitivity is exceptionally high. The convergence of AI, analytics, and access control is expected to unlock new market opportunities and accelerate adoption across verticals.

From a regional perspective, North America currently dominates the Data Access Control for Analytics market, accounting for the largest share in 2024. This leadership is attributed to the region’s advanced digital infrastructure, high adoption of analytics solutions, and a mature regulatory landscape. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid digitization, expanding cloud adoption, and increasing awareness of data privacy. Europe continues to show steady growth, underpinned by strict compliance requirements and a strong focus on data governance. Other regions, including Latin America and the Middle East & Africa, are gradually catching up as enterprises in these markets recognize the strategic value of secure analytics.

Component Analysis

The Data Access Control for Analytics market is segmented by component into Software, Hardware, and Services. The Software segment currently holds the largest market share, benefiting from the widespread adoption of analytics platforms and the growing complexity of data environments. Modern

Data Masking AI Market Outlook

According to our latest research, the global Data Masking AI market size reached USD 1.52 billion in 2024 and is expected to expand at a robust CAGR of 16.3% from 2025 to 2033. By the end of the forecast period, the market is projected to attain a valuation of USD 5.08 billion. The rapid market growth is primarily driven by the increasing need for advanced data privacy solutions in the face of stringent regulatory requirements and the widespread adoption of artificial intelligence technologies across industries.

One of the most significant growth factors for the Data Masking AI market is the rising tide of global data privacy regulations, such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and similar frameworks emerging in Asia and Latin America. These regulations mandate that organizations rigorously protect sensitive customer and business data, spurring investments in advanced data masking solutions powered by artificial intelligence. AI-driven data masking tools offer the ability to automate the anonymization and obfuscation of personally identifiable information (PII) and other sensitive data sets, reducing the operational burden on IT teams and ensuring compliance at scale. As organizations face increasing scrutiny from regulators and consumers alike, the adoption of AI-based data masking technologies is becoming not just a best practice but a business imperative.

Another key driver propelling the Data Masking AI market is the exponential growth in data volumes and the corresponding rise in cyber threats. Enterprises are generating and storing vast amounts of data across cloud, on-premises, and hybrid environments, making it increasingly challenging to secure sensitive information. AI-powered data masking solutions are uniquely positioned to address these challenges by automatically detecting sensitive data across disparate sources and applying dynamic masking policies in real time. This capability is particularly valuable in environments where data is frequently accessed for development, testing, analytics, and business intelligence, as it ensures that only non-sensitive, masked data is exposed to users, mitigating the risk of data breaches and insider threats.

The growing integration of AI in business processes, coupled with the demand for secure data sharing and analytics, is further accelerating the adoption of Data Masking AI solutions. Organizations are leveraging AI-driven data masking to enable secure data access for third-party vendors, partners, and remote employees without compromising data privacy. Additionally, the proliferation of digital transformation initiatives, especially in sectors such as BFSI, healthcare, and retail, is creating new opportunities for market expansion. As businesses increasingly rely on data-driven decision-making, the need to balance data utility with privacy protection is driving investment in sophisticated masking technologies that leverage machine learning and automation.

In the banking sector, Test Data Masking for Banking is becoming increasingly crucial as financial institutions handle vast amounts of sensitive customer information. With the rise of digital banking and online financial services, banks are under pressure to ensure that customer data is not only secure but also compliant with stringent regulations such as PCI DSS and GDPR. Test Data Masking for Banking allows these institutions to create realistic, non-sensitive datasets for testing and development purposes, ensuring that real customer data is never exposed during these processes. This approach not only enhances data security but also facilitates innovation by allowing developers to work with high-quality data without risking privacy breaches.

From a regional perspective, North America currently leads the global Data Masking AI market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of leading AI technology providers, a highly regulated business environment, and a strong emphasis on cybersecurity. Meanwhile, Asia Pacific is expected to witness the fastest growth during the forecast period, fueled by rapid digitalization, expanding regulatory frameworks, and increasing awareness of data priv

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

Sample excludes occupied and conflict areas in Donetsk and Lugansk oblasts. Theexcluded areas represent 10% of the population.

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 1000.

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 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

Access Market Research Intellect's Healthcare Provider Data Management Software Market Report for insights on a market worth USD 5.2 billion in 2024, expanding to USD 12.6 billion by 2033, driven by a CAGR of 10.2%.Learn about growth opportunities, disruptive technologies, and leading market participants.

Kenya KE: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15-24 data was reported at 76.021 % in 2017. This records an increase from the previous number of 66.357 % for 2014. Kenya KE: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15-24 data is updated yearly, averaging 66.357 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 76.021 % in 2017 and a record low of 40.272 % in 2011. Kenya KE: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

The National Bioscience Database Center (NBDC) intends to integrate all databases for life sciences in Japan, by linking each database with expediency to maximize convenience and make the entire system more user-friendly. We aim to focus our attention on the needs of the users of these databases who have all too often been neglected in the past, rather than the needs of the people tasked with the creation of databases. It is important to note that we will continue to honor the independent integrity of each database that will contribute to our endeavor, as we are fully aware that each database was originally crafted for specific purposes and divergent goals. Services: * Database Catalog - A catalog of life science related databases constructed in Japan that are also available in English. Information such as URL, status of the database site (active vs. inactive), database provider, type of data and subjects of the study are contained for each database record. * Life Science Database Cross Search - A service for simultaneous searching across scattered life-science databases, ranging from molecular data to patents and literature. * Life Science Database Archive - maintains and stores the datasets generated by life scientists in Japan in a long-term and stable state as national public goods. The Archive makes it easier for many people to search datasets by metadata in a unified format, and to access and download the datasets with clear terms of use. * Taxonomy Icon - A collection of icons (illustrations) of biological species that is free to use and distribute. There are more than 200 icons of various species including Bacteria, Fungi, Protista, Plantae and Animalia. * GenLibi (Gene Linker to bibliography) - an integrated database of human, mouse and rat genes that includes automatically integrated gene, protein, polymorphism, pathway, phenotype, ortholog/protein sequence information, and manually curated gene function and gene-related or co-occurred Disease/Phenotype and bibliography information. * Allie - A search service for abbreviations and long forms utilized in life sciences. It provides a solution to the issue that many abbreviations are used in the literature, and polysemous or synonymous abbreviations appear frequently, making it difficult to read and understand scientific papers that are not relevant to the reader's expertise. * inMeXes - A search service for English expressions (multiple words) that appear no less than 10 times in PubMed/MEDLINE titles or abstracts. In addition, you can easily access the sentences where the expression was used or other related information by clicking one of the search results. * HOWDY - (Human Organized Whole genome Database) is a database system for retrieving human genome information from 14 public databases by using official symbols and aliases. The information is daily updated by extracting data automatically from the genetic databases and shown with all data having the identifiers in common and linking to one another. * MDeR (the MetaData Element Repository in life sciences) - a web-based tool designed to let you search, compare and view Data Elements. MDeR is based on the ISO/IEC 11179 Part3 (Registry metamodel and basic attributes). * Human Genome Variation Database - A database for accumulating all kinds of human genome variations detected by various experimental techniques. * MEDALS - A portal site that provides information about databases, analysis tools, and the relevant projects, that were conducted with the financial support from the Ministry of Economy, Trade and Industry of Japan.

Techsalerator's Corporate Actions Dataset in Sri Lanka offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 289 companies traded on the Colombo Stock Exchange (XCOL).

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Top 5 used data fields in the Corporate Actions Dataset for Sri Lanka:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in Sri Lanka:

Equity Issuances: Sri Lankan companies often engage in equity issuances such as initial public offerings (IPOs) or rights issues to raise capital for expansion and growth.

Mergers and Acquisitions (M&A): Corporate actions related to mergers, acquisitions, and takeovers play a role in shaping industry landscapes and consolidation in Sri Lanka.

Foreign Direct Investment (FDI): Corporate actions involving foreign investment and joint ventures contribute to economic growth and international collaboration in various sectors.

Debt Issuances: Sri Lankan businesses may issue bonds or other debt instruments to raise funds for capital investments and operational needs.

Dividend Declarations: Companies often announce dividends to distribute profits to shareholders, reflecting financial performance and commitment to shareholder value.

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Top 5 financial instruments with corporate action Data in Sri Lanka

Colombo Stock Exchange (CSE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Colombo Stock Exchange. This index would provide insights into the performance of the Sri Lankan stock market.

Colombo Stock Exchange (CSE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Colombo Stock Exchange. This index would give an overview of foreign business involvement in Sri Lanka.

LankaMart: A Sri Lanka-based supermarket chain with operations in multiple regions. LankaMart focuses on offering high-quality products and services to local and international customers.

Inclusive Finance Lanka: A financial services provider with operations across various markets, including Sri Lanka. Inclusive Finance Lanka emphasizes financial inclusion and access to services for underserved populations.

GreenSeed Lanka: A leading producer and distributor of sustainable agricultural products and certified crop seeds in various countries, including Sri Lanka. GreenSeed Lanka contributes to Sri Lanka's commitment to sustainability and responsible agricultural practices.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Sri Lanka, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Sri Lanka?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

How complete is the Corporate Actions Dataset coverage in Sri Lanka?

Techsalerator provides comprehensive coverage of Corporate Actions Data for various companies and securities traded on the Sri Lanka Stock Exchange. The dataset encompasses major corporate actions announced by entities in the Sri Lanka market.

How does Techsale...

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

Individuals

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 for a list of the economies included). 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 1003.

Mode of data collection

Landline and cellular telephone

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

Techsalerator’s Business Funding Data for Bangladesh

Techsalerator’s Business Funding Data for Bangladesh offers a comprehensive and insightful collection of information crucial for businesses, investors, and financial analysts. This dataset provides a detailed analysis of funding activities across various sectors in Bangladesh, capturing and categorizing data related to funding rounds, investment sources, and financial milestones.

If you need the full dataset, reach out to us at info@techsalerator.com or https://www.techsalerator.com/contact-us.

Techsalerator’s Business Funding Data for Bangladesh

Techsalerator’s Business Funding Data for Bangladesh presents a thorough and insightful overview of key information for businesses, investors, and financial analysts. This dataset offers an in-depth examination of funding activities across diverse sectors in Bangladesh, detailing data related to funding rounds, investment sources, and significant financial milestones.

Top 5 Key Data Fields

• Company Name: Identifies the company receiving funding. This information helps investors discover potential opportunities and allows analysts to track funding trends within specific industries.

• Funding Amount: Shows the total amount of funding a company has received. Understanding these amounts provides insights into the financial health and growth potential of businesses and the scale of investment activities.

• Funding Round: Indicates the stage of funding, such as seed, Series A, Series B, or later stages. This helps investors evaluate a business’s maturity and growth trajectory.

• Investor Name: Provides details about the investors or investment firms involved. Knowing the investors helps assess the credibility of the funding source and their strategic interests.

• Investment Date: Records when the funding was completed. The timing of investments can reflect market trends, investor confidence, and potential impacts on a company’s future.

Top 5 Funding Trends in Bangladesh

• Infrastructure Development: Significant investments are being made in infrastructure projects, including roads, bridges, and energy projects. These investments are crucial for the country's economic growth and development.

• Agriculture and Agritech: With agriculture being a key sector in Bangladesh’s economy, funding is directed towards modernizing agricultural practices through agritech, focusing on sustainability and productivity improvements.

• Telecommunications and Digital Connectivity: The telecom sector in Bangladesh is drawing investment, with efforts to enhance digital connectivity and access to information, vital for economic progress and social inclusion.

• Healthcare and Pharmaceuticals: Increased funding is flowing into healthcare infrastructure, pharmaceuticals, and health tech to address healthcare needs and support medical research and innovation.

• Education and Vocational Training: Funding is being allocated to educational initiatives and vocational training programs aimed at improving literacy rates, enhancing skills, and creating job opportunities.

Top 5 Companies with Notable Funding Data in Bangladesh

• Grameenphone: Bangladesh’s leading telecommunications provider, Grameenphone, has received substantial funding to expand network coverage, enhance digital services, and support community initiatives.

• BRAC Bank: This financial institution has attracted significant investment to improve its banking services, expand its reach across the country, and promote financial inclusion.

• Pathao: A major player in the logistics and transportation sector, Pathao has secured funding for expanding its services and developing innovative technology solutions.

• Aamra Technologies: This IT and tech services company has garnered funding to advance its technology solutions, expand its market presence, and drive digital transformation.

• Dhaka Bank: Dhaka Bank has received notable funding to strengthen its financial products, develop digital banking solutions, and support the growth of Bangladesh’s financial sector.

Accessing Techsalerator’s Business Funding Data

To obtain Techsalerator’s Business Funding Data for Bangladesh, contact info@techsalerator.com with your specific needs. Techsalerator will provide a customized quote based on the required data fields and records, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Company Name
• Funding Amount
• Funding Round
• Investor Name
• Investment Date
• Funding Type (Equity, Debt, Grants, etc.)
• Sector Focus
• Deal Structure
• Investment Stage
• Contact Information

For detailed insights into funding activities and financial trends in Bangladesh, Techsalerator’s dataset is an invaluable resource for investors, business analysts, and financial professionals seeking informed, strategic decisions.

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 Lesotho is 1010.

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.

Romania RO: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15-24 data was reported at 51.062 % in 2017. This records a decrease from the previous number of 55.035 % for 2014. Romania RO: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15-24 data is updated yearly, averaging 51.062 % from Dec 2011 (Median) to 2017, with 3 observations. The data reached an all-time high of 55.035 % in 2014 and a record low of 37.043 % in 2011. Romania RO: Bank Account Ownership at a Financial Institution or with a Mobile-Money-Service Provider: % of Population Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Romania – Table RO.World Bank.WDI: Bank Account Ownership. Account denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months (young adults, % of population ages 15-24).; ; Demirguc-Kunt et al., 2018, Global Financial Inclusion Database, World Bank.; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

The Medical Care Cost Recovery National Database (MCCR NDB) provides a repository of summary Medical Care Collections Fund (MCCF) billing and collection information used by program management to compare facility performance. It stores summary information for Veterans Health Administration (VHA) receivables including the number of receivables and their summarized status information. This database is used to monitor the status of the VHA's collection process and to provide visibility on the types of bills and collections being done by the Department. The objective of the VA MCCF Program is to collect reimbursement from third party health insurers and co-payments from certain non-service-connected (NSC) Veterans for the cost of medical care furnished to Veterans. Legislation has authorized VHA to: submit claims to and recover payments from Veterans' third party health insurance carriers for treatment of non-service-connected conditions; recover co-payments from certain Veterans for treatment of non-service-connected conditions; and recover co-payments for medications from certain Veterans for treatment of non-service-connected conditions. All of the information captured in the MCCR NDB is derived from the Accounts Receivable (AR) modules running at each medical center. MCCR NDB is not used for official collections figures; instead, the Department uses the Financial Management System (FMS).

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

POC Connectivity Middleware Gateways Market Outlook

According to our latest research, the global POC Connectivity Middleware Gateways market size reached USD 1.12 billion in 2024, and is projected to grow at a robust CAGR of 8.3% over the forecast period, reaching USD 2.16 billion by 2033. This impressive growth is fueled by the increasing adoption of point-of-care (POC) diagnostic solutions, the rising need for seamless data integration across healthcare systems, and the ongoing digital transformation within the healthcare sector. The market is witnessing a paradigm shift as healthcare providers prioritize interoperability, real-time data access, and enhanced patient outcomes, making POC connectivity middleware gateways an essential component of modern healthcare infrastructure.

One of the primary growth drivers for the POC Connectivity Middleware Gateways market is the surging demand for rapid and accurate diagnostic solutions at the point of care. With the global healthcare landscape evolving towards patient-centric models, there is a growing emphasis on reducing turnaround times for diagnostic results and improving clinical workflows. POC connectivity middleware gateways bridge the gap between diverse diagnostic devices and hospital information systems, enabling seamless data exchange and integration. This capability not only enhances operational efficiency but also supports timely clinical decision-making, which is critical in acute care settings. Furthermore, the proliferation of chronic diseases and the increasing volume of diagnostic tests performed outside traditional laboratory environments are compelling healthcare providers to invest in advanced connectivity solutions that ensure data integrity and compliance with regulatory standards.

Another significant factor contributing to market expansion is the rapid technological advancements in middleware gateway solutions. The integration of advanced features such as automated data routing, bidirectional communication, and support for multiple device protocols has transformed middleware gateways into sophisticated platforms that go beyond basic connectivity. These innovations are particularly important in the context of growing adoption of Internet of Things (IoT) devices and the need for scalable, secure, and interoperable infrastructure. Additionally, the shift towards cloud-based deployment models is enabling healthcare organizations to achieve greater flexibility, scalability, and cost-effectiveness in managing their connectivity solutions. Cloud-based middleware gateways facilitate remote monitoring, centralized data management, and easier compliance with evolving regulatory requirements, which are increasingly important in a post-pandemic healthcare environment.

The trend towards healthcare digitalization and the integration of electronic health records (EHRs) is also playing a pivotal role in driving the POC Connectivity Middleware Gateways market. Healthcare providers are under mounting pressure to deliver high-quality care while adhering to stringent data privacy and security regulations. Middleware gateways serve as a critical link in ensuring that diagnostic data from POC devices is accurately captured, transmitted, and stored within EHR systems. This not only improves the continuity of care but also supports population health management initiatives and value-based care models. As healthcare systems around the world continue to invest in digital infrastructure, the demand for reliable, scalable, and secure middleware gateway solutions is expected to surge.

Regionally, North America continues to dominate the POC Connectivity Middleware Gateways market, accounting for the largest share in 2024, driven by advanced healthcare infrastructure, high adoption of digital health technologies, and supportive regulatory frameworks. Europe follows closely, with increasing investments in healthcare IT and a growing focus on interoperability standards. The Asia Pacific region is emerging as a high-growth market, fueled by expanding healthcare access, rising investments in healthcare infrastructure, and the adoption of innovative diagnostic technologies. Latin America and the Middle East & Africa regions are also witnessing steady growth, supported by government initiatives to modernize healthcare systems and improve patient outcomes.