To produce data on barriers faced when deciding how and where to conduct financial transactions and inform policy-makers on issues related to economic inclusion.

We review the recent literature on the causes and consequences of financial exclusion—that is, the lack of bank account ownership—in the United States. We examine existing work in a range of fields, including economics, finance, public policy, and sociology.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

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

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

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

The sample size in Belgium was 1,004 individuals.

Mode of data collection

Other [oth]

Research instrument

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

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

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Indonesia Financial Technology Services Market size was valued at USD 8.6 Billion in 2024 and is projected to reach USD 32.5 Billion by 2032, growing at a CAGR of 17.9% from 2026 to 2032. Key Market Drivers:Rising Digital Banking Adoption: Indonesia's Financial Services Authority (OJK) reported that digital banking transactions reached USD 2.5 Trillion in 2023, representing a 37.8% year-on-year growth. Based on Bank Indonesia data, the number of mobile banking users increasing from 50.4 million in 2019 to more than 115 million by 2023.Large Unbanked Population: The World Bank's Findex Database indicates that approximately 52% of Indonesia's adult population (95 million people) remains unbanked or underbanked. Also, the Indonesian Internet Service Providers Association reports that smartphone penetration is at 73.7%, indicating a significant opportunity for digital financial inclusion.

The Global Cardless ATM Market Report is Segmented by Technology (NFC, QR-Code, Biometric, Mobile-App OTP/Token, and More), ATM Location (On-Site Branch ATMs, Off-Site/Retail ATMs, Other White-Label/Drive-Through ATMs), End-User (Retail Banking, Corporate, Under-Banked Population), and Geography (North America, South America, Europe, Asia-Pacific, Middle East and Africa). The Market Forecasts are Provided in Terms of Value (USD).

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

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

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

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

The sample size in Indonesia was 1,000 individuals.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

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

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

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

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 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

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 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

The access to services of banks or similar organizations differs widely worldwide depending on the country. While the whole population in all the Nordic countries, the Netherlands, Australia, and Canada had access to banks (meaning an “unbanked” population of **** percent), countries like Morocco and Vietnam had a higher unbanked population. Morocco was the country with the lowest share of bank account owners: less than ***** percent as of 2023. Vietnam, Egypt, and the Philippines were other countries with very high share of unbanked populations. Why are people unbanked? Countries with high shares of unbanked, such as Morocco and the abovementioned, are typically less stable economies with a less developed financial system. It is generally also countries where the citizens have little trust in the banking system. Although these countries have the highest shares of unbanked, the lack of access to services of banks or similar organizations is also present in more developed and financially stable countries as well. In the United States for example, ***** percent of the population is unbanked. The most common reason for this, according to U.S. financial households in 2019, was that they had too little money. Financial services often cost money and comes with fees, and without sufficient finances, customers might find it too expensive to open a bank account. Did the situation change after COVID-19? It can be seen, at least in Latin American countries, that the share of unbanked population dropped because of the COVID-19 pandemic, as various social benefit programs were introduced to alleviate the economic impact of the pandemic. The change in unbanked population was especially apparent in Brazil, where the share declined by ** percent in 2020.

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

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

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

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

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

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

The sample size in Bangladesh was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

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

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

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

The 2017 Global Financial Inclusion and Consumer Protection (FICP) Survey tracks the prevalence of key policy, legal, regulatory, and supervisory approaches to advancing financial inclusion and consumer protection. The 2017 Global FICP Survey covers key topics related to the enabling environment for financial inclusion and financial consumer protection, including national financial inclusion strategies, the issuance of e-money by nonbanks, agent-based delivery models, simplified customer due diligence, institutional arrangements for financial consumer protection, disclosure, dispute resolution, and financial capability. Financial sector authorities in 124 jurisdictions - representing 141 economies and more than 90% of the world’s unbanked adult population - responded to the 2017 Global FICP Survey. The survey covers regulated financial service providers offering retail credit, deposit, and/or payment products and services. The reporting period was from November 2016 to June 2017.

Alternative Credit Scoring AI Market Outlook

According to our latest research, the global Alternative Credit Scoring AI market size reached USD 1.72 billion in 2024. The market is projected to expand at a robust CAGR of 23.6% from 2025 to 2033, reaching an estimated USD 13.6 billion by 2033. This remarkable growth is driven by the increasing demand for more inclusive, data-driven, and accurate credit assessment solutions across financial services and adjacent industries, as traditional credit scoring models face limitations in addressing the needs of underserved and unbanked populations.

One of the primary growth factors fueling the Alternative Credit Scoring AI market is the rapid digitization of financial services and the proliferation of alternative data sources. With the rise of digital payments, e-commerce, and social media, financial institutions now have access to a wealth of non-traditional data points, such as utility payments, online transaction history, and behavioral analytics. AI-powered alternative credit scoring solutions leverage these datasets to build more comprehensive risk profiles, enabling lenders to assess creditworthiness for individuals and small businesses with limited or no traditional credit history. This not only expands access to credit, particularly in emerging markets, but also reduces default rates by providing more accurate risk assessments.

Another significant driver is the growing adoption of AI and machine learning technologies in the financial sector. Financial institutions and fintech companies are increasingly recognizing the value of advanced analytics in automating decision-making processes, improving customer experiences, and minimizing operational costs. AI-driven alternative credit scoring platforms can process vast amounts of structured and unstructured data in real time, uncovering patterns and correlations that traditional models may overlook. Furthermore, regulatory support for financial inclusion initiatives, particularly in regions with large unbanked populations, is encouraging the deployment of innovative credit assessment tools that go beyond conventional metrics.

The competitive landscape and the need for differentiation are also accelerating the uptake of alternative credit scoring AI solutions. As fintech disruptors challenge traditional banks, both incumbents and new entrants are seeking to enhance their risk management frameworks and offer tailored lending products. Alternative credit scoring enables financial service providers to capture new market segments, such as gig economy workers, micro-entrepreneurs, and young consumers who may lack a formal credit history. Additionally, the integration of AI-powered credit scoring with digital onboarding and instant loan approval processes is streamlining customer journeys and boosting operational efficiency, further driving market growth.

Regionally, the Asia Pacific market is witnessing the fastest adoption of alternative credit scoring AI, driven by the region's large unbanked population, rapid digital transformation, and supportive regulatory environment. North America and Europe, while more mature in terms of financial infrastructure, are also experiencing significant growth as traditional lenders modernize their credit assessment frameworks and fintech innovation accelerates. Latin America and the Middle East & Africa are emerging as promising markets due to increasing smartphone penetration, digital payment adoption, and a strong push toward financial inclusion. The global market's expansion is thus characterized by both advanced economies upgrading their systems and emerging markets leapfrogging traditional credit models with AI-powered alternatives.

Component Analysis

The Alternative Credit Scoring AI market is segmented by component into Software and Services, each playing a critical role in the ecosystem. The software segment encompasses AI-powered platforms, predictive analytics engines, and machine learning algorithms that process alternative data to generate credit scores. These solutions are increasingly leveraging cloud-based architectures, APIs, and modular frameworks, enabling seamless integration with lenders’ existing systems. As the demand for real-time risk assessment and scalable credit scoring grows, software providers are investing heavily in enhancing their platforms with advanced features such as explainable AI, customizable scoring models, and

The share of UK respondents who claimed to have no access to a banking account almost disappeared between several surveys held between 2011 and 2021, bit increased slightly in 2024. According to a three-year survey, the "unbanked" population in the UK - or those who or those who did not access to the services of a bank or another, similar financial organization - was *** percent by 2024. The report adds that men and women in the United Kingdom were equally likely to be financially excluded from services like ATM machines, credit cards, or financial products like insurance or mortgages. The declining figures for unbanked population are reflected in the decreasing market share of cash in UK physical stores.

Alternative Credit Data via PRBC Market Outlook

According to our latest research, the global Alternative Credit Data via PRBC market size reached USD 4.8 billion in 2024, reflecting the rapid adoption of alternative data sources in credit risk assessment and scoring. The market is expected to expand at a robust CAGR of 12.4% from 2025 to 2033, reaching a forecasted market size of USD 13.6 billion by 2033. This surge is primarily driven by the increasing demand for more inclusive and accurate credit evaluation mechanisms, especially for underserved and unbanked populations worldwide. As financial institutions seek to leverage non-traditional data for better risk profiling, the Alternative Credit Data via PRBC market is experiencing significant momentum, underpinned by technological advancements and regulatory encouragement for financial inclusion.

One of the primary growth factors for the Alternative Credit Data via PRBC market is the global push towards financial inclusion. Traditional credit scoring systems often exclude individuals with limited or no credit history, commonly referred to as the "credit invisible" population. By integrating alternative data sources such as utility payments, rental records, and telecom payment histories, PRBC and similar platforms enable lenders to evaluate the creditworthiness of these individuals more effectively. This not only helps in expanding the customer base for financial institutions but also fosters economic participation among marginalized groups. Governments and regulatory bodies across regions are increasingly recognizing the importance of such data in bridging the credit gap, further propelling market growth.

Another significant driver is the evolution of technology, particularly advancements in data analytics, artificial intelligence, and machine learning. These technologies allow for the efficient processing and interpretation of vast and diverse data sets, transforming raw alternative credit data into actionable insights. Financial institutions are leveraging these capabilities to enhance their risk assessment models, reduce default rates, and offer more personalized financial products. The integration of cloud-based solutions has further streamlined data aggregation and analysis, making it easier for lenders to access and utilize alternative credit data in real-time. As a result, the market is witnessing heightened adoption across both established and emerging economies.

The proliferation of fintech companies and digital lending platforms is also catalyzing the growth of the Alternative Credit Data via PRBC market. These organizations are at the forefront of innovation, often targeting underserved segments with tailored financial products. By incorporating alternative credit data into their underwriting processes, fintech firms are able to extend credit to individuals and small businesses that might otherwise be overlooked by traditional banks. This not only drives competition and innovation within the financial services sector but also accelerates market expansion as more players recognize the value of alternative data-driven credit assessment.

From a regional perspective, North America currently dominates the Alternative Credit Data via PRBC market, accounting for the largest share in 2024, driven by a mature financial ecosystem and early adoption of alternative data practices. However, Asia Pacific is poised for the fastest growth over the forecast period, fueled by a large unbanked population, rapid digitalization, and supportive regulatory frameworks. Europe also presents significant opportunities, particularly with the rise of open banking initiatives and data-sharing regulations. Latin America and the Middle East & Africa are gradually catching up, with increasing investments in digital infrastructure and a growing focus on financial inclusion. These regional dynamics are shaping the competitive landscape and influencing market strategies globally.

Data Type Analysis

The segmentation of the Alternative Credit

According to a *****-year survey, the share of Brazilian respondents without access to a banking account more than ****** between 2017 and 2024. The decline seems especially fueled by the major decline of "unbanked" population - or those who did not access to the services of a bank or another, similar financial organization - among the youngest respondents, as the share of 15-to 24-year-olds respondents declined from **** percent in 2017 to **** percent in 2024. The report adds that women in Brazil were more likely than men to be financially excluded from services like ATM machines, credit cards, or financial products like insurance or mortgages. The declining figures for unbanked population are reflected in the decreasing market share of cash in Brazilian physical stores.

Open Banking Data for Affordability Checks Market Outlook

According to our latest research, the global open banking data for affordability checks market size reached USD 2.17 billion in 2024, with robust momentum fueled by regulatory mandates and digital innovation. The market is expanding at a CAGR of 23.6% and is forecasted to reach USD 16.27 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of open banking frameworks, rising demand for real-time credit assessments, and the need for more accurate, data-driven affordability checks across financial services.

One of the pivotal growth factors for the open banking data for affordability checks market is the global shift towards digital transformation in the financial sector. Financial institutions, including banks, credit unions, and fintechs, are increasingly leveraging open banking APIs to access a broader spectrum of consumer financial data. This access allows for more granular, real-time insights into an individual’s financial health, resulting in more accurate affordability assessments. The proliferation of digital lending, particularly in the wake of the COVID-19 pandemic, has accelerated the demand for seamless, automated, and customer-centric affordability checks. As consumers expect faster loan approvals and personalized financial products, open banking data serves as a critical enabler for financial institutions to deliver these services while maintaining regulatory compliance and risk management standards.

Another significant growth driver is the evolving regulatory landscape, especially in regions such as Europe, North America, and parts of Asia Pacific. Regulations like PSD2 in the European Union and the Consumer Data Right (CDR) in Australia have mandated data sharing between financial institutions, fostering a competitive and transparent environment. These regulatory frameworks are encouraging the adoption of open banking platforms and standardizing the use of consumer-permissioned data for credit risk assessment and affordability checks. As a result, financial service providers are able to streamline their onboarding processes, reduce manual intervention, and improve the accuracy of their lending decisions. The increasing collaboration between banks and fintech firms is also driving innovation in the development of advanced algorithms and analytics tools that utilize open banking data for real-time affordability analysis.

The market’s growth is further amplified by technological advancements and the integration of artificial intelligence and machine learning with open banking data. These technologies enable financial institutions to analyze vast and complex datasets, identify spending patterns, and predict future financial behaviors with high precision. Enhanced data analytics not only improve the accuracy of affordability checks but also help in identifying potential financial distress signals early on. This proactive approach benefits both lenders and borrowers by reducing default rates and promoting responsible lending. Additionally, the growing consumer awareness regarding data privacy and control over personal financial information is fostering trust in open banking solutions, thereby supporting market expansion.

From a regional perspective, Europe currently leads the open banking data for affordability checks market, accounting for the largest share due to its mature regulatory environment and high adoption of digital banking services. North America follows closely, driven by rapid fintech innovation and increasing collaboration between traditional banks and technology providers. The Asia Pacific region is witnessing the fastest growth, propelled by a burgeoning digital economy, supportive government initiatives, and a large unbanked population gaining access to formal financial services. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a slower pace, as regulatory frameworks and digital infrastructure continue to evolve. Overall, the global outlook for open banking data in affordability checks remains highly optimistic, underpinned by regulatory support, technological progress, and shifting consumer expectations.

Component Analysis

The open banking data for affordability checks market is segmented by component into solutions and services, each playing a vital role in the value chain. The solutions segment encompasses the core technology platforms, APIs, and software

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