Free layers of bank and credit union data for the United States are available for use with GIS mapping software, databases, and web applications.

The global financial-grade distributed database market is experiencing robust growth, driven by the increasing demand for high-availability, scalability, and performance in financial applications. The market's expansion is fueled by the digital transformation within the banking, securities, and insurance sectors, necessitating robust and resilient database solutions capable of handling massive transaction volumes and complex data analytics. Key trends include the rising adoption of cloud-native architectures, the increasing preference for open-source solutions offering greater flexibility and cost-effectiveness, and the growing need for enhanced security and compliance features to meet stringent regulatory requirements. Leading players like Tencent, PingCAP, and AWS are actively innovating and expanding their offerings to cater to this burgeoning market, fostering competition and driving further advancements in technology. We estimate the market size in 2025 to be approximately $5 billion, with a projected Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, leading to a market size exceeding $20 billion by 2033. This growth is primarily driven by increasing adoption across diverse applications within the financial sector. Segment-wise, sub-database and sub-table middleware solutions are currently leading, but native distributed databases are projected to witness significant growth owing to their inherent scalability and performance advantages. Geographic growth is expected to be strong across all regions, with North America and Asia Pacific leading in market share, though developing economies will present significant future opportunities. While the market presents significant opportunities, challenges remain. These include the complexity of implementation and management of distributed databases, the need for skilled professionals to operate and maintain these systems, and the potential security risks associated with managing large and distributed datasets. Furthermore, the high initial investment costs associated with implementing these solutions can act as a barrier for smaller financial institutions. However, the long-term cost savings achieved through improved efficiency, scalability, and reduced downtime are anticipated to outweigh these initial costs, driving wider adoption. The continuous advancements in technology and the emergence of new players are shaping a dynamic and competitive market landscape.

We obtain the data of our research using the SDI (Statistics on Depository Institutions) report created by FDIC1. This report provides banks’ financial statements, ratios, types, ownership structure and information for USA banks. Therefore, it is the reference database for USA samples that offer data capturing the whole spectrum of banks loans and leases products. [1] Our data are from the Statistics on Depository Institutions (https://www5.fdic.gov/sdi/download), which provides branch-level information.

This database provides an exhaustive list of financial institutions that were active in the period between 1880-1940.

A list of HMG UK and overseas bank and control accounts

Turkey Internet Banking: Financial Transactions (FT): Value data was reported at 955,666.208 TRY mn in Mar 2018. This records a decrease from the previous number of 957,452.151 TRY mn for Dec 2017. Turkey Internet Banking: Financial Transactions (FT): Value data is updated quarterly, averaging 310,124.586 TRY mn from Mar 2007 (Median) to Mar 2018, with 45 observations. The data reached an all-time high of 957,452.151 TRY mn in Dec 2017 and a record low of 101,558.822 TRY mn in Mar 2007. Turkey Internet Banking: Financial Transactions (FT): Value data remains active status in CEIC and is reported by The Banks Association of Turkey. The data is categorized under Global Database’s Turkey – Table TR.KA010: Internet Banking Statistics.

The files (R and CSV) contain the replication data for our analysis of a set of 2,355 death duty forms from the Netherlands in 1921, presented in our article “Exploring Modern Bank Penetration in the Netherlands in the 20th Century,” published in Economic History Review. A detailed description of this dataset is available in Ruben Peeters and Amaury de Vicq. “Inheritance Taxation Records in the Netherlands in 1921: The Memories Database” (forthcoming in 2023). The database itself is currently stored on the servers of University of Antwerp as part of the datafiles of the Social History of Finance Reserach Group. The paper also uses two other datasets, Tafel Vbis and the Dutch Banking Database (1880-1940). The Tafel Vbis dataset is described in a published paper by Ruben Peeters and Amaury de Vicq: de Vicq, A., & Peeters, R. (2020). “Introduction to the ‘Tafel v-bis’ Dataset: Death Duty Summary Information for The Netherlands, 1921,” Research Data Journal for the Humanities and Social Sciences, 5(1), 1-19. doi: https://doi.org/10.1163/24523666-bja10007. The Tafel Vbis datafiles are currently stored on the servers of University of Antwerp as part of the datafiles of the Social History of Finance Reserach Group, Odysseus Group. The Dutch Banking Database is described and published by DANS, and should be cited as follows: Vicq, A. de; Gelderblom, Prof. dr. O.; Jonker, Prof. dr. J. (2021): Dutch Banking Database, 1880-1940. DANS. https://doi.org/10.17026/dans-xre-kfdf

The global financial database market is experiencing robust growth, driven by the increasing demand for real-time data and advanced analytical capabilities across various sectors. The market's expansion is fueled by several key factors, including the rising adoption of sophisticated investment strategies, regulatory compliance needs, and the burgeoning fintech industry. The market is segmented by application (personal and commercial use) and database type (real-time and historical). Commercial use currently dominates the market, owing to the extensive data requirements of financial institutions, investment banks, and research firms. However, personal use is anticipated to witness significant growth driven by the increasing accessibility and affordability of financial data through online platforms and subscription services. The real-time database segment holds a larger market share due to its critical role in high-frequency trading and real-time risk management. Key players like Bloomberg, Refinitiv (formerly Thomson Reuters), and S&P Capital IQ are establishing themselves as market leaders through continuous product innovation and strategic acquisitions, solidifying their dominant positions through comprehensive data offerings and sophisticated analytical tools. Geographic expansion is another key driver, with regions like North America and Europe currently holding significant market share, while Asia Pacific is poised for substantial growth due to the expanding financial markets and increasing technological adoption in the region. Competitive pressures are evident, with several companies striving to differentiate themselves through specialized data offerings and partnerships. The forecast period (2025-2033) suggests continued market expansion, albeit at a potentially moderating CAGR compared to previous years. This moderation could be attributed to market saturation in some developed regions and the potential for economic fluctuations. However, emerging markets and technological advancements, such as AI-driven analytics and the integration of alternative data sources, will likely continue to fuel market growth. The increasing importance of ESG (environmental, social, and governance) factors in investment decisions is also expected to drive demand for specialized financial databases that incorporate such data. The ongoing evolution of data security and privacy regulations will also play a crucial role in shaping the market's trajectory. Maintaining data integrity and compliance will be critical for market players.

Credit Risk Database Market Outlook

The global credit risk database market size was valued at USD 2.8 billion in 2023 and is expected to reach USD 5.6 billion by 2032, growing at a CAGR of 7.8% during the forecast period. The growth of this market can be attributed to increasing regulatory requirements for risk management, advancements in data analytics, and the rising need for efficient credit risk assessment tools across various industries. With financial institutions and enterprises focusing more on mitigating risks and ensuring robust financial health, the demand for comprehensive credit risk databases is poised to rise significantly.

One of the primary growth factors driving the credit risk database market is the increasing regulatory scrutiny across the globe. Financial institutions are under immense pressure to comply with stringent regulations such as Basel III in banking, which necessitates robust risk assessment and management frameworks. These regulations mandate institutions to maintain adequate capital reserves and to perform comprehensive risk evaluations, thereby driving the demand for advanced credit risk databases. Such tools provide crucial insights that help in identifying potential defaults and enabling proactive risk mitigation strategies.

Technological advancements, particularly in the realms of big data and artificial intelligence, are significantly contributing to the market's growth. Modern credit risk databases leverage AI and machine learning algorithms to analyze vast datasets in real-time, providing more accurate and timely risk assessments. By utilizing predictive analytics, these databases can forecast potential credit risks and financial distress, which allows companies to take preemptive measures. The integration of such advanced technologies is expected to propel market growth further as businesses increasingly adopt these solutions for enhanced decision-making processes.

Moreover, the growing digitization and the proliferation of digital financial services have elevated the importance of efficient credit risk management tools. As financial transactions increasingly shift online, the volume of data generated has surged, necessitating more sophisticated analysis tools to manage credit risk. This trend is especially prominent in emerging economies where digital banking and fintech services are rapidly expanding. The ability to process and analyze vast amounts of data accurately and quickly is becoming indispensable, further driving the adoption of credit risk databases.

Credit Rating Software plays a pivotal role in the landscape of credit risk databases by providing essential tools that enhance the accuracy and efficiency of credit assessments. These software solutions integrate seamlessly with credit risk databases, offering advanced analytics and real-time data processing capabilities. By leveraging sophisticated algorithms and data models, credit rating software enables organizations to evaluate creditworthiness with greater precision, thereby reducing the likelihood of defaults. The integration of credit rating software into existing systems not only streamlines the risk assessment process but also supports compliance with regulatory requirements, making it an indispensable component for financial institutions and enterprises aiming to maintain robust credit risk management frameworks.

From a regional perspective, North America is expected to hold the largest market share due to the early adoption of advanced technologies and stringent regulatory frameworks. The presence of major market players and a well-established financial sector also contribute to the region's dominance. However, the Asia Pacific region is anticipated to witness the fastest growth, driven by the rapid expansion of the financial sector, increasing regulatory requirements, and growing awareness about the benefits of credit risk databases. This region's burgeoning economies, such as China and India, offer lucrative opportunities for market players.

Component Analysis

The credit risk database market by component is segmented into software and services. The software segment includes platforms and applications that provide credit risk assessment and management functionalities. These software solutions are equipped with advanced analytics tools and machine learning algorithms to facilitate real-time risk analysis and decision-making. The rising demand for integrated software solutions that offer seamless data integration and com

Serbia Banking Sector: Assets Share: Domestic Banks: State Owned data was reported at 16.400 % in Jun 2018. This records an increase from the previous number of 16.100 % for Mar 2018. Serbia Banking Sector: Assets Share: Domestic Banks: State Owned data is updated quarterly, averaging 18.000 % from Jun 2009 (Median) to Jun 2018, with 37 observations. The data reached an all-time high of 19.500 % in Mar 2014 and a record low of 16.100 % in Mar 2018. Serbia Banking Sector: Assets Share: Domestic Banks: State Owned data remains active status in CEIC and is reported by National Bank of Serbia. The data is categorized under Global Database’s Serbia – Table RS.KB011: Banking Sector Performance Indicators.

The global financial database market is experiencing robust growth, driven by increasing demand for real-time data, sophisticated analytical tools, and the expansion of the financial technology (FinTech) sector. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based solutions offers scalability and cost-effectiveness, attracting both large financial institutions and smaller firms. Furthermore, the growing complexity of financial markets necessitates access to comprehensive and reliable data for informed decision-making, driving demand for advanced analytical tools integrated within these databases. Regulatory compliance requirements also contribute significantly to market growth, as financial institutions increasingly invest in robust data management systems to meet stringent reporting obligations. The market is segmented by application (personal and commercial use) and database type (real-time and historical), with the commercial segment dominating due to the higher data needs of financial institutions. Key players like Bloomberg, Refinitiv (formerly Thomson Reuters), and FactSet are consolidating their market positions through strategic acquisitions and technological advancements, while smaller specialized providers cater to niche market segments. The geographical distribution shows a concentration in North America and Europe, reflecting the established financial markets in these regions. However, the Asia-Pacific region is expected to exhibit significant growth over the forecast period, fueled by rapid economic expansion and the increasing adoption of financial technologies in emerging markets like India and China. Competition is intense, with established players facing challenges from new entrants offering innovative solutions and disruptive technologies. The primary restraint on market growth is the high cost of these comprehensive databases, particularly for smaller businesses and individual investors. However, the ongoing trend of subscription-based models and cloud-based solutions is partially mitigating this challenge, making the technology more accessible.

Banking CRM Software Market Outlook

The global Banking CRM Software market size was valued at approximately USD 7.5 billion in 2023 and is projected to reach USD 18.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.3% during the forecast period. The growth of this market is primarily driven by the increasing need for efficient customer relationship management solutions within the banking sector, evolving customer expectations, and advancements in technology.

One of the critical growth factors for the Banking CRM Software market is the rising emphasis on customer experience and personalized services. Banks are increasingly focusing on providing superior customer experiences to retain existing customers and attract new ones. This trend is driving the adoption of advanced CRM solutions that utilize data analytics, artificial intelligence, and machine learning to better understand customer needs and preferences. Furthermore, the integration of CRM software with other banking software solutions facilitates seamless operations, enabling better customer service and operational efficiency.

Another significant growth driver is the rapid digital transformation within the banking sector. The adoption of digital banking services has accelerated, particularly in the wake of the COVID-19 pandemic. This shift has compelled banks to invest in robust CRM systems to manage their growing online customer base effectively. The need to streamline workflows, enhance communication, and provide personalized digital experiences has become paramount, thereby fueling the demand for sophisticated CRM software.

Additionally, regulatory compliance and data security concerns are also propelling the growth of the Banking CRM Software market. Banks are required to comply with stringent regulatory standards and safeguard sensitive customer information. Advanced CRM solutions offer comprehensive data security features and compliance management tools that help banks adhere to these regulations. As a result, the demand for secure and compliant CRM software is on the rise, further boosting market growth.

From a regional perspective, North America holds a significant share of the Banking CRM Software market due to the presence of major market players, advanced technological infrastructure, and early adoption of innovative solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid economic development, increasing penetration of digital banking, and growing focus on customer-centric services in countries like China, India, and Japan are driving the market in this region.

In the evolving landscape of banking, Customer Database Software Solutions play a pivotal role in managing and analyzing vast amounts of customer data. These solutions enable banks to store, retrieve, and analyze customer information efficiently, leading to enhanced customer insights and personalized service offerings. By leveraging advanced database technologies, banks can ensure data accuracy and accessibility, which are crucial for maintaining customer trust and satisfaction. Furthermore, the integration of these database solutions with CRM systems allows for seamless data flow across various banking functions, thereby improving operational efficiency and decision-making processes. As banks continue to prioritize customer-centric strategies, the demand for robust Customer Database Software Solutions is expected to rise, further driving the growth of the Banking CRM Software market.

Component Analysis

The Banking CRM Software market is segmented by component into Software and Services. The Software segment dominates the market, driven by the high demand for comprehensive CRM solutions that cater to various banking needs. CRM software includes functionalities such as customer data management, sales automation, marketing automation, and customer service management. The increasing need for banks to streamline their operations, improve customer engagement, and drive sales growth is fostering the adoption of CRM software.

Within the Software segment, there is a growing preference for integrated CRM solutions that offer a unified platform for managing customer interactions across multiple channels. Such solutions enable banks to gain a holistic view of customer behavior, preferences, and needs, allowing them to deliver personalized services. Furthermore, the integration

The Data Management System (DBMS) market is experiencing robust growth, driven by the exponential increase in data volume and the rising adoption of cloud computing and big data analytics across diverse sectors. The market, estimated at $50 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $150 billion by 2033. This expansion is fueled by several key factors: the increasing need for robust data security and compliance measures across regulated industries like banking and healthcare; the burgeoning demand for real-time data processing capabilities for improved decision-making; and the growing adoption of advanced analytics techniques, including artificial intelligence and machine learning, which rely heavily on efficient data management. The relational DBMS segment currently holds a significant market share, but the non-relational (NoSQL) segment is witnessing rapid growth due to its scalability and flexibility in handling unstructured data. Industries like banking and finance, government, and healthcare are major adopters, driven by their need to manage massive datasets and comply with regulatory requirements. Geographic distribution shows North America and Europe currently holding the largest market shares, but the Asia-Pacific region is expected to experience the fastest growth in the coming years, driven by increased digitalization and technological advancements in countries like China and India. However, the market faces some challenges, including the complexity of data integration across different systems, the need for skilled professionals to manage and maintain DBMS solutions, and the high initial investment costs associated with implementing and upgrading these systems. Despite these restraints, the long-term outlook for the DBMS market remains positive, with continued technological innovation and increasing data generation fueling its expansion across various sectors and geographic regions.

Nowadays the collection of operational risk data worldwide highly relies on human labour, leading to slow updates, data inconsistency, and limited quantity. There remains a substantial shortage of publicly accessible operational risk databases for risk analysis. This study proposes a new data collection framework by aggregating text mining methods to replace the exhausting manual collection process. The news about operational risk can be automatically collected from the web page, then its content is analyzed and the key information is extracted. Finally, the Public-Chinese Operational Loss Data (P-COLD) database for financial institutions is constructed and expanded. Each record contains 12 key information, such as occurrence time, loss amount, and business lines, offering a more thorough description of operational risk events. With 3,723 data records from 1986 to 2023, the P-COLD database has become one of the largest and most comprehensive external operational risk databases in China. We anticipate the P-COLD database will contribute to advancements in operational risk capital calculations, dependence analysis, and institutional internal controls.The P-COLD-English ver.xlsx is a cross-institutional database on operational risk data in China's banking sector, collected from publicly available sources and translated into English.The P-COLD-Chinese ver.xlsx is a cross-institutional database on operational risk data in China's banking sector, collected from publicly available sources and recorded in Chinese.(The P-COLD-English ver.xlsx is the English-translated version of P-COLD-Chinese ver.xlsx.)The Data dictionary.xlsx records the description of each field in P-COLD database.

Time Series Databases (TSDB) Software For BFSI Sector Market size was valued at USD 106.74 Million in 2023 and is projected to reach USD 235.99 Million by 2030, growing at a CAGR of 10.53% from 2024 to 2030.

Global Time Series Databases (TSDB) Software For BFSI Sector Market Overview

The need to handle and analyze time-stamped data in various industries, including finance, led to the emergence of time series databases. Traditional relational databases needed better suited for efficiently managing large volumes of time-series data. The banking, financial services, and insurance (BFSI) sector is undergoing a data revolution driven by the exponential growth of time-series data. This data, which captures trends and changes over time, is invaluable for everything from understanding customer behavior to managing risk and making investment decisions. As a result, the demand for robust and scalable time series databases (TSDBs) is skyrocketing in the BFSI sector.

The history of TSDBs in the BFSI sector can be traced back to the early days of electronic trading when the need for high-speed data capture and analysis became apparent. Early TSDBs were often custom-built solutions designed to meet the specific needs of individual financial institutions. However, the rise of cloud computing and big data has led to a new generation of commercial TSDBs that are more affordable, scalable, and easier to use. The BFSI sector generates massive amounts of time-series data from transactions, market movements, customer behavior, and operational systems. Traditional relational databases struggle to handle this data efficiently, making TSDBs essential for storage, retrieval, and analysis.

Regulations like Basel III and IFRS 17 necessitate comprehensive data storage and analysis capabilities. TSDBs facilitate efficient recordkeeping, risk management, and compliance reporting for BFSI institutions. Timely insights into market trends, customer behavior, and fraud detection are crucial for competitive advantage. TSDBs enable real-time data capture, analysis, and prediction, powering AI-driven applications for personalized banking, fraud prevention, and dynamic risk management.

United States US: Account at a Financial Institution: % Aged 15+ data was reported at 93.584 % in 2014. This records an increase from the previous number of 87.958 % for 2011. United States US: Account at a Financial Institution: % Aged 15+ data is updated yearly, averaging 90.771 % from Dec 2011 (Median) to 2014, with 2 observations. The data reached an all-time high of 93.584 % in 2014 and a record low of 87.958 % in 2011. United States US: Account at a Financial Institution: % Aged 15+ data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Banking Indicators. Account at a financial institution 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.; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

The global financial database market is experiencing robust growth, driven by increasing demand for real-time data analytics and insights across various financial sectors. The market, currently estimated at $15 billion in 2025, is projected to expand at a compound annual growth rate (CAGR) of 8% from 2025 to 2033, reaching approximately $28 billion by 2033. This expansion is fueled by several key factors. The rise of algorithmic trading and quantitative finance necessitates access to high-quality, comprehensive financial data, driving demand for both real-time and historical databases. Moreover, regulatory compliance requirements are pushing financial institutions to invest in robust data management systems, contributing to market growth. The increasing adoption of cloud-based solutions and advanced analytical tools further accelerates market expansion. The market is segmented by application (personal and commercial use) and database type (real-time and historical). The commercial segment currently dominates, propelled by the needs of large financial institutions, investment banks, and asset management firms. However, the personal use segment is expected to witness significant growth driven by the increasing accessibility of financial data and analytical tools to individual investors. Geographical distribution shows a strong presence in North America and Europe, which are expected to remain dominant markets due to the established financial infrastructure and advanced technological capabilities. However, Asia-Pacific is anticipated to demonstrate the fastest growth, driven by increasing economic activity and the expansion of financial markets in emerging economies. Competition is intense, with established players like Bloomberg and Refinitiv (Thomson Reuters) alongside emerging niche players. The competitive landscape is marked by both established giants and agile newcomers. Established players, like Bloomberg, Thomson Reuters, and WRDS, leverage their extensive data networks and brand reputation. However, these are challenged by newer entrants offering innovative solutions and specialized datasets targeting specific niche markets. The ongoing technological advancements, such as the rise of big data analytics and artificial intelligence, presents both opportunities and challenges. While AI-powered analytics unlock deeper insights from financial data, the need to adapt to evolving technologies and data security concerns require substantial investment. Regulatory changes and data privacy concerns also represent potential restraints, requiring continuous adaptation and compliance measures. The future of the market hinges on the ability of players to innovate, adapt to evolving regulations, and meet the increasing demand for speed, accuracy, and comprehensive financial data insights. The market's trajectory strongly suggests a promising future for both established and emerging companies.

This dictionary gathers different disciplines and topics such as: finance, economy, trade, business, stock-exchange, banking, firms, negotiation, mailing, telephone conversation, values, etc. It also includes many phrases relevant for business, impersonal expressions, conjugated sentences, relevant sentences, standard sentences, synonyms, abbreviations. The DISCIPLINE field gives a subdivision into sectors : stock exchange, trade, export, business, values, economy, banking, etc. Single words are associated with the meaning or event which they apply to.Languages : French - English (GB, US), English (GB, US) - FrenchNumber of entries: 91,300. Number of terms per language: about -10% with respect to the number of entries (i.e. ca. 82,000 terms)Disciplines: about 105Format: .DBF files, sorted alphabetically in French and EnglishA viewer is also available upon demand. This software enables a spontaneous search French => English and English => French in the database according to different criteria:- by beginning of term, - by included word,- by discipline,- by abbreviation.Terms, phrases and conjugated sentences are sorted alphabetically.Examples : phrases beginning with "à" : à terme, à titre gracieux, à titre onéreux, à vue...; "en" : en compte, en vigueur..., "prix" : prix abordable, prix choc, prix exorbitant...Viewing format: .FIC (Windev)Please note that the prices indicated here are dependent from the number of entries available which is growing constantly. Please contact us for further details.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

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

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Mali is 1000.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

Privacy-Preserving Feature Extraction for Detection of

Anomalous Financial Transactions

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This repository holds the code written by the PPMLHuskies for the 2nd Place solution in the PETs Prize Challenge, Track A.

Description

The task is to predict probabilities for anomalous transactions, from a

synthetic database of international transactions, and several synthetic

databases of banking account information. We provide two solutions. One

solution, our centralized approach, found in `solution_centralized.py`,

uses the transactions database (PNS) and the banking database with no

privacy protections. The second solution, which provides robust privacy

gurantees outlined in our report, follows a federated architecture,

found in `solution_federated.py` and model.py. In this approach, PNS

data resides in one client, banking data is divided up accross other

clients, and an aggregator handles all the communication between any

clients. We have built in privacy protections so that clients and the

aggregator learn minimal information about each other, while engaging in

communication to detect anomalous transactions in PNS.

The way in which we conduct training and inference in both the

centralized and the federated architectures is fundamentally the same

(other than the privacy protections in the latter). Several new features

are engineered from the given PNS data. Then a model is trained on those

features from PNS. Next, during inference, a check is made to determine

if attributes from a PNS transaction match with the banking data, or if

the associated account in the banking data is flagged. If any of these

attributes are amiss, we give it a value of 1, and a 0 otherwise.

Lastly, we take the maximum of the inferred probabilities from the PNS

model, and the result from the Banking data validation, which is used as

our final prediction for the probability that the transaction is

anomalous.

The difference between the federated and centralized logic is that in

the federated set up, where there are one or multiple partitions of the

banking data across clients, is that the PNS client engages in a

cryptographic protocol based on homomorphic encryption with the banking

clients, routed through the aggregator, to perform feature extraction.

This protocol, to ensure privacy, and that PNS does not learn anything

from the banks beyond the set membership of a select few features, is

carried out over several rounds, r. r = 7 + n, where n is the number of

bank clients.