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 2.1 billion in 2024, demonstrating robust momentum driven by digital transformation in financial services. The market is expected to expand at a CAGR of 17.8% during the forecast period, reaching approximately USD 10.5 billion by 2033. This growth is primarily fueled by increasing regulatory support for open banking, the need for more accurate and real-time affordability assessments, and the rapid adoption of digital lending and credit scoring solutions across both established financial institutions and FinTech disruptors.

The primary growth driver for the Open Banking Data for Affordability Checks market is the evolving regulatory landscape, particularly in regions such as Europe and North America, where open banking frameworks like PSD2 and the Consumer Data Right (CDR) are actively enforced. These regulations mandate banks and financial institutions to securely share customer-permissioned data with third-party providers, enabling more precise and dynamic affordability checks. As a result, lenders and insurers can access real-time financial data to assess borrowers’ repayment capacities, thereby reducing default risks and promoting responsible lending practices. The integration of open banking APIs into affordability assessment workflows not only enhances compliance but also streamlines the customer experience, making the process faster, more transparent, and less reliant on outdated credit models.

Another significant growth factor is the increasing digitalization of lending and personal finance management. With consumers demanding instant decisions and seamless onboarding, financial institutions are leveraging open banking data to automate and personalize affordability checks. This trend is particularly evident among FinTechs and challenger banks, which utilize advanced analytics and AI-driven solutions to deliver tailored lending products. The ability to analyze granular transaction data, income patterns, and spending behaviors enables lenders to create more inclusive and accurate credit profiles, facilitating access to credit for underserved segments. Furthermore, the rise of embedded finance and the proliferation of digital banking platforms are accelerating the adoption of open banking data solutions in both developed and emerging markets.

The surge in partnerships and collaborations between banks, FinTechs, credit bureaus, and technology providers is also propelling market growth. These alliances foster innovation in affordability assessment tools, combining expertise in data analytics, cybersecurity, and regulatory compliance. Additionally, the growing awareness among consumers about the benefits of data sharing—such as receiving better loan terms or personalized financial advice—has led to greater acceptance and utilization of open banking services. As the ecosystem matures, we are witnessing the emergence of standardized protocols and interoperable platforms, further lowering barriers to entry and enabling more players to offer affordability checks powered by open banking data.

From a regional perspective, Europe currently leads the global market, accounting for the largest revenue share in 2024, followed closely by North America and Asia Pacific. The early adoption of open banking regulations in the United Kingdom and the European Union has created a fertile environment for innovation and market expansion. North America is experiencing rapid growth, driven by increasing regulatory clarity and the proliferation of FinTech startups. Meanwhile, Asia Pacific is poised for significant expansion, fueled by large unbanked populations, digital banking initiatives, and supportive government policies. Latin America and the Middle East & Africa are also witnessing steady uptake, although market maturity varies across countries due to differences in infrastructure and regulatory readiness.

Component Analysis

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Solutions and Challenges For Cloud Accounting Implementation in SMEs : A Systematic Literature ReviewSmall and Medium Enterprises (SMEs) frequently encounter financial management challenges due to limited resources and outdated accounting systems. Cloud accounting is seen as a promising solution, offering cost-effective, flexible, and accessible tools for managing financial data. This study aims to identify the key factors influencing the adoption of cloud accounting among SMEs, as well as its main benefits and challenges. Employing a qualitative approach with the Systematic Literature Review (SLR) method and PRISMA process, articles were sourced from five major databases: Scopus, Science Direct, Emerald Insight, IEEE, and Taylor & Francis. From an initial 28,817 articles, 28 relevant papers were selected for in-depth analysis. The findings reveal that cost savings, flexibility, and real-time accessibility are the primary benefits of cloud accounting for SMEs. However, adoption is still hindered by concerns regarding data security, dependence on stable internet connections, and privacy issues involving third parties. Recommendations include enhancing security standards by cloud service providers, government support for infrastructure and training, and transparent data management. Future research should further explore sector-specific adoption factors and effective strategies to overcome these challenges.

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

Consulting Firms: Deliver informed recommendations to clients with access to detailed financial datasets and key stakeholder profiles.

Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

The Convergent Aeronautics Solutions (CAS) Project uses short-duration activities to establish early-stage concept and technology feasibility for high-potential solutions. Internal teams propose ideas for overcoming key barriers associated with large-scale aeronautics problems associated with ARMD’s six strategic thrusts. The teams will conduct initial feasibility studies, perform experiments, try out new ideas, identify failures, and try again. At the end of the cycle, a review determines whether the developed solutions have met their goals, established initial feasibility, and identified potential for future aviation impact. During these reviews, the most promising capabilities will be considered for continued development further by other ARMD programs or by direct transfer to the aviation community. In the dynamic environment of new ideas, ARMD also gains significant value from the knowledge gained in activities that do not proceed.

In order to enable new capabilities in commercial aviation, the CAS Project’s focus is on merging traditional aeronautics disciplines with advancements driven by the non-aeronautics world.  The Project will draw on external collaborators to supplement in-house NASA expertise in technologies and disciplines that broadly support advancements in all ARMD strategic thrusts.

The NeluxTech Retail Dataset captures daily sales from a diverse retail and service business in Kenya, covering June 5, 2023, to September 12, 2024. Featuring loyalty and non-loyalty customer segments, it provides insights into purchasing behavior across products and services like food, stationery, and cyber services. Created from internal sales records, the dataset supports real-world applications such as sales forecasting, customer segmentation, and revenue optimization, while also offering educational value in data analysis and machine learning. Designed by an experienced freelance data analyst, it is a valuable resource for solving business challenges and fostering data science collaboration.

⚖️ License Notice

This project is shared under the Creative Commons Attribution 4.0 International License (CC-BY-4.0).

© 2025 Trakanalytica Data Solutions.
Commercial use, redistribution, or modification of the dataset and dashboard is not permitted without written consent.

Open Data Portal Software Market Outlook

According to our latest research, the global Open Data Portal Software market size reached USD 1.24 billion in 2024, demonstrating robust momentum driven by the increasing demand for data transparency and digital governance initiatives worldwide. The market is projected to expand at a strong CAGR of 16.1% during the forecast period, reaching a forecasted value of USD 3.23 billion by 2033. Key growth factors include the proliferation of open government data initiatives, rapid digital transformation across public and private sectors, and the growing need for centralized, accessible data repositories to fuel innovation and drive operational efficiency.

The Open Data Portal Software market is witnessing exponential growth due to the rising emphasis on data-driven decision-making across both governmental and commercial organizations. Governments worldwide are increasingly adopting open data policies to promote transparency, accountability, and citizen engagement. These policies are compelling public agencies to invest in robust open data portal solutions that can efficiently manage, publish, and share large volumes of structured and unstructured data. Moreover, the integration of advanced analytics, artificial intelligence, and machine learning tools within these portals is unlocking new avenues for value creation, enabling organizations to derive actionable insights from vast datasets. This trend is further amplified by regulatory mandates such as the Open Government Directive in the United States and the European Union’s Open Data Directive, which require public sector bodies to make their data freely available, thus bolstering market demand.

Another significant growth driver for the Open Data Portal Software market is the accelerated pace of digital transformation initiatives across industries. Enterprises are increasingly recognizing the strategic value of open data in fostering innovation, enhancing customer experiences, and driving operational efficiencies. By leveraging open data portals, organizations can break down data silos, facilitate seamless data sharing across departments, and foster collaboration with external stakeholders such as partners, developers, and researchers. The adoption of cloud-based deployment models is further democratizing access to open data portal solutions, enabling small and medium enterprises (SMEs) to participate in the data economy without incurring high upfront infrastructure costs. As digital ecosystems continue to evolve, the demand for scalable, secure, and user-friendly open data portal software is expected to surge, underpinning sustained market growth.

The Open Data Portal Software market is also benefiting from the increasing focus on smart city initiatives and the Internet of Things (IoT). Municipal governments and urban planners are leveraging open data portals to aggregate and disseminate real-time data on transportation, energy consumption, public safety, and environmental metrics. This data-driven approach is empowering cities to optimize resource allocation, enhance service delivery, and engage citizens in urban governance. Furthermore, the growing adoption of open data portals in sectors such as healthcare, education, and transportation is unlocking new opportunities for innovation, research, and public-private partnerships. As organizations strive to harness the full potential of open data, the market for robust, interoperable, and customizable open data portal software is poised for significant expansion over the next decade.

Regionally, North America continues to dominate the Open Data Portal Software market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, has emerged as a frontrunner due to its early adoption of open government data initiatives and strong regulatory support. Europe is witnessing rapid growth, driven by stringent data transparency regulations and the proliferation of digital government programs across member states. Meanwhile, Asia Pacific is emerging as a high-growth region, fueled by digital transformation efforts in countries such as China, India, and Australia. Latin America and the Middle East & Africa are also showing promising signs of adoption, albeit at a slower pace, as governments and enterprises in these regions increasingly recognize the value of open data in driving socio-economic development.

In this paper we propose an innovative learning algorithm - a variation of One-class ? Support Vector Machines (SVMs) learning algorithm to produce sparser solutions with much reduced computational complexities. The proposed technique returns an approximate solution, nearly as good as the solution set obtained by the classical approach, by minimizing the original risk function along with a regularization term. We introduce a bi-criterion optimization that helps guide the search towards the optimal set in much reduced time. The outcome of the proposed learning technique was compared with the benchmark one-class Support Vector machines algorithm which more often leads to solutions with redundant support vectors. Through out the analysis, the problem size for both optimization routines was kept consistent. We have tested the proposed algorithm on a variety of data sources under different conditions to demonstrate the effectiveness. In all cases the proposed algorithm closely preserves the accuracy of standard one-class ? SVMs while reducing both training time and test time by several factors.

The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) is the home (archive) of Precipitation, Atmospheric Chemistry and Dynamics, and information, as well as data and information from other related disciplines.

Context

The Open Data 500, funded by the John S. and James L. Knight Foundation (http://www.knightfoundation.org/) and conducted by the GovLab, is the first comprehensive study of U.S. companies that use open government data to generate new business and develop new products and services.

Study Goals

• Provide a basis for assessing the economic value of government open data

• Encourage the development of new open data companies

• Foster a dialogue between government and business on how government data can be made more useful

The Govlab's Approach

The Open Data 500 study is conducted by the GovLab at New York University with funding from the John S. and James L. Knight Foundation. The GovLab works to improve people’s lives by changing how we govern, using technology-enabled solutions and a collaborative, networked approach. As part of its mission, the GovLab studies how institutions can publish the data they collect as open data so that businesses, organizations, and citizens can analyze and use this information.

Company Identification

The Open Data 500 team has compiled our list of companies through (1) outreach campaigns, (2) advice from experts and professional organizations, and (3) additional research.

Outreach Campaign

• Mass email to over 3,000 contacts in the GovLab network

• Mass email to over 2,000 contacts OpenDataNow.com

• Blog posts on TheGovLab.org and OpenDataNow.com

• Social media recommendations

• Media coverage of the Open Data 500

• Attending presentations and conferences

Expert Advice

• Recommendations from government and non-governmental organizations

• Guidance and feedback from Open Data 500 advisors

Research

• Companies identified for the book, Open Data Now

• Companies using datasets from Data.gov

• Directory of open data companies developed by Deloitte

• Online Open Data Userbase created by Socrata

• General research from publicly available sources

What The Study Is Not

The Open Data 500 is not a rating or ranking of companies. It covers companies of different sizes and categories, using various kinds of data.

The Open Data 500 is not a competition, but an attempt to give a broad, inclusive view of the field.

The Open Data 500 study also does not provide a random sample for definitive statistical analysis. Since this is the first thorough scan of companies in the field, it is not yet possible to determine the exact landscape of open data companies.

One of the four primary use cases for open contracting is promoting integrity. Public contracting and procurement is government’s single greatest corruption risk, a fact highlighted by the OECD, the UN Office of Drugs and Crime, and the European Commission. Some 57% of foreign bribery cases prosecuted under the OECD Anti-Bribery Convention involved bribes to obtain public contracts. One of the exciting promises of real-time data and analytics in public procurement is that corruption and fraud can potentially be detected and prevented before they occur, rather than leaving government to pick up the pieces afterwards. Similarly, potentially anomalous patterns of bidding or contract allocation can be scrutinised directly to check for potential problems as they occur, supporting innovative, data-driven policy solutions to reinforce the most promising practices in integrity and fairness. An additional value of open data is that it allows different actors to check the integrity of the system at different times from their own unique user perspective, enabling a more robust analysis and potentially preventing a single point of failure.

Hello colleagues,

Here is the data scrapped from Art Of Problem Solving website. In the dataset you have the 8818 solutions for the 3711 problems with extracted answer (and answer letter (A,B,C,D,E) if the problem was stated like a test with answer samples).

In my opinion this data could be really helpful for us in different applications: - Bechmarking different LLMs - Prompt engineering (including few-shot pipelines) - SFT tasks - ...

The links which was fully parsed: - AMC 8 / AJHSME Problems and Solutions - AMC 10 Problems and Solutions - AMC 12 Problems and Solutions - AHSME Problems and Solutions - AIME Problems and Solutions - USAMO Problems and Solutions - USAJMO Problems and Solutions

Important note: currently I have processed about the half of the solutions, which have answer in \\boxed{} or almost boxed manner. Other solution need more careful and manual answers extraction so stay tuned for further updates of the dataset.

❤️ I hope that this dataset can help us to beat the 20/50 score on public LB.

Neonomics is a company that specializes in open banking solutions, providing a new generation of payments and financial data solutions that prioritize people. With a focus on innovation and security, Neonomics aims to revolutionize the way businesses and individuals interact with their financial data. The company's products and services include Checkout, a payment solution that allows customers to pay online, and Platform Services, which offers extensive payment connectivity and account data integration.

By leveraging its expertise in open banking, Neonomics enables businesses to streamline their financial operations, improve customer experiences, and reduce costs. With a strong presence in the Nordics, Neonomics is well-positioned to support the region's rapidly evolving fintech landscape. As a licensed Payment Institution (PI), Payment Initiation Service Provider (PISP), and Account Information Service Provider (AISP), Neonomics ensures that its solutions are secure, compliant, and compliant with regulatory requirements.

A multidisciplinary repository of public data sets such as the Human Genome and US Census data that can be seamlessly integrated into AWS cloud-based applications. AWS is hosting the public data sets at no charge for the community. Anyone can access these data sets from their Amazon Elastic Compute Cloud (Amazon EC2) instances and start computing on the data within minutes. Users can also leverage the entire AWS ecosystem and easily collaborate with other AWS users. If you have a public domain or non-proprietary data set that you think is useful and interesting to the AWS community, please submit a request and the AWS team will review your submission and get back to you. Typically the data sets in the repository are between 1 GB to 1 TB in size (based on the Amazon EBS volume limit), but they can work with you to host larger data sets as well. You must have the right to make the data freely available.

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Private And Public Cloud In Financial Services Industry Market Size 2025-2029

The private and public cloud in financial services industry market size is valued to increase by USD 178.59 billion, at a CAGR of 23.8% from 2024 to 2029. Growing demand for virtually unlimited storage and big data will drive the private and public cloud in financial services industry market.

Market Insights

North America dominated the market and accounted for a 43% growth during the 2025-2029.
By Service Type - SaaS segment was valued at USD 30.99 billion in 2023
By Deployment - Private cloud segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 341.07 million 
Market Future Opportunities 2024: USD 178588.40 million
CAGR from 2024 to 2029 : 23.8%

Market Summary

In the financial services industry, the adoption of private and public cloud solutions continues to gain momentum, driven by the growing demand for virtually unlimited storage and big data processing capabilities. The financial sector's increasing reliance on technology for operational efficiency, risk management, and customer experience enhancement is fueling this trend. OpenStack, an open-source cloud computing platform, is a popular choice for many financial institutions due to its flexibility and scalability. However, the use of both private and public clouds presents unique challenges. Data security and privacy are paramount in the financial services industry, and ensuring the protection of sensitive information is a top priority. A real-world business scenario illustrates the importance of cloud solutions in financial services. Consider a global bank with a complex and geographically dispersed supply chain. By implementing a private cloud solution, the bank can securely store and process data related to its supply chain operations. Meanwhile, a public cloud platform can be used to analyze this data in real-time, providing valuable insights for optimizing the supply chain and improving operational efficiency. Despite the benefits, the implementation of private and public cloud solutions in financial services comes with its own set of challenges. Ensuring data security and privacy, complying with stringent regulatory requirements, and maintaining operational continuity are just a few of the issues that financial institutions must address. As the financial services industry continues to digitalize, the importance of secure and efficient cloud solutions will only grow.

What will be the size of the Private And Public Cloud In Financial Services Industry Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free SampleIn the dynamic financial services industry, the adoption of both private and public cloud solutions continues to evolve, shaping crucial business areas such as compliance, budgeting, and product strategy. According to recent research, private cloud deployments account for approximately 45% of the total cloud infrastructure in the financial sector, while public cloud usage holds the remaining 55% (Market Research Future). Private clouds offer enhanced security and customization, making them an attractive choice for handling sensitive financial data. However, public clouds' scalability and cost-effectiveness enable financial institutions to efficiently manage their budgets and quickly respond to market demands. For instance, a financial services company may leverage a private cloud for core banking applications while utilizing a public cloud for big data analytics. Data encryption standards, tokenization, and application security are essential considerations in cloud infrastructure finance. Compliance reporting finance necessitates adherence to data privacy regulations such as GDPR and HIPAA. In addition, cloud service providers must undergo regular security audits and penetration testing to ensure robust cybersecurity. Orchestration platforms, database management systems, and containerization are essential components of cloud systems that require agile development finance and system integration finance. Key management systems and data analytics finance enable financial institutions to gain valuable insights from their data, driving informed business decisions. In conclusion, the private and public cloud landscape in the financial services industry is a complex ecosystem, requiring a strategic approach to finance and technology. By understanding the unique benefits and challenges of each cloud deployment model, financial institutions can optimize their operations, maintain regulatory compliance, and effectively manage their budgets.

Unpacking the Private And Public Cloud In Financial Services Industry Market Landscape

In the financial services industry, the adoption of cloud technology continues to gain momentum, with private and public cloud solut

The Global Human Modification of Terrestrial Systems data set provides a cumulative measure of the human modification of terrestrial lands across the globe at a 1-km resolution. It is a continuous 0-1 metric that reflects the proportion of a landscape modified, based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global data sets with a median year of 2016.

This statistic shows which organizations or institutions consumers would contact first to resolve the issue of money loss due to the open data bank policy in the United Kingdom (UK) in 2015. Over a half of respondents, 56 percent, stated that they would first contact the bank itself. Further 22 percent would contact the third party institution involved.

Explore our Retail Transactions Dataset designed for market basket analysis, customer segmentation, and retail analytics.

The following datasets are based on the children and youth (under age 21) beneficiary population and consist of aggregate Mental Health Service data derived from Medi-Cal claims, encounter, and eligibility systems. These datasets were developed in accordance with California Welfare and Institutions Code (WIC) § 14707.5 (added as part of Assembly Bill 470 on 10/7/17). Please contact BHData@dhcs.ca.gov for any questions or to request previous years’ versions of these datasets. Note: The Performance Dashboard AB 470 Report Application Excel tool development has been discontinued. Please see the Behavioral Health reporting data hub at https://behavioralhealth-data.dhcs.ca.gov/ for access to dashboards utilizing these datasets and other behavioral health data.

Sexual health services in York. For further information please visit City of York Council's website. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

Introduction

California Health and Human Services (CalHHS) single and multi-program participation counts with demographics by year and geography, both the Annual (cumulative) and July (point in time) files. The data dictionary for each file is loaded as a resource within the CSV preview page.

Background

The CalHHS Program Data and Dashboard contain participation data related to seven California Health & Human Services agency programs: CalWORKs, CalFresh, In-Home Supportive Services, Foster Care, Medi-Cal (California Medicaid), Women, Infants, & Children, and Developmental Services. Users are able to view these data at the county level or by legislative district level (U.S. Congress, State Assembly, State Senate). Statistics on persons served, persons per case, average grant amount, and basic demographics are presented in both tabular spreadsheets and customizable visualizations by program. In addition to presenting statistics for each program separately, the dashboard also provides data on the number of individuals participating in more than one program at the same time in a given district or county.

Methodology

The Children's Data Network has prepared a methodology document for the process involved in creating this data. If you would like to review the methodology used, please click the link below to visit the CalHHS Records Reconciliation Methodology. https://data.chhs.ca.gov/pages/calhhs-program-counts-methodology