The global synthetic data market size is projected to grow from USD 0.4 billion in the current year to USD 19.22 billion by 2035, representing a CAGR of 42.14%, during the forecast period till 2035

Synthetic Evaluation Data Generation Market Outlook

According to our latest research, the synthetic evaluation data generation market size reached USD 1.4 billion globally in 2024, reflecting robust growth driven by the increasing need for high-quality, privacy-compliant data in AI and machine learning applications. The market demonstrated a remarkable CAGR of 32.8% from 2025 to 2033. By the end of 2033, the synthetic evaluation data generation market is forecasted to attain a value of USD 17.7 billion. This surge is primarily attributed to the escalating adoption of AI-driven solutions across industries, stringent data privacy regulations, and the critical demand for diverse, scalable, and bias-free datasets for model training and validation.

One of the primary growth factors propelling the synthetic evaluation data generation market is the rapid acceleration of artificial intelligence and machine learning deployments across various sectors such as healthcare, finance, automotive, and retail. As organizations strive to enhance the accuracy and reliability of their AI models, the need for diverse and unbiased datasets has become paramount. However, accessing large volumes of real-world data is often hindered by privacy concerns, data scarcity, and regulatory constraints. Synthetic data generation bridges this gap by enabling the creation of realistic, scalable, and customizable datasets that mimic real-world scenarios without exposing sensitive information. This capability not only accelerates the development and validation of AI systems but also ensures compliance with data protection regulations such as GDPR and HIPAA, making it an indispensable tool for modern enterprises.

Another significant driver for the synthetic evaluation data generation market is the growing emphasis on data privacy and security. With increasing incidents of data breaches and the rising cost of non-compliance, organizations are actively seeking solutions that allow them to leverage data for training and testing AI models without compromising confidentiality. Synthetic data generation provides a viable alternative by producing datasets that retain the statistical properties and utility of original data while eliminating direct identifiers and sensitive attributes. This allows companies to innovate rapidly, collaborate more openly, and share data across borders without legal impediments. Furthermore, the use of synthetic data supports advanced use cases such as adversarial testing, rare event simulation, and stress testing, further expanding its applicability across verticals.

The synthetic evaluation data generation market is also experiencing growth due to advancements in generative AI technologies, including Generative Adversarial Networks (GANs) and large language models. These technologies have significantly improved the fidelity, diversity, and utility of synthetic datasets, making them nearly indistinguishable from real data in many applications. The ability to generate synthetic text, images, audio, video, and tabular data has opened new avenues for innovation in model training, testing, and validation. Additionally, the integration of synthetic data generation tools into cloud-based platforms and machine learning pipelines has simplified adoption for organizations of all sizes, further accelerating market growth.

From a regional perspective, North America continues to dominate the synthetic evaluation data generation market, accounting for the largest share in 2024. This is largely due to the presence of leading technology vendors, early adoption of AI technologies, and a strong focus on data privacy and regulatory compliance. Europe follows closely, driven by stringent data protection laws and increased investment in AI research and development. The Asia Pacific region is expected to witness the fastest growth during the forecast period, fueled by rapid digital transformation, expanding AI ecosystems, and increasing government initiatives to promote data-driven innovation. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a slower pace, as organizations in these regions begin to recognize the value of synthetic data for AI and analytics applications.

Synthetic Data Generation for Analytics Market Outlook

According to our latest research, the synthetic data generation for analytics market size reached USD 1.42 billion in 2024, reflecting robust momentum across industries seeking advanced data solutions. The market is poised for remarkable expansion, projected to achieve USD 12.21 billion by 2033 at a compelling CAGR of 27.1% during the forecast period. This exceptional growth is primarily fueled by the escalating demand for privacy-preserving data, the proliferation of AI and machine learning applications, and the increasing necessity for high-quality, diverse datasets for analytics and model training.

One of the primary growth drivers for the synthetic data generation for analytics market is the intensifying focus on data privacy and regulatory compliance. With the implementation of stringent data protection regulations such as GDPR, CCPA, and HIPAA, organizations are under immense pressure to safeguard sensitive information. Synthetic data, which mimics real data without exposing actual personal details, offers a viable solution for companies to continue leveraging analytics and AI without breaching privacy laws. This capability is particularly crucial in sectors like healthcare, finance, and government, where data sensitivity is paramount. As a result, enterprises are increasingly adopting synthetic data generation technologies to facilitate secure data sharing, innovation, and collaboration while mitigating regulatory risks.

Another significant factor propelling the growth of the synthetic data generation for analytics market is the rising adoption of machine learning and artificial intelligence across diverse industries. High-quality, labeled datasets are essential for training robust AI models, yet acquiring such data is often expensive, time-consuming, or even infeasible due to privacy concerns. Synthetic data bridges this gap by providing scalable, customizable, and bias-free datasets that can be tailored for specific use cases such as fraud detection, customer analytics, and predictive modeling. This not only accelerates AI development but also enhances model performance by enabling broader scenario coverage and data augmentation. Furthermore, synthetic data is increasingly used to test and validate algorithms in controlled environments, reducing the risk of real-world failures and improving overall system reliability.

The continuous advancements in data generation technologies, including generative adversarial networks (GANs), variational autoencoders (VAEs), and other deep learning methods, are further catalyzing market growth. These innovations enable the creation of highly realistic synthetic datasets that closely resemble actual data distributions across various formats, including tabular, text, image, and time series data. The integration of synthetic data solutions with cloud platforms and enterprise analytics tools is also streamlining adoption, making it easier for organizations to deploy and scale synthetic data initiatives. As businesses increasingly recognize the strategic value of synthetic data for analytics, competitive differentiation, and operational efficiency, the market is expected to witness sustained investment and innovation throughout the forecast period.

Regionally, North America commands the largest share of the synthetic data generation for analytics market, driven by early technology adoption, a mature analytics ecosystem, and a strong regulatory focus on data privacy. Europe follows closely, benefiting from strict data protection laws and a vibrant AI research community. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, expanding AI investments, and increasing awareness of data privacy challenges. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with growing interest in advanced analytics and digital transformation initiatives. The global landscape is characterized by dynamic regional trends, with each market presenting unique opportunities and challenges for synthetic data adoption.

Component Analysis

The synthetic data generation for analytics market is segmented by component into software and services, each playing a pivotal role in enabling organizations to harness the power of synthetic data. The software segment dominates the market, accounting for the majority of rev

Synthetic Data as a Service Market Outlook

According to our latest research, the global synthetic data as a service market size reached USD 475 million in 2024, reflecting robust adoption across industries focused on data-driven innovation and privacy compliance. The market is growing at a remarkable CAGR of 37.2% and is projected to reach USD 6.26 billion by 2033. This accelerated expansion is primarily driven by the rising demand for privacy-preserving data solutions, the proliferation of artificial intelligence and machine learning applications, and stringent regulatory requirements around data security and compliance.

A key growth factor for the synthetic data as a service market is the increasing prioritization of data privacy and regulatory compliance across industries. Organizations are facing mounting pressure to comply with frameworks such as GDPR, CCPA, and other regional data protection laws, which significantly restrict the use of real customer data for analytics, AI training, and testing. Synthetic data offers a compelling solution by providing statistically similar, yet entirely artificial datasets that eliminate the risk of exposing sensitive information. This capability not only supports organizations in maintaining compliance but also accelerates innovation by facilitating unrestricted data sharing and collaboration across teams and partners. As privacy regulations become more stringent worldwide, the demand for synthetic data as a service is expected to surge, particularly in sectors such as healthcare, finance, and government.

Another significant driver is the rapid adoption of artificial intelligence and machine learning across diverse sectors. High-quality, labeled data is the lifeblood of effective AI model training, but real-world data is often scarce, imbalanced, or inaccessible due to privacy concerns. Synthetic data as a service enables enterprises to generate large volumes of realistic, balanced, and customizable datasets tailored to specific use cases, drastically reducing the time and cost associated with traditional data collection and annotation. This is particularly crucial for industries such as autonomous vehicles, financial services, and healthcare, where obtaining real data is either prohibitively expensive or fraught with ethical and legal complexities. The ability to augment or entirely replace real datasets with synthetic alternatives is transforming the pace and scale of AI innovation globally.

Furthermore, the market is witnessing robust investments in advanced synthetic data generation technologies, including generative adversarial networks (GANs), variational autoencoders, and diffusion models. These technologies are enabling the creation of highly realistic synthetic data across modalities such as tabular, image, text, and video. As a result, the adoption of synthetic data as a service is expanding beyond traditional use cases like data privacy and AI training to include fraud detection, system testing, and data augmentation for rare events. The growing ecosystem of synthetic data vendors, coupled with increasing awareness among enterprises of its strategic value, is creating a fertile environment for sustained market expansion.

Regionally, North America continues to lead the synthetic data as a service market, accounting for the largest share in 2024, driven by early adoption of AI technologies, strong regulatory frameworks, and a vibrant ecosystem of technology providers. Europe is following closely, propelled by stringent GDPR compliance requirements and a growing focus on responsible AI. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid digital transformation, increased investments in AI infrastructure, and expanding regulatory initiatives around data protection. These regional dynamics are shaping the competitive landscape and driving the global adoption of synthetic data as a service across both established and emerging markets.

The introduction of a Synthetic Data Generation Appliance is revolutionizing how enterprises approach data privacy and security. These appliances are designed to generate synthetic datasets on-premises, providing organizations with greater control over their data generation processes. By leveraging advanced algorithms and machine learning models, these appli

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

Ainnotate’s proprietary dataset generation methodology based on large scale generative modelling and Domain randomization provides data that is well balanced with consistent sampling, accommodating rare events, so that it can enable superior simulation and training of your models.

Ainnotate currently provides synthetic datasets in the following domains and use cases.

Internal Services - Visa application, Passport validation, License validation, Birth certificates Financial Services - Bank checks, Bank statements, Pay slips, Invoices, Tax forms, Insurance claims and Mortgage/Loan forms Healthcare - Medical Id cards

Overview This dataset contains synthetic images of road scenarios designed for training and testing autonomous vehicle AI systems. Each image simulates common driving conditions, featuring various elements such as vehicles, pedestrians, and potential obstacles like animals. Notably, specific elements—like the synthetically generated dog in the images—are included to challenge machine learning models in detecting unexpected road hazards. This dataset is ideal for projects focusing on computer vision, object detection, and autonomous driving simulations.

To learn more about the challenges of autonomous driving and how synthetic data can aid in overcoming them, check out our article: Autonomous Driving Challenge: Can Your AI See the Unseen? https://www.neurobot.co/use-cases-posts/autonomous-driving-challenge

Want to see more synthetic data in action? Visit www.neurobot.co to schedule a demo or sign up to upload your own images and generate custom synthetic data tailored to your projects.

Note Important Disclaimer: This dataset has not been part of any official research study or peer-reviewed article reviewed by autonomous driving authorities or safety experts. It is recommended for educational purposes only. The synthetic elements included in the images are not based on real-world data and should not be used in production-level autonomous vehicle systems without proper review by experts in AI safety and autonomous vehicle regulations. Please use this dataset responsibly, considering ethical implications.

Synthetic Data Generation for Training LE AI Market Outlook

According to our latest research, the global market size for Synthetic Data Generation for Training LE AI was valued at USD 1.42 billion in 2024, with a robust compound annual growth rate (CAGR) of 33.8% projected through the forecast period. By 2033, the market is expected to reach an impressive USD 18.4 billion, reflecting the surging demand for scalable, privacy-compliant, and cost-effective data solutions. The primary growth factor underpinning this expansion is the increasing need for high-quality, diverse datasets to train large enterprise artificial intelligence (LE AI) models, especially as real-world data becomes more restricted due to privacy regulations and ethical considerations.

One of the most significant growth drivers for the Synthetic Data Generation for Training LE AI market is the escalating adoption of artificial intelligence across multiple sectors such as healthcare, finance, automotive, and retail. As organizations strive to build and deploy advanced AI models, the requirement for large, diverse, and unbiased datasets has intensified. However, acquiring and labeling real-world data is often expensive, time-consuming, and fraught with privacy risks. Synthetic data generation addresses these challenges by enabling the creation of realistic, customizable datasets without exposing sensitive information, thereby accelerating AI development cycles and improving model performance. This capability is particularly crucial for industries dealing with stringent data regulations, such as healthcare and finance, where synthetic data can be used to simulate rare events, balance class distributions, and ensure regulatory compliance.

Another pivotal factor propelling the growth of the Synthetic Data Generation for Training LE AI market is the technological advancements in generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other deep learning techniques. These innovations have significantly enhanced the fidelity, scalability, and versatility of synthetic data, making it nearly indistinguishable from real-world data in many applications. As a result, organizations can now generate high-resolution images, complex tabular datasets, and even nuanced audio and video samples tailored to specific use cases. Furthermore, the integration of synthetic data solutions with cloud-based platforms and AI development tools has democratized access to these technologies, allowing both large enterprises and small-to-medium businesses to leverage synthetic data for training, testing, and validation of LE AI models.

The increasing focus on data privacy and security is also fueling market growth. With regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, organizations are under immense pressure to safeguard personal and sensitive information. Synthetic data offers a compelling solution by allowing businesses to generate artificial datasets that retain the statistical properties of real data without exposing any actual personal information. This not only mitigates the risk of data breaches and compliance violations but also enables seamless data sharing and collaboration across departments and organizations. As privacy concerns continue to mount, the adoption of synthetic data generation technologies is expected to accelerate, further driving the growth of the market.

From a regional perspective, North America currently dominates the Synthetic Data Generation for Training LE AI market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of leading technology companies, robust R&D investments, and a mature AI ecosystem have positioned North America as a key innovation hub for synthetic data solutions. Meanwhile, Asia Pacific is anticipated to witness the highest CAGR during the forecast period, driven by rapid digital transformation, government initiatives supporting AI adoption, and a burgeoning startup landscape. Europe, with its strong emphasis on data privacy and security, is also emerging as a significant market, particularly in sectors such as healthcare, automotive, and finance.

Component Analysis

The Component segment of the Synthetic Data Generation for Training LE AI market is primarily divided into Software and

Synthetic Health Data Market Outlook

According to our latest research, the global synthetic health data market size reached USD 312.4 million in 2024. The market is demonstrating robust momentum, growing at a CAGR of 31.2% from 2025 to 2033. By 2033, the synthetic health data market is forecasted to achieve a value of USD 3.14 billion. This remarkable growth is primarily driven by the increasing demand for privacy-compliant, high-quality datasets to accelerate innovation across healthcare research, clinical trials, and digital health solutions.

One of the most significant growth drivers for the synthetic health data market is the intensifying focus on data privacy and regulatory compliance. Healthcare organizations are under mounting pressure to adhere to stringent regulations such as HIPAA in the United States and GDPR in Europe. These frameworks restrict the sharing and utilization of real patient data, creating a critical need for synthetic health data that mimics real-world datasets without compromising patient privacy. The ability of synthetic data to facilitate research, AI training, and analytics without the risk of identifying individuals is a key factor fueling its widespread adoption among healthcare providers, pharmaceutical companies, and research organizations globally.

Technological advancements in artificial intelligence and machine learning are further propelling the synthetic health data market forward. The sophistication of generative models, such as GANs and variational autoencoders, has enabled the creation of highly realistic and diverse synthetic datasets. These advancements not only enhance the quality and utility of synthetic health data but also expand its applicability across a wide range of use cases, from medical imaging to genomics. The integration of synthetic data into clinical workflows and drug development pipelines is accelerating time-to-market for new therapies and improving the reliability of predictive analytics, thereby contributing to better patient outcomes and operational efficiencies.

Another critical factor supporting market expansion is the growing emphasis on interoperability and data sharing across the healthcare ecosystem. Synthetic health data enables seamless collaboration between diverse stakeholders, including healthcare providers, insurers, and technology vendors, by eliminating privacy barriers. This collaborative environment fosters innovation in areas such as population health management, personalized medicine, and remote patient monitoring. Additionally, the adoption of synthetic data is helping to address the challenges of data scarcity and bias, particularly in underrepresented populations, ensuring that AI models and healthcare solutions are more equitable and effective.

From a regional perspective, North America leads the synthetic health data market, accounting for the largest revenue share in 2024. This dominance is attributed to the region’s advanced healthcare infrastructure, high adoption of digital health technologies, and strong presence of key market players. Europe is following closely, driven by rigorous data protection regulations and a rapidly growing research ecosystem. The Asia Pacific region is emerging as a high-growth market, fueled by increasing investments in healthcare technology, expanding clinical research activities, and rising awareness about the benefits of synthetic health data. Latin America and the Middle East & Africa are also witnessing steady growth, supported by government initiatives to modernize healthcare systems and improve data-driven decision-making.

Component Analysis

The synthetic health data market is segmented by component into software and services, each playing a pivotal role in shaping the industry landscape. The software segment encompasses platforms and tools designed to generate, manage, and validate synthetic health datasets. These solutions leverage advanced machine learning algorithms and generative models to produce high-fidelity synthetic data that closely mirrors

AI in Synthetic Data Market Outlook

According to our latest research, the AI in Synthetic Data market size reached USD 1.32 billion in 2024, reflecting an exceptional surge in demand across various industries. The market is poised to expand at a CAGR of 36.7% from 2025 to 2033, with the forecasted market size expected to reach USD 21.38 billion by 2033. This remarkable growth trajectory is driven by the increasing necessity for privacy-preserving data solutions, the proliferation of AI and machine learning applications, and the rapid digital transformation across sectors. As per our latest research, the market’s robust expansion is underpinned by the urgent need to generate high-quality, diverse, and scalable datasets without compromising sensitive information, positioning synthetic data as a cornerstone for next-generation AI development.

One of the primary growth factors for the AI in Synthetic Data market is the escalating demand for data privacy and compliance with stringent regulations such as GDPR, HIPAA, and CCPA. Enterprises are increasingly leveraging synthetic data to circumvent the challenges associated with using real-world data, particularly in industries like healthcare, finance, and government, where data sensitivity is paramount. The ability of synthetic data to mimic real-world datasets while ensuring anonymity enables organizations to innovate rapidly without breaching privacy laws. Furthermore, the adoption of synthetic data significantly reduces the risk of data breaches, which is a critical concern in today’s data-driven economy. As a result, organizations are not only accelerating their AI and machine learning initiatives but are also achieving compliance and operational efficiency.

Another significant driver is the exponential growth in AI and machine learning adoption across diverse sectors. These technologies require vast volumes of high-quality data for training, validation, and testing purposes. However, acquiring and labeling real-world data is often expensive, time-consuming, and fraught with privacy concerns. Synthetic data addresses these challenges by enabling the generation of large, labeled datasets that are tailored to specific use cases, such as image recognition, natural language processing, and fraud detection. This capability is particularly transformative for sectors like automotive, where synthetic data is used to train autonomous vehicle algorithms, and healthcare, where it supports the development of diagnostic and predictive models without exposing patient information.

Technological advancements in generative AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have further propelled the market. These innovations have significantly improved the realism, diversity, and utility of synthetic data, making it nearly indistinguishable from real-world data in many applications. The synergy between synthetic data generation and advanced AI models is enabling new possibilities in areas like computer vision, speech synthesis, and anomaly detection. As organizations continue to invest in AI-driven solutions, the demand for synthetic data is expected to surge, fueling further market expansion and innovation.

From a regional perspective, North America currently leads the AI in Synthetic Data market due to its early adoption of AI technologies, strong presence of leading technology companies, and supportive regulatory frameworks. Europe follows closely, driven by its rigorous data privacy regulations and a burgeoning ecosystem of AI startups. The Asia Pacific region is emerging as a lucrative market, propelled by rapid digitalization, government initiatives, and increasing investments in AI research and development. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as organizations in these regions begin to recognize the value of synthetic data for digital transformation and innovation.

Component Analysis

The AI in Synthetic Data market is segmented by component into Software and Services, each playing a pivotal role in the industry’s growth. Software solutions dominate the market, accounting for the largest share in 2024, as organizations increasingly adopt advanced platforms for data generation, management, and integration. These software platforms leverage state-of-the-art generative AI models that enable users to create highly realistic and customizab

Synthetic Healthcare Dataset — Powered by Syncora

High-Fidelity Synthetic Medical Records for AI, ML Modeling, LLM Training & HealthTech Research

About This Dataset

This is a synthetic dataset of healthcare records generated using Syncora.ai, a next-generation synthetic data generation platform designed for privacy-safe AI development.

It simulates patient demographics, medical conditions, treatments, billing, and admission data, preserving statistical realism while ensuring 0% privacy risk.

This free dataset is designed for:

• Healthcare AI research
• Predictive analytics (disease risk, treatment outcomes)
• LLM training on structured tabular healthcare data
• Medical data science education & experimentation

Think of this as fake data that mimics real-world healthcare patterns — statistically accurate, but without any sensitive patient information.

Dataset Context & Features

The dataset captures patient-level hospital information, including:

• Demographics: Age, Gender, Blood Type
• Medical Details: Diagnosed medical condition, prescribed medication, test results
• Hospital Records: Admission type (emergency, planned, outpatient), billing amount
• Target Applications: Predictive modeling, anomaly detection, cost optimization

All records are 100% synthetic, maintaining the statistical properties of real-world healthcare data while remaining safe to share and use for ML & LLM tasks.

LLM Training & Generative AI Applications 🧠

Unlike most healthcare datasets, this one is tailored for LLM training:

• Fine-tune LLMs on tabular + medical data for reasoning tasks
• Create medical report generators from structured fields (e.g., convert demographics + condition + test results into natural language summaries)
• Use as fake data for prompt engineering, synthetic QA pairs, or generative simulations
• Safely train LLMs to understand healthcare schemas without exposing private patient data

Machine Learning & AI Use Cases

• Predictive Modeling: Forecast patient outcomes or readmission likelihood
• Classification: Disease diagnosis prediction using demographic and medical variables
• Clustering: Patient segmentation by condition, treatment, or billing pattern
• Healthcare Cost Prediction: Estimate and optimize billing amounts
• Bias & Fairness Testing: Study algorithmic bias without exposing sensitive patient data

Why Syncora?

Syncora.ai is a synthetic data generation platform designed for healthcare, finance, and enterprise AI.

Key benefits:

• Privacy-first: 100% synthetic, zero risk of re-identification
• Statistical accuracy: Feature relationships preserved for ML & LLM training
• Regulatory compliance: HIPAA, GDPR, DPDP safe
• Scalability: Generate millions of synthetic patient records with agentic AI

Ideas for Exploration

• Which medical conditions correlate with higher billing amounts?
• Can test results predict hospitalization type?
• How do demographics influence treatment or billing trends?
• Can synthetic datasets reduce bias in healthcare AI & LLMs?

🔗 Generate Your Own Synthetic Data

Take your AI projects to the next level with Syncora.ai:
→ Generate your own synthetic datasets now

Licensing & Compliance

This is a free dataset, 100% synthetic, and contains no real patient information.
It is safe for public use in education, research, open-source contributions, LLM training, and AI development.

This collection comprises of interview and focus group data gathered in 2024-2025 as part of a project aimed at investigating how synthetic data can support secure data access and improve research workflows, particularly from the perspective of data-owning organisations.

The interviews included 4 case studies of UK-based organisations who had piloted work generating and disseminating synthetic datasets, including the Ministry of Justice, NHS England, the project team working in partnership with the Department for Education, and Office for National Statistics. It also includes 2 focus groups with Trusted Repository Environment (TRE) representatives who had published or were considering publishing synthetic data.

The motivation for this collection stemmed from the growing interest in synthetic data as a tool to enhance access to sensitive data and reduce pressure on Trusted Research Environments (TREs). The study explored organisational engagement with two types of synthetic data: synthetic data generated from real data, and “data-free” synthetic data created using metadata only.

The aims of the case studies and focus groups were to assess current practices, explore motivations and barriers to adoption, understand cost and governance models, and gather perspectives on scaling and outsourcing synthetic data production. Conditional logic was used to tailor the survey to organisations actively producing, planning, or not engaging with synthetic data.

The interviews covered 5 key themes: organisational background; Infrastructure, operational costs, and resourcing; challenges of sharing synthetic data; benefits and use cases of synthetic data; and organisational policy and procedures.

The data offers exploratory insights into how UK organisations are approaching synthetic data in practice and can inform future research, infrastructure development, and policy guidance in this evolving area.

The findings have informed recommendations to support the responsible and efficient scaling of synthetic data production across sectors.

The growing discourse around synthetic data underscores its potential not only in addressing data challenges in a fast-paced changing landscape but for fostering innovation and accelerating advancements in data analytics and artificial intelligence. From optimising data sharing and utility (James et al., 2021), to sustaining and promoting reproducibility (Burgard et al., 2017) to mitigating disclosure (Nikolenko, 2021) synthetic data has emerged as a solution to various complexities of the data ecosystem.

The project proposes a mixed-methods approach and seeks to explore the operational, economic, and efficiency aspects of using low-fidelity synthetic data from the perspectives of data owners and Trusted Research Environments (TREs).

The essence of the challenge is in understanding the tangible and intangible costs associated with creating and sharing low-fidelity synthetic data, alongside measuring its utility and acceptance among data producers, data oweners and TREs. The broader aim of the project is to foster a nuanced understanding that could potentially catalyse a shift towards a more efficient and publicly acceptable model of synthetic data dissemination.

This project is centred around three primary goals: 1. to evaluate the comprehensive costs incurred by data owners and TREs in the creation and ongoing maintenance of low-fidelity synthetic data, including the initial production of synthetic data and subsequent costs; 2. to assess the various models of synthetic data sharing, evaluating the implications and efficiencies for data owners and TREs, covering all aspects from pre-ingest to curation procedures, metadata sharing, and data discoverability; and 3. to measure the efficiency improvements for data owners and TREs when synthetic data is available, analysing impacts on resources, secure environment usage load, and the uptake dynamics between synthetic and real datasets by researchers.

Commencing in March 2024, the project will begin with stakeholder engagement, forming an expert panel and aligning collaborative efforts with parallel projects. Following a robust literature review, the project will embark on a methodical data collection journey through a targeted survey with data creators, case studies with d and data owners and providers of synthetic data, and a focus group with TRE representatives. The insights collected from these activities will be analysed and synthesized to draft a comprehensive report delineating the findings and sensible recommendations for scaling up the production and dissemination of low-fidelity synthetic data as applicable.

The potential applications and benefits of the proposed work are diverse. The project aims to provide a solid foundation for data owners and TREs to make informed decisions regarding synthetic data production and sharing. Furthermore, the findings could significantly influence future policy concerning data privacy thereby having a broader impact on the research community and public perception. By fostering a deeper understanding and establishing a dialogue among key stakeholders, this project strives to bridge the existing knowledge gap and push the domain of synthetic data into a new era of informed and efficient usage. Through meticulous data collection and analysis, the project aims to unravel the intricacies of low-fidelity synthetic data, aiming to pave the way for an efficient, cost-effective, and publicly acceptable framework of synthetic data production and dissemination.

Synthetic Data Platform Market Outlook

As per our latest research, the global synthetic data platform market size reached USD 1.42 billion in 2024, demonstrating robust growth driven by the increasing demand for privacy-preserving data solutions and AI model training. The market is expected to expand at a remarkable CAGR of 34.8% from 2025 to 2033, reaching a forecasted market size of USD 19.12 billion by 2033. This rapid expansion is primarily attributed to the growing need for high-quality, scalable, and diverse datasets that comply with stringent data privacy regulations and support advanced analytics and machine learning initiatives across various industries.

One of the primary growth factors propelling the synthetic data platform market is the escalating adoption of artificial intelligence (AI) and machine learning (ML) technologies across sectors such as BFSI, healthcare, automotive, and retail. As organizations increasingly rely on AI-driven insights for decision-making, the demand for large, diverse, and high-quality datasets has surged. However, access to real-world data is often restricted due to privacy concerns, regulatory constraints, and the risk of data breaches. Synthetic data platforms address these challenges by generating artificial datasets that closely mimic real-world data while ensuring data privacy and compliance. This capability not only accelerates AI development but also reduces the risk of exposing sensitive information, thereby fueling the market’s growth.

Another significant driver is the rising importance of data privacy and protection, particularly in the wake of global regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. Organizations are under increasing pressure to protect consumer data and avoid regulatory penalties. Synthetic data platforms enable businesses to create anonymized datasets that retain the statistical properties and utility of original data, making them invaluable for testing, analytics, and model training without compromising privacy. This ability to balance innovation with compliance is a key factor boosting the adoption of synthetic data solutions.

Furthermore, the synthetic data platform market is benefiting from the growing complexity and volume of data generated by digital transformation initiatives, IoT devices, and connected systems. Traditional data collection methods are often time-consuming, expensive, and limited by accessibility issues. Synthetic data platforms offer a scalable and cost-effective alternative, allowing organizations to generate customized datasets for various use cases, including fraud detection, data augmentation, and software testing. This flexibility is particularly valuable in industries where real data is scarce, sensitive, or costly to obtain, thereby driving further market expansion.

Regionally, North America currently dominates the synthetic data platform market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology companies, robust investments in AI research, and stringent regulatory frameworks in these regions are key contributors to market growth. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, increasing adoption of AI technologies, and supportive government policies. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a relatively slower pace, as organizations in these regions begin to recognize the value of synthetic data in driving innovation and ensuring compliance.

Component Analysis

The synthetic data platform market by component is broadly segmented into software and services. The software segment currently holds the largest market share, as organizations across industries are increasingly investing in advanced synthetic data generation tools to address their growing data needs. These software solutions leverage cutting-edge technologies such as generative adversarial networks (GANs), variational autoencoders, and other machine learning algorithms to create highly realistic synthetic datasets. The ability of these platforms to generate data that closely resembles real-world scenarios, while ensuring privacy and compliance, is a major factor contributing to their widespread adoption.

Within the software segment, vendors are focusing on enhancing the scalability, flexibil

Description: This dataset contains 5 sample PDF Electronic Health Records (EHRs), generated as part of a synthetic healthcare data project. The purpose of this dataset is to assist with sales distribution, offering potential users and stakeholders a glimpse of how synthetic EHRs can look and function. These records have been crafted to mimic realistic admission data while ensuring privacy and compliance with all data protection regulations.

Key Features: 1. Synthetic Data: Entirely artificial data created for testing and demonstration purposes. 1. PDF Format: Records are presented in PDF format, commonly used in healthcare systems. 1. Diverse Use Cases: Useful for evaluating tools related to data parsing, machine learning in healthcare, or EHR management systems. 1. Rich Admission Details: Includes admission-related data that highlights the capabilities of synthetic EHR generation.

Potential Use Cases:

• Demonstrating EHR-related tools or services.
• Benchmarking data parsing models for PDF health records.
• Showcasing synthetic healthcare data in sales or marketing efforts.

Feel free to use this dataset for non-commercial testing and demonstration purposes. Feedback and suggestions for improvements are always welcome!

Synthetic Data Platform Service Liability Market Outlook

According to our latest research, the global Synthetic Data Platform Service Liability market size reached USD 1.98 billion in 2024, with a robust year-on-year growth trajectory. The market is anticipated to expand at a CAGR of 35.2% during the forecast period, reaching an estimated USD 33.91 billion by 2033. This remarkable growth is primarily driven by the increasing demand for data privacy compliance, the critical need for high-quality training data in AI and machine learning applications, and the growing awareness among enterprises regarding liability risks associated with synthetic data platforms.

The exponential surge in the adoption of artificial intelligence and machine learning across various sectors has significantly contributed to the growth of the Synthetic Data Platform Service Liability market. Organizations are increasingly leveraging synthetic data to overcome the limitations of real data, such as scarcity, privacy concerns, and regulatory restrictions. As synthetic data generation becomes more mainstream, the legal and ethical implications surrounding its use, including platform service liability, have come to the forefront. This heightened awareness is compelling vendors to integrate advanced liability management features, thereby fueling market expansion. Furthermore, the proliferation of data-intensive applications in sectors like healthcare, BFSI, and retail is amplifying the need for robust synthetic data solutions that ensure compliance and minimize liability risks.

Another pivotal growth factor is the evolving regulatory landscape, particularly with stringent data protection laws such as GDPR, CCPA, and HIPAA. Enterprises are under increasing pressure to safeguard sensitive information while maintaining operational efficiency. Synthetic data platforms provide a viable solution by generating data that mirrors real datasets without exposing actual personal information. However, the potential for liability, such as data misuse or model bias, necessitates comprehensive service liability frameworks. This trend is prompting platform providers to offer enhanced liability coverage, compliance guarantees, and transparent data lineage tracking, further driving the adoption of these platforms across regulated industries.

The market is also witnessing substantial investments in research and development, resulting in innovative synthetic data generation techniques and liability management tools. These advancements are enabling organizations to generate high-fidelity synthetic datasets tailored to specific use cases, such as fraud detection, risk management, and model validation. Additionally, the integration of synthetic data platforms with cloud and on-premises infrastructures is providing enterprises with the flexibility to deploy solutions that align with their security and compliance requirements. The convergence of these factors is expected to sustain the growth momentum of the Synthetic Data Platform Service Liability market over the forecast period.

From a regional perspective, North America currently dominates the global market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The region's leadership can be attributed to the early adoption of advanced data technologies, a mature regulatory environment, and the presence of key market players. Meanwhile, Asia Pacific is poised for the fastest growth, driven by rapid digitalization, expanding AI initiatives, and increasing regulatory scrutiny. Europe remains a critical market due to its stringent data privacy regulations and strong focus on ethical AI deployment. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing investments in digital infrastructure and the rising adoption of synthetic data solutions across various sectors.

Component Analysis

The component segment of the Synthetic Data Platform Service Liability market is bifurcated into software and services, each playing a pivotal role in shaping the overall market landscape. The software segment encompasses a wide array of platforms and tools designed for the automated generation, management, and validation of synthetic data. These solutions are increasingly incorporating advanced features such as AI-driven data synthesis, customizable data generation templates, and integrated liability management modules. The demand for such sophis

This synthetic dataset simulates energy consumption patterns and user behavior for 10,000 fictional smart households. Designed for privacy-conscious research, it mirrors real-world trends in energy usage, household demographics, and weather correlations while avoiding sensitive or identifiable information.

Synthetic Data: Programmatically generated using Python’s Faker, Pandas, and statistical models.

Real-World Relevance: Patterns align with benchmarks from the IEA and Indian Census.

Use Cases: Ideal for regression, clustering, and time-series forecasting tasks.

Context

There is a lack of public available datasets on financial services and specially in the emerging mobile money transactions domain. Financial datasets are important to many researchers and in particular to us performing research in the domain of fraud detection. Part of the problem is the intrinsically private nature of financial transactions, that leads to no publicly available datasets.

We present a synthetic dataset generated using the simulator called PaySim as an approach to such a problem. PaySim uses aggregated data from the private dataset to generate a synthetic dataset that resembles the normal operation of transactions and injects malicious behaviour to later evaluate the performance of fraud detection methods.

Content

PaySim simulates mobile money transactions based on a sample of real transactions extracted from one month of financial logs from a mobile money service implemented in an African country. The original logs were provided by a multinational company, who is the provider of the mobile financial service which is currently running in more than 14 countries all around the world.

This synthetic dataset is scaled down 1/4 of the original dataset and it is created just for Kaggle.

NOTE: Transactions which are detected as fraud are cancelled, so for fraud detection these columns (oldbalanceOrg, newbalanceOrig, oldbalanceDest, newbalanceDest ) must not be used.

Headers

This is a sample of 1 row with headers explanation:

1,PAYMENT,1060.31,C429214117,1089.0,28.69,M1591654462,0.0,0.0,0,0

step - maps a unit of time in the real world. In this case 1 step is 1 hour of time. Total steps 744 (30 days simulation).

type - CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

amount - amount of the transaction in local currency.

nameOrig - customer who started the transaction

oldbalanceOrg - initial balance before the transaction

newbalanceOrig - new balance after the transaction.

nameDest - customer who is the recipient of the transaction

oldbalanceDest - initial balance recipient before the transaction. Note that there is not information for customers that start with M (Merchants).

newbalanceDest - new balance recipient after the transaction. Note that there is not information for customers that start with M (Merchants).

isFraud - This is the transactions made by the fraudulent agents inside the simulation. In this specific dataset the fraudulent behavior of the agents aims to profit by taking control or customers accounts and try to empty the funds by transferring to another account and then cashing out of the system.

isFlaggedFraud - The business model aims to control massive transfers from one account to another and flags illegal attempts. An illegal attempt in this dataset is an attempt to transfer more than 200.000 in a single transaction.

Past Research

There are 5 similar files that contain the run of 5 different scenarios. These files are better explained at my PhD thesis chapter 7 (PhD Thesis Available here http://urn.kb.se/resolve?urn=urn:nbn:se:bth-12932.

We ran PaySim several times using random seeds for 744 steps, representing each hour of one month of real time, which matches the original logs. Each run took around 45 minutes on an i7 intel processor with 16GB of RAM. The final result of a run contains approximately 24 million of financial records divided into the 5 types of categories: CASH-IN, CASH-OUT, DEBIT, PAYMENT and TRANSFER.

Acknowledgements

This work is part of the research project ”Scalable resource-efficient systems for big data analytics” funded by the Knowledge Foundation (grant: 20140032) in Sweden.

Please refer to this dataset using the following citations:

PaySim first paper of the simulator:

E. A. Lopez-Rojas , A. Elmir, and S. Axelsson. "PaySim: A financial mobile money simulator for fraud detection". In: The 28th European Modeling and Simulation Symposium-EMSS, Larnaca, Cyprus. 2016

Synthetic Data for Computer Vision Market Outlook

According to our latest research, the synthetic data for computer vision market size reached USD 410 million globally in 2024, with a robust year-on-year growth rate. The market is expected to expand at a CAGR of 32.7% from 2025 to 2033, propelling the industry to a forecasted value of USD 4.62 billion by the end of 2033. This remarkable growth is primarily driven by the escalating demand for high-quality, annotated datasets to train computer vision models, coupled with the increasing adoption of AI and machine learning across diverse sectors. As per our comprehensive analysis, advancements in synthetic data generation technologies and the urgent need to overcome data privacy challenges are pivotal factors accelerating market expansion.

The synthetic data for computer vision market is witnessing exponential growth due to several compelling factors. One of the most significant drivers is the growing complexity of computer vision applications, which require massive volumes of accurately labeled and diverse data. Traditional data collection methods are often time-consuming, expensive, and fraught with privacy concerns, especially in sensitive sectors such as healthcare and security. Synthetic data offers a scalable and cost-effective alternative, enabling organizations to generate vast datasets with customizable attributes, thus facilitating the training of robust and unbiased computer vision models. Additionally, the rise of autonomous vehicles, advanced robotics, and smart surveillance systems is fueling the demand for synthetic data, as these applications necessitate highly accurate and versatile datasets for real-world deployment.

Another key growth factor is the rapid evolution of generative AI and simulation technologies, which have significantly enhanced the quality and realism of synthetic data. Innovations in 3D modeling, photorealistic rendering, and deep learning-based data augmentation have enabled the creation of synthetic datasets that closely mimic real-world scenarios. This technological progress not only improves model performance but also accelerates development cycles, allowing enterprises to bring AI-powered solutions to market faster. Furthermore, synthetic data helps address the issue of data bias by enabling the generation of balanced datasets, which is crucial for ensuring fairness and accuracy in computer vision applications. The growing regulatory scrutiny around data privacy and the implementation of stringent data protection laws globally are further encouraging the shift towards synthetic data solutions.

The expanding ecosystem of AI and machine learning startups, coupled with increasing investments from venture capitalists and large technology firms, is also propelling the synthetic data for computer vision market forward. Organizations across industries are recognizing the strategic value of synthetic data in accelerating innovation while minimizing operational risks associated with real-world data collection. The proliferation of cloud-based synthetic data generation platforms has democratized access to advanced tools, enabling small and medium enterprises to leverage synthetic data for their AI initiatives. As a result, the market is experiencing widespread adoption across automotive, healthcare, retail, robotics, and other sectors, each with unique requirements and use cases for synthetic data.

From a regional perspective, North America currently leads the synthetic data for computer vision market, driven by the presence of major technology companies, robust research and development activities, and early adoption of AI technologies. Europe follows closely, with strong regulatory frameworks and a focus on ethical AI development. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, increasing investments in AI infrastructure, and a burgeoning ecosystem of AI startups. Latin America and the Middle East & Africa are also witnessing growing interest, particularly in sectors such as security, agriculture, and retail, as organizations seek to harness the benefits of synthetic data to overcome local data collection challenges and accelerate digital transformation.

Component Analysis

The synthetic data for computer vision market is segmented by component into software and services, each playing a crucial role in the ecosystem. The software segment encompasses a wide range of synthetic data ge

Overview This dataset contains 1,000 synthetic financial transactions, mimicking real-world spending behaviors across various expense categories. It is ideal for machine learning, data analysis, and financial modeling tasks such as expense classification, anomaly detection, and trend analysis.

Dataset Features Transaction_ID: Unique identifier for each transaction (e.g., TX0001).

Date: Transaction date (randomly generated within the past year).

Amount: Transaction value (ranging from $5 to $150, following a uniform distribution).

Description: Short description of the transaction.

Merchant: Business or service provider where the transaction occurred.

Category: High-level expense category (e.g., Food & Beverage, Bills, Healthcare).

Categories & Merchants Food & Beverage: Starbucks, McDonald's, Subway, Dunkin

Bills: Local Utility, Internet Provider, Mobile Carrier

Entertainment: AMC Theatres, Netflix, Spotify

Transportation: Uber, Lyft, Local Transit

Groceries: Walmart, Target, Costco

Healthcare: CVS Pharmacy, Walgreens, Local Clinic

Use Cases ✅ Financial Analysis: Understand spending patterns across different categories. ✅ Anomaly Detection: Identify potential fraud by analyzing transaction amounts. ✅ Time-Series Analysis: Study spending behavior trends over time. ✅ Classification & Clustering: Build models to categorize transactions automatically. ✅ Synthetic Data Research: Use it as a benchmark dataset for developing synthetic data generation techniques.

Limitations This dataset is fully synthetic and does not reflect real financial data.

Spending patterns are generated using random sampling, without real-world statistical distributions.

Does not include user profiles, locations, or payment methods.

Generative AI experienced a massive expansion of use cases in financial services during 2024, with customer experience and engagement emerging as the dominant application. A 2024 survey revealed that ** percent of respondents prioritized this area, a dramatic increase from ** percent in the previous year. Report generation, investment research, and document processing also gained significant traction, with over ** percent of firms implementing these applications. Additional use cases included synthetic data generation, code assistance, software development, marketing and sales asset creation, and enterprise research.