This dataset is a synthetic yet realistic representation of personal auto insurance data, crafted using real-world statistics. While actual insurance data is sensitive and unavailable for public use, this dataset bridges the gap by offering a safe and practical alternative for building robust data science projects.

Why This Dataset? - Realistic Foundation: Synthetic data generated from real-world statistical patterns ensures practical relevance. - Safe for Use: No personal or sensitive information—completely anonymized and compliant with data privacy standards. - Flexible Applications: Ideal for testing models, developing prototypes, and showcasing portfolio projects.

How You Can Use It: - Build machine learning models for predicting customer conversion and retention. - Design risk assessment tools or premium optimization algorithms. - Create dashboards to visualize trends in customer segmentation and policy data. - Explore innovative solutions for the insurance industry using a realistic data foundation.

This dataset empowers you to work on real-world insurance scenarios without compromising on data sensitivity.

NY Insurance Policy Assets, Liabilities, and Premiums Annually

Investigating the Impact of Financial Health on Health Insurance Costs

By State of New York [source]

About this dataset

This dataset tracks health insurance premiums written in New York annually since 2004. It provides vital insight into the amount of money and risk taken on by insurance companies in the state: including what types of insurers are writing policies, how much they are taking on in assets and liabilities, and how this has shifted over time. This data will be invaluable to those looking to understand large scale trends in terms of the health insurance industry. The data has been updated as recently as 2021, so it provides a comprehensive picture of changes year-over-year spanning nearly two decades

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How to use the dataset

This dataset contains vital information regarding health insurance premiums, assets and liabilities related to policies written in New York annually. It is designed to provide key insights into the performance of insurance companies in New York state.

The data consists of Type of Insurer, Company Name, Year, Assets, Liabilities and Premium Written for each policy written in every year since 2009. This data can be used to gain greater insight into the performance of certain companies within this industry over time as well as creating benchmarked comparison metrics against other companies within this market space.

For individual or team exploration projects – you may want to compare one company’s yearly assets/liabilities or premiums against the average value for that same period in order to identify high or low performing periods or take a look at how some variables changed across a 5 year (or wider) timescale e.g compare how did assets/liabilites changed over the duration of 5 years?

By utilizing basic data visualizations like scatterplots and bar graphs we can start gaining more insights from our analysis by looking at potential correlations between variables such as: Are premium prices related to their assets? Does company size have an impact on the premium price? Have liabilities remained constant compared with past years?

Administrators in management roles could also use this dataset to track yearly changes within their own companys results- such as tracking existing trends over longer periods with pay attention for changes which require further investigation/ research as necessary .

All in all this data set is a great tool for students , researchers & analysts alike!

Research Ideas

• Establishing a baseline of average health insurance premiums in New York by year across different insurers.
• Comparing insurance company assets and liabilities with their premium-written to provide an understanding of how profitable they are in the New York market.
• Tracking the growth and success of health insurers in the New York over time to understand changes in industry trends or policy standards

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: health-insurance-premiums-on-policies-written-in-new-york-annually-1.csv | Column name | Description | |:--------------------|:--------------------------------------------------------------------------------------------------------------------------------| | Type of Insurer | This column indicates the type of insurer that wrote the policy. (String) | | Company Name | This column indicates the name of the company that wrote the policy. (String) | | Year | This column indicates the year that the policy was written in. (Integer) | | Assets | This column indicates the total assets of the company that wrote the policy. (Integer) | | Liabilities | This column indicates the total liabilities of the company that wrote the policy. (Integer) | | Premium Written | This column indicates the total amount paid by an individual or organization for a given product or service annually. (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit State of New York.

This is Health insurance Data to analyze Sales , internal operations and market size of a health insurance company . To analyze the sales, internal operations, and market size of a health insurance company, you would need access to relevant data. While I don't have real-time data, I can provide you with a general outline of the types of data you may need to analyze these aspects. Here are some key data points to consider:

Sales Analysis:

Monthly/quarterly/annual premium revenue Number of policies sold Premiums by product types (e.g., individual, family, group) Sales channels (e.g., agents, brokers, online) Internal Operations Analysis:

Claims data: Number of claims filed, paid, and denied Claim settlement time and ratios Customer service metrics (e.g., response time, satisfaction ratings) Underwriting metrics (e.g., policy acceptance rate, risk assessment) Market Analysis:

Market share: Percentage of the total health insurance market held by the company Competition analysis: Market share of competitors, their product offerings, and pricing Demographics: Age, income, location, and other relevant demographic information of policyholders Regulatory factors: Changes in regulations or laws affecting the health insurance industry Other data points that could be useful for analysis include customer retention rates, profitability analysis, marketing expenditure, and customer feedback.

Keep in mind that this is a general overview, and the specific data requirements may vary based on your company's unique goals and objectives. Additionally, it's important to handle and analyze this data in compliance with relevant privacy and data protection laws.

The data is formatted as a spreadsheet, encompassing the primary activities over a span of three full years (November 2015 to December 2018) concerning non-life motor insurance portfolio. This dataset comprises 105,555 rows and 30 columns. Each row signifies a policy transaction, while each column represents a distinct var

Insurance Data-as-a-Service Market Outlook

According to our latest research, the global Insurance Data-as-a-Service (DaaS) market size stood at USD 2.1 billion in 2024, demonstrating robust growth driven by the increasing digital transformation across the insurance sector. The market is projected to expand at a CAGR of 23.6% during the forecast period, reaching a value of approximately USD 16.5 billion by 2033. This significant growth is propelled by the rising adoption of cloud-based analytics, the need for real-time data access, and the evolving regulatory landscape demanding greater transparency and data-driven decision-making within the insurance industry.

One of the primary growth factors for the Insurance Data-as-a-Service market is the accelerated digitalization of insurance operations. Insurers are increasingly leveraging advanced analytics, artificial intelligence, and big data platforms to enhance their risk assessment, underwriting, and claims management processes. The demand for seamless integration of disparate data sources, both structured and unstructured, is driving the adoption of DaaS solutions. These platforms enable insurers to access, analyze, and utilize real-time data, resulting in improved operational efficiency and more accurate decision-making. As insurers strive to remain competitive in a rapidly changing landscape, the ability to harness actionable insights from vast datasets is becoming a critical differentiator.

Another significant driver is the growing emphasis on customer-centricity within the insurance industry. Modern consumers expect personalized products, faster claims processing, and seamless digital experiences. Insurance Data-as-a-Service platforms empower insurers to analyze customer behaviors, preferences, and risk profiles at a granular level. By utilizing advanced customer analytics and predictive modeling, insurers can develop tailored products, optimize pricing strategies, and enhance customer engagement. This shift towards data-driven customer management not only improves retention rates but also opens up new avenues for cross-selling and upselling, further fueling market growth.

Regulatory compliance and risk management are also pivotal growth factors in the Insurance Data-as-a-Service market. With the increasing complexity of global insurance regulations, insurers are under pressure to maintain transparency, comply with evolving standards, and mitigate emerging risks such as fraud and cyber threats. DaaS platforms offer robust tools for regulatory reporting, fraud detection, and risk analytics, enabling insurers to stay ahead of compliance requirements and safeguard their operations. The integration of advanced data governance and security features within DaaS solutions ensures that insurers can manage sensitive data responsibly, fostering trust among stakeholders and regulators alike.

From a regional perspective, North America currently dominates the Insurance Data-as-a-Service market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of advanced technologies, presence of leading insurance providers, and stringent regulatory frameworks in North America have contributed to its leadership position. Meanwhile, Asia Pacific is witnessing the fastest growth, driven by rapid digitalization, increasing insurance penetration, and the emergence of insurtech startups. Latin America and the Middle East & Africa are also experiencing steady growth, albeit at a slower pace, as insurers in these regions gradually embrace digital transformation and data-driven business models.

Insurance Third-Party Data Enrichment is becoming an integral component of the Insurance Data-as-a-Service market. As insurers seek to enhance their risk assessment and underwriting capabilities, they are increasingly turning to third-party data sources to enrich their datasets. This enrichment process involves integrating external data such as credit scores, social media activity, and public records into existing insurance datasets. By doing so, insurers can gain a more comprehensive view of their customers, allowing for more accurate risk profiling and personalized product offerings. The ability to leverage third-party data effectively is becoming a key differentiator in the competitive insurance landscape, driving innovati

Data provided by insurers, on the premiums written and claims incurred for the 2013 fiscal year. Based on reporting on the consolidated pages of the P&C-1 or Life-1 Annual returns. This data is also reported in the Superintendent of Insurance’s Annual Report.

Dataset Description: Insurance Claims Prediction

Introduction: In the insurance industry, accurately predicting the likelihood of claims is essential for risk assessment and policy pricing. However, insurance claims datasets frequently suffer from class imbalance, where the number of non-claims instances far exceeds that of actual claims. This class imbalance poses challenges for predictive modeling, often leading to biased models favoring the majority class, resulting in subpar performance for the minority class, which is typically of greater interest.

Dataset Overview: The dataset utilized in this project comprises historical data on insurance claims, encompassing a variety of information about the policyholders, their demographics, past claim history, and other pertinent features. The dataset is structured to facilitate predictive modeling tasks aimed at accurately identifying the likelihood of future insurance claims.

Key Features: 1. Policyholder Information: This includes demographic details such as age, gender, occupation, marital status, and geographical location. 2. Claim History: Information regarding past insurance claims, including claim amounts, types of claims (e.g., medical, automobile), frequency of claims, and claim durations. 3. Policy Details: Details about the insurance policies held by the policyholders, such as coverage type, policy duration, premium amount, and deductibles. 4. Risk Factors: Variables indicating potential risk factors associated with policyholders, such as credit score, driving record (for automobile insurance), health status (for medical insurance), and property characteristics (for home insurance). 5. External Factors: Factors external to the policyholders that may influence claim likelihood, such as economic indicators, weather conditions, and regulatory changes.

Objective: The primary objective of utilizing this dataset is to develop robust predictive models capable of accurately assessing the likelihood of insurance claims. By leveraging advanced machine learning techniques, such as classification algorithms and ensemble methods, the aim is to mitigate the effects of class imbalance and produce models that demonstrate high predictive performance across both majority and minority classes.

Application Areas: 1. Risk Assessment: Assessing the risk associated with insuring a particular policyholder based on their characteristics and historical claim behavior. 2. Policy Pricing: Determining appropriate premium amounts for insurance policies by estimating the expected claim frequency and severity. 3. Fraud Detection: Identifying fraudulent insurance claims by detecting anomalous patterns in claim submissions and policyholder behavior. 4. Customer Segmentation: Segmenting policyholders into distinct groups based on their risk profiles and insurance needs to tailor marketing strategies and policy offerings.

Conclusion: The insurance claims dataset serves as a valuable resource for developing predictive models aimed at enhancing risk management, policy pricing, and overall operational efficiency within the insurance industry. By addressing the challenges posed by class imbalance and leveraging the rich array of features available, organizations can gain valuable insights into insurance claim likelihood and make informed decisions to mitigate risk and optimize business outcomes.

Insurance companies that are not licensed in the state but have qualified to write insurance in the surplus lines market. The business that is written is not easily placed in the admitted carrier market, as generally the risk is too big, too unusual or substandard.

Bitext - Insurance Tagged Training Dataset for LLM-based Virtual Assistants

  Overview

This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the [insurance] sector can be easily achieved using our two-step approach to LLM Fine-Tuning. An… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-insurance-llm-chatbot-training-dataset.

The dataset contains year- and month-wise market share of each insurance company in the total number of life insurance individual or group premium policies or schemes issued, number of lives covered under group schemes, total first year premium collected and total sum assured. The same data is categorized by single, non-single, group, non-group and yearly renewable premium categories

Note: 1) The First year Premium to actual premium collected by life insurers net of only free look cancellations for the period. 2) Negative Values are as per Official Source

According to Cognitive Market Research, the global Artificial Intelligence AI in Insurance market size is USD 4681.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 33.60% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 1872.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 31.8% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 1404.36 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 1076.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 35.6% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 234.06 million in 2024 and will grow at a compound annual growth rate (CAGR) of 33.0% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 93.62 million in 2024 and will grow at a compound annual growth rate (CAGR) of 33.3% from 2024 to 2031.
The Hardware held the highest Artificial Intelligence AI in Insurance market revenue share in 2024.

Market Dynamics of Artificial Intelligence AI in Insurance Market

Key Drivers of Artificial Intelligence AI in Insurance Market

Data Explosion and Processing Power to Increase the Demand Globally

The proliferation of data and advances in processing capacity are causing a revolution in the insurance sector. Insurance companies must overcome the difficulty of efficiently evaluating and utilizing the massive volumes of data that are being collected, which range from driving patterns to client demographics. The ability of artificial intelligence (AI), which can analyze data more accurately and quickly than humans, makes it an important answer. Insurance companies may make better judgments about risk assessment, pricing, and personalized offerings by using AI algorithms to extract insightful information from large, complicated datasets. This improves operational effectiveness and consumer happiness.

Improved Risk Assessment and Underwriting to Propel Market Growth

The insurance business collects data, including a wide range of information, including driving habits and client demographics. By dramatically improving data processing capabilities, artificial intelligence (AI) offers a disruptive possibility. Insurers can quickly and accurately extract useful insights from complicated datasets with unprecedented speed and precision using AI analysis. Thanks to this increased efficiency, Insurance companies can make faster, more informed decisions—from risk assessment to customized policy offerings. Insurance companies can improve operational efficiency, effectively manage risks, and ultimately offer more individualized services to their clients by utilizing AI's capacity to navigate the data explosion. This will help the industry become more adaptable and resilient to changing market conditions.

Restraint Factors Of Artificial Intelligence AI in Insurance Market

Rising Risk Assessment to Limit the Sales

Using sophisticated data analytics, AI algorithms are transforming risk assessment and underwriting in the insurance sector. These algorithms are highly skilled at analyzing complex datasets to identify trends and predict dangers with previously unheard-of accuracy. Insurers can increase customer satisfaction and loyalty by providing low-risk customers with more competitive rates when they are reliably identified as such. Furthermore, insurers can quickly and efficiently identify possible fraudulent activity due to AI's skill in detecting anomalies. Insurance companies benefit from streamlined underwriting procedures, reduced losses, and increased profitability due to improved risk assessment and fraud detection. AI technologies improve the insurance sector's capacity to customize policies, reduce risks, and stop fraudulent activity, creating a more robust and customer-focused market.

Impact of COVID-19 on the Artificial Intelligence AI in the Insurance Market

Artificial Intelligence (AI) in the insurance industry has been greatly impacted by the COVID-19 epidemic, creating both potential and challenges. The crisis highlighted the significance of artificial intelligence (AI) in insurance, even as it slowed down conventional...

Data Clean Room for Insurance Market Outlook

According to our latest research, the global Data Clean Room for Insurance market size reached USD 1.24 billion in 2024, driven by the sector’s pressing need for privacy-centric data collaboration and advanced analytics. The market is expected to grow at a robust CAGR of 23.8% from 2025 to 2033, reaching a forecasted value of USD 9.77 billion by 2033. This remarkable growth trajectory is fueled by increasing regulatory demands for data privacy, the proliferation of digital insurance channels, and the insurance sector’s shift toward data-driven decision-making.

One of the primary growth factors for the Data Clean Room for Insurance market is the intensifying regulatory landscape surrounding data privacy and protection. Insurance companies handle vast volumes of sensitive customer data, making them prime targets for both cyber threats and regulatory scrutiny. With the advent of stringent regulations such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA), and similar frameworks globally, insurers are under constant pressure to ensure compliant data usage. Data clean rooms offer a secure, privacy-preserving environment where insurers can collaborate with partners, analyze datasets, and extract actionable insights without exposing raw personally identifiable information (PII). This capability not only mitigates legal risks but also enhances trust among customers and partners, thereby accelerating market adoption.

Another significant driver is the insurance industry's increasing reliance on advanced analytics and artificial intelligence. As insurers seek to optimize underwriting, claims processing, and customer engagement, the need for high-quality, integrated datasets has never been greater. Data clean rooms empower insurers to combine internal and external data sources securely, unlocking richer customer insights, more precise risk assessment, and enhanced fraud detection capabilities. This is particularly crucial in a landscape where digital transformation is reshaping customer expectations and competitive dynamics. By leveraging data clean rooms, insurance firms can innovate faster, personalize offerings, and respond proactively to emerging risks, all while maintaining strict data governance.

Furthermore, the rapid digitalization of insurance distribution channels is amplifying the demand for data clean room solutions. The shift from traditional agent-based models to digital platforms and insurtech solutions has led to an explosion in data volumes and complexity. Insurers now interact with customers through multiple touchpoints, generating valuable behavioral, transactional, and demographic data. Data clean rooms enable seamless, privacy-first data collaboration between insurers, brokers, reinsurers, and technology partners. This collaborative approach not only drives more effective marketing analytics and customer segmentation but also supports compliance and reporting functions, ensuring that insurers can scale digital initiatives without compromising on security or privacy.

Regionally, North America currently dominates the Data Clean Room for Insurance market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The United States, in particular, is a frontrunner due to its advanced insurance sector, high digital adoption, and proactive regulatory environment. Europe’s growth is propelled by strict data privacy regulations and a mature insurance ecosystem, while Asia Pacific is emerging as a high-growth region, fueled by rapid digitalization and expanding insurance penetration. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit from smaller bases, as insurers in these regions increasingly recognize the value of secure data collaboration in driving innovation and compliance.

Component Analysis

The Component segment of the Data Clean Room for Insurance market is divided into software and services, each playing a critical role in the adoption and implementation of data clean room solutions. Software solutions form the backbone of data clean rooms, providing the secure computational environment necessary for privacy-preserving analytics. These platforms are equipped with advanced features such as encryption, access controls, audit trails, and customizable analytics modules that enable insurers to securely collaborate on sensit

Hazard Data for Insurance Market Outlook

According to the latest research, the global hazard data for insurance market size reached USD 2.41 billion in 2024, reflecting a dynamic landscape shaped by evolving risk environments and technological advancements. The market is experiencing robust expansion, with a CAGR of 12.8% forecasted from 2025 to 2033. By 2033, the hazard data for insurance market is projected to reach USD 7.13 billion. This growth is primarily driven by the increasing frequency and severity of both natural and man-made hazards, compelling insurers to leverage advanced data analytics and modeling to refine risk assessment, underwriting, and claims management processes.

A key growth factor for the hazard data for insurance market is the escalating impact of climate change, which has led to a surge in natural disasters such as hurricanes, floods, wildfires, and earthquakes. Insurance companies are under immense pressure to accurately quantify and price these risks. The integration of high-resolution hazard data, including satellite imagery, geospatial information, and real-time weather analytics, empowers insurers to identify risk hotspots, design more resilient products, and optimize reinsurance strategies. As regulatory bodies worldwide tighten compliance requirements for risk transparency, the demand for sophisticated hazard data solutions continues to intensify, further propelling market growth.

Another significant driver is the rapid adoption of advanced technologies such as artificial intelligence (AI), machine learning, and big data analytics within the insurance industry. These technologies enable the processing and interpretation of vast volumes of hazard data, facilitating more precise catastrophe modeling and risk assessment. The proliferation of cloud-based platforms has democratized access to complex datasets and modeling tools, allowing insurers of all sizes to enhance their risk management frameworks. Moreover, the push towards digital transformation in the insurance sector is fostering investments in data-driven solutions that streamline underwriting and claims management, reducing operational costs and improving customer experience.

The evolution of the insurance value chain is also influencing market dynamics. Insurers, reinsurers, brokers, and agents are increasingly collaborating with data providers and technology firms to build integrated hazard data ecosystems. This collaborative approach is fostering innovation in product development, pricing strategies, and customer engagement. The growing emphasis on proactive risk mitigation, rather than reactive claims settlement, is encouraging insurers to invest in predictive analytics and real-time hazard monitoring. As the competitive landscape intensifies, companies that effectively leverage hazard data are gaining a strategic edge, driving further adoption across the industry.

From a regional perspective, North America remains the dominant market, accounting for the largest share of global revenues in 2024. This leadership is attributed to the region’s advanced insurance infrastructure, early adoption of digital technologies, and high exposure to catastrophic events. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, increasing insurance penetration, and heightened vulnerability to climate-related hazards. Europe continues to demonstrate steady growth, supported by stringent regulatory frameworks and a mature insurance sector. Meanwhile, Latin America and the Middle East & Africa are witnessing gradual uptake, driven by growing awareness of risk management and evolving regulatory landscapes.

Data Type Analysis

The data type segment of the hazard data for insurance market encompasses natural hazard data, man-made hazard data, catastrophe modeling data, risk assessment data, and other specialized datasets. Natural hazard data remains the cornerstone of the market, as insurers seek granular information on events such as earthquakes, floods, hurricanes, and wildfires. High-resolution satellite imagery, historical event databases, and geospatial mapping technologies are increasingly utilized to assess exposure and vulnerability at property and portfolio levels. The integration of real-time weather feeds and predictive analytics allows insurers to anticipate potential losses and adjust underwriting criteria dynamically, enhancing the accuracy of risk selection and pricing.

United States Health Insurance: Premium Per Member Per Month data was reported at 364.000 USD in Sep 2024. This stayed constant from the previous number of 364.000 USD for Jun 2024. United States Health Insurance: Premium Per Member Per Month data is updated quarterly, averaging 262.000 USD from Mar 2012 (Median) to Sep 2024, with 51 observations. The data reached an all-time high of 364.000 USD in Sep 2024 and a record low of 178.000 USD in Sep 2013. United States Health Insurance: Premium Per Member Per Month data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG017: Health Insurance: Industry Financial Snapshots.

United Healthcare Transparency in Coverage Dataset

Unlock the power of healthcare pricing transparency with our comprehensive United Healthcare Transparency in Coverage dataset. This invaluable resource provides unparalleled insights into healthcare costs, enabling data-driven decision-making for insurers, employers, researchers, and policymakers.

Key Features:

• Extensive Coverage: Access detailed pricing information for a wide range of medical procedures and services across the United States, covering approximately 76,000 employers.
• Granular Data: Analyze costs at the provider, plan, and employer levels, allowing for in-depth comparisons and trend analysis.
• Massive Scale: Over 400TB of data generated monthly, providing a wealth of information for comprehensive analysis.
• Historical Perspective: Track pricing changes over time to identify patterns and forecast future trends.
• Regular Updates: Stay current with the latest pricing information, ensuring your analyses are always based on the most recent data.

Detailed Data Points:

For each of the 76,000 employers, the dataset includes: 1. In-network negotiated rates for covered items and services 2. Historical out-of-network allowed amounts and billed charges 3. Cost-sharing information for specific items and services 4. Pricing data for medical procedures and services across providers, plans, and employers

Use Cases

For Insurers: - Benchmark your rates against competitors - Optimize network design and provider contracting - Develop more competitive and cost-effective insurance products

For Employers: - Make informed decisions about health plan offerings - Negotiate better rates with insurers and providers - Implement cost-saving strategies for employee healthcare

For Researchers: - Conduct in-depth studies on healthcare pricing variations - Analyze the impact of policy changes on healthcare costs - Investigate regional differences in healthcare pricing

For Policymakers: - Develop evidence-based healthcare policies - Monitor the effectiveness of price transparency initiatives - Identify areas for potential cost-saving interventions

Data Delivery

Our flexible data delivery options ensure you receive the information you need in the most convenient format:

• Custom Extracts: We can provide targeted datasets focusing on specific regions, procedures, or time periods.
• Regular Reports: Receive scheduled updates tailored to your specific requirements.

Why Choose Our Dataset?

1. Expertise: Our team has extensive experience in healthcare data retrieval and analysis, ensuring high-quality, reliable data.
2. Customization: We can tailor the dataset to meet your specific needs, whether you're interested in particular companies, regions, or procedures.
3. Scalability: Our infrastructure is designed to handle the massive scale of this dataset (400TB+ monthly), allowing us to provide comprehensive coverage without compromise.
4. Support: Our dedicated team is available to assist with data interpretation and technical support.

Harness the power of healthcare pricing transparency to drive your business forward. Contact us today to discuss how our United Healthcare Transparency in Coverage dataset can meet your specific needs and unlock valuable insights for your organization.

The dataset contains year-, month- and company-wise complied data on the market share in the total amount of Gross Direct Premium Underwritten by each insurance company, categorized by General, Private, Public, Stand Alone, and Specialized PPSU Insurers, etc.

Notes:

1. As per IRDA definition, Underwriting refers to the process of assessing risk and ensuring that the cost of the cover is proportionate to the risks faced by the individual concerned. Based on underwriting, a decision on acceptance or rejection of cover as well as applicability of suitable premium or modified terms, if any, is taken

2. Negative Values in the dataset are as per Official Source

This dataset is about companies. It has 18,561 rows and is filtered where the industry is Insurance. It features 2 columns including sector.

The Iowa Insurance Division regulates and supervises the business of insurance in Iowa. This dataset provides a list of insurance companies licensed to do business in Iowa.

Comprehensive dataset containing 45,895 verified Insurance company businesses in India with complete contact information, ratings, reviews, and location data.

Comprehensive dataset showing the market share, founding year, and promoter details of leading life insurance companies in India for FY 2024–25, based on IRDAI New Business Premium Report.