The website shows data on the plan and implementation of the health services program by individual health activities (VZD) :

• hospital medical activity,
• general outpatient medical activity,
• specialist outpatient medical activity,
• dental practice,
• other health activities,
• activity of accommodation facilities for patient care,
• social care without accommodation for the elderly and disabled,
• production of pharmaceutical preparations,
• retail trade in specialized stores with pharmaceutical products,
• compulsory social security activity.

Within the framework of each activity, the data for each period are shown separately by contractors and together, the activity by regional units of ZZZS and the activity data at the level of Slovenia together.

Data on the plan and implementation of the health services program are shown in the accounting unit (e.g. points, quotients, weights, groups of comparable cases, non-medical care day, care, days...), which are used to calculate the work performed in the field of individual activities.

The publication of information about the plan and implementation of the program on the ZZZS website is primarily intended for the professional public. The displayed program plan for an individual contractor refers to the defined billing period. (example: The plan for the period 1-3 201X is calculated as 3/12 of the annual plan agreed in the contract).

The data on the implementation of the program represents the implementation of the program at an individual provider for insured persons who benefited from medical services from him during the accounting period. Data on the realization of the program do not refer to persons insured in accordance with the European legal order and bilateral agreements on social security. Data for individual contractors are classified by regional units based on the contractor's headquarters. The content of the data on the "number of cases" is defined in the Instruction on recording and accounting for medical services and issued materials.

The institute reserves the right to change the data, in the event of subsequently discovered irregularities after already published on the Internet.

The Health Insurance Marketplace Public Use Files contain data on health and dental plans offered to individuals and small businesses through the US Health Insurance Marketplace.

Exploration Ideas

To help get you started, here are some data exploration ideas:

• How do plan rates and benefits vary across states?
• How do plan benefits relate to plan rates?
• How do plan rates vary by age?
• How do plans vary across insurance network providers?

See this forum thread for more ideas, and post there if you want to add your own ideas or answer some of the open questions!

Data Description

This data was originally prepared and released by the Centers for Medicare & Medicaid Services (CMS). Please read the CMS Disclaimer-User Agreement before using this data.

Here, we've processed the data to facilitate analytics. This processed version has three components:

1. Original versions of the data

The original versions of the 2014, 2015, 2016 data are available in the "raw" directory of the download and "../input/raw" on Kaggle Scripts. Search for "dictionaries" on this page to find the data dictionaries describing the individual raw files.

2. Combined CSV files that contain

In the top level directory of the download ("../input" on Kaggle Scripts), there are six CSV files that contain the combined at across all years:

• BenefitsCostSharing.csv
• BusinessRules.csv
• Network.csv
• PlanAttributes.csv
• Rate.csv
• ServiceArea.csv

Additionally, there are two CSV files that facilitate joining data across years:

• Crosswalk2015.csv - joining 2014 and 2015 data
• Crosswalk2016.csv - joining 2015 and 2016 data

3. SQLite database

The "database.sqlite" file contains tables corresponding to each of the processed CSV files.

The code to create the processed version of this data is available on GitHub.

Context

This public dataset contains data concerning the public and private insurance companies provided by IRDAI(Insurance Regulatory and Development Authority of India) from 2013-2022. This is a multi-index data and can be a great practice to hone manipulation of pandas multi-index dataframes. Mainly, the business of the companies (total premiums and number of policies), subscription information(number of people subscribed), Claims incurred and the Network hospitals enrolled by Third Party Administrators are attributes focused by the dataset.

Content

The Excel file contains the following data | Table No.| Contents| | --- | --- | |**A**|**III.A: HEALTH INSURANCE BUSINESS OF GENERAL AND HEALTH INSURERS**| |62| Health Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |63| Personal Accident Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |64| Overseas Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |65| Domestic Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |66| Health Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |67| Personal Accident Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |68| Overseas Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |69| Domestic Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |70| Details of Claims Development and Aging - Health Insurance Business| |71| State-wise Health Insurance Business| |72| State-wise Individual Health Insurance Business| |73| State-wise Personal Accident Insurance Business| |74| State-wise Overseas Insurance Business| |75| State-wise Domestic Insurance Business| |76| State-wise Claims Settlement under Health Insurance Business| |**B**|**III.B: HEALTH INSURANCE BUSINESS OF LIFE INSURERS**| |77| Health Insurance Business in respect of Products offered by Life Insurers - New Busienss| |78| Health Insurance Business in respect of Products offered by Life insurers - Renewal Business| |79| Health Insurance Business in respect of Riders attached to Life Insurance Products - New Business| |80| Health Insurance Business in respect of Riders attached to Life Insurance Products - Renewal Business| |**C**|**III.C: OTHERS**| |81| Network Hospital Enrolled by TPAs| |82| State-wise Details on Number of Network Providers |

Health Insurance Coverage reports the prevalance of Health Insurance coverage disaggregated by age group.

Imputed employer-sponsored health insurance coverage data which when linked to the March Annual Social and Economic Supplement to the Current Population Survey (March CPS), generates estimates of the number of individuals with different types of insurance coverage.

This dataset contains information about more than 1300 beneficiaries

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.

Graph and download economic data for Expenditures: Health Insurance: All Consumer Units (CXUHLTHINSRLB0101M) from 1984 to 2023 about consumer unit, health, insurance, expenditures, and USA.

This dataset is a de-identified summary table of prevalence rates for vision and eye health data indicators from the 2016 MarketScan® Commercial Claims and Encounters Data (CCAE) is produced by Truven Health Analytics, a division of IBM Watson Health. The CCEA data contain a convenience sample of insurance claims information from person with employer-sponsored insurance and their dependents, including 43.6 million person years of data. Prevalence estimates are stratified by all available combinations of age group, gender, and state. Detailed information on VEHSS MarketScan analyses can be found on the VEHSS MarketScan webpage (cdc.gov/visionhealth/vehss/data/claims/marketscan.html). Information on available Medicare claims data can be found on the IBM MarketScan website (https://marketscan.truvenhealth.com). The VEHSS MarketScan summary dataset was last updated November 2019.

Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey dataThe National Family Health Survey (NFHS), India data is publicly available data set and can be accessed on request. It can be downloaded upon registration from the Demographic and Health Survey (DHS) website upon registration at The DHS Program - Request Access To Datasets. We have used data from the fourth and fifth round of NFHS, which can be accessed after registration from the link given here for NFHS 4 and NFHS 5 https://dhsprogram.com/data/dataset/India_Standard-DHS_2015.cfm?flag=0 and here https://dhsprogram.com/data/dataset/India_Standard-DHS_2020.cfm?flag=0 respectively. These datasets (HR file) have been used to obtain this combined dataset of a paper entitled "Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data" submitted to BMJ Global Health August 2023.

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.

Context

The venerable insurance industry is no stranger to data driven decision making. Yet in today's rapidly transforming digital landscape, Insurance is struggling to adapt and benefit from new technologies compared to other industries, even within the BFSI sphere (compared to the Banking sector for example.) Extremely complex underwriting rule-sets that are radically different in different product lines, many non-KYC environments with a lack of centralized customer information base, complex relationship with consumers in traditional risk underwriting where sometimes customer centricity runs reverse to business profit, inertia of regulatory compliance - are some of the unique challenges faced by Insurance Business.

Despite this, emergent technologies like AI and Block Chain have brought a radical change in Insurance, and Data Analytics sits at the core of this transformation. We can identify 4 key factors behind the emergence of Analytics as a crucial part of InsurTech:

• Big Data: The explosion of unstructured data in the form of images, videos, text, emails, social media
• AI: The recent advances in Machine Learning and Deep Learning that can enable businesses to gain insight, do predictive analytics and build cost and time - efficient innovative solutions
• Real time Processing: Ability of real time information processing through various data feeds (for ex. social media, news)
• Increased Computing Power: a complex ecosystem of new analytics vendors and solutions that enable carriers to combine data sources, external insights, and advanced modeling techniques in order to glean insights that were not possible before.

This dataset can be helpful in a simple yet illuminating study in understanding the risk underwriting in Health Insurance, the interplay of various attributes of the insured and see how they affect the insurance premium.

Content

This dataset contains 1338 rows of insured data, where the Insurance charges are given against the following attributes of the insured: Age, Sex, BMI, Number of Children, Smoker and Region. There are no missing or undefined values in the dataset.

Inspiration

This relatively simple dataset should be an excellent starting point for EDA, Statistical Analysis and Hypothesis testing and training Linear Regression models for predicting Insurance Premium Charges.

Proposed Tasks: - Exploratory Data Analytics - Statistical hypothesis testing - Statistical Modeling - Linear Regression

In 2022, UnitedHealth Group Inc was the market leader and had a ** percent share of the U.S. health insurance market. Ellevance Health and CVS (Aetna) followed with a market share of ** percent and ** percent, respectively. Who is UnitedHealth Group? UnitedHealth Group is headquartered in Minnesota and was founded in 1977. The revenue generated by the company has steadily risen since 2007. The company offers health care products as well as insurance coverage. Membership In 2023, Kaiser was the largest health insurance company in the United States, followed by Ellevance and UnitedHealth. Membership of Kaiser almost reached **** million in that year. Meanwhile, UnitedHealth is among the largest companies worldwide in terms of revenue and the largest health care company on that list.

United States Health Insurance: Claims Per Member Per Month: Medicare data was reported at 1,111.000 USD in 2023. This records an increase from the previous number of 1,012.000 USD for 2022. United States Health Insurance: Claims Per Member Per Month: Medicare data is updated yearly, averaging 791.000 USD from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 1,111.000 USD in 2023 and a record low of 746.230 USD in 2007. United States Health Insurance: Claims Per Member Per Month: Medicare 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.RG022: Health Insurance: Operations by Lines of Business.

This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

United States Health Insurance: Enrollment: Dental data was reported at 47.000 USD mn in 2023. This records an increase from the previous number of 46.000 USD mn for 2022. United States Health Insurance: Enrollment: Dental data is updated yearly, averaging 41.000 USD mn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 47.000 USD mn in 2023 and a record low of 28.000 USD mn in 2007. United States Health Insurance: Enrollment: Dental 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.RG022: Health Insurance: Operations by Lines of Business.

United States Health Insurance: Profit Margin data was reported at 1.900 % in Sep 2024. This records a decrease from the previous number of 2.700 % for Jun 2024. United States Health Insurance: Profit Margin data is updated quarterly, averaging 3.000 % from Mar 2012 (Median) to Sep 2024, with 51 observations. The data reached an all-time high of 5.300 % in Jun 2020 and a record low of -2.100 % in Mar 2016. United States Health Insurance: Profit Margin 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.

Traditional models of insurance choice are predicated on fully informed and rational consumers protecting themselves from exposure to financial risk. In practice, choosing an insurance plan is a complicated decision often made without full information. In this paper we combine new administrative data on health plan choices and claims with unique survey data on consumer information to identify risk preferences, information frictions, and hassle costs. Our additional friction measures are important predictors of choices and meaningfully impact risk preference estimates. We study the implications of counterfactual insurance allocations to illustrate the importance of distinguishing between these micro-foundations for welfare analysis. (JEL D81, D8 3, G22, I13)

The Austrian social insurance system regularly publishes statistics on the number of insured persons (pension insurance, health insurance, accident insurance) and on care recipients as well as on the number of employed persons in Austria.

Snapshot img

Insurance Analytics Market Size 2025-2029

The insurance analytics market size is valued to increase by USD 16.12 billion, at a CAGR of 16.7% from 2024 to 2029. Increasing government regulations on mandatory insurance coverage in developing countries will drive the insurance analytics market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Deployment - Cloud segment was valued at USD 4.41 billion in 2023
By Component - Tools segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 328.64 million 
Market Future Opportunities 2024: USD 16123.20 million
CAGR from 2024 to 2029 : 16.7%

Market Summary

The market is experiencing significant growth due to the increasing adoption of data-driven decision-making in the insurance industry and the expanding regulatory landscape. In developing countries, mandatory insurance coverage is becoming more prevalent, leading to an influx of data and the need for advanced analytics to manage risk and optimize operations. Furthermore, the integration of diverse data sources, including social media, IoT, and satellite imagery, is adding complexity to the analytics process. For instance, a global logistics company uses insurance analytics to optimize its supply chain by identifying potential risks and implementing preventative measures. By analyzing historical data on weather patterns, traffic, and other external factors, the company can proactively reroute shipments and minimize disruptions.
Additionally, compliance with regulations such as the European Union's General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) requires insurers to invest in advanced analytics solutions to ensure data security and privacy. Despite these opportunities, challenges remain. The complexity of integrating and managing vast amounts of data from various sources can be a significant barrier to entry for smaller insurers. Additionally, the need for real-time analytics and the ability to make accurate predictions requires significant computational power and expertise. As the market continues to evolve, insurers that can effectively harness the power of data analytics will gain a competitive edge.

What will be the size of the Insurance Analytics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market is a dynamic and ever-evolving landscape, driven by advancements in technology and the growing demand for data-driven insights. According to recent studies, the market is projected to grow by over 15% annually, underscoring its significance in the insurance industry. This growth can be attributed to the increasing adoption of advanced analytics techniques such as machine learning, artificial intelligence, and predictive modeling. One trend that is gaining traction is the use of analytics for solvency II compliance. With the implementation of this regulation, insurers are under pressure to ensure adequate capital and manage risk more effectively.
Analytics tools enable them to do just that, by providing real-time risk assessments, predictive modeling, and capital adequacy modeling. This not only helps insurers meet regulatory requirements but also enhances their risk management capabilities. Another area where analytics is making a significant impact is in customer churn prediction. By analyzing customer data, insurers can identify patterns and trends that indicate potential churn. This enables them to proactively engage with customers and offer personalized solutions, thereby reducing churn and improving customer satisfaction. In conclusion, the market is a critical driver of innovation and growth in the insurance industry.
Its ability to provide actionable insights and enable data-driven decision-making is transforming the way insurers operate, from risk management and compliance to product strategy and customer engagement.

Unpacking the Insurance Analytics Market Landscape

In the dynamic and competitive insurance industry, analytics plays a pivotal role in driving business success. Actuarial data science, with its advanced pricing optimization techniques, enables insurers to set premiums that align with risk profiles, resulting in a 15% increase in underwriting profitability. Risk assessment algorithms, fueled by data mining techniques and real-time risk assessment, improve loss reserving models by 20%, ensuring accurate claim payouts and enhancing customer trust. Data security protocols safeguard sensitive information, reducing the risk of fraud by 30%, as detected by fraud detection systems and claims processing automation. Insurance technology, including business intelligence tools and data visualization dashboards, facilitates data governance frameworks and policy lifecycle management, enab