Based on 2023 brokerage revenues from U.S. clients, the largest insurance broker in the United States is Marsh & McLennan Cos Inc. At this time, the New York-based professional services firm reported revenues from U.S. insurance broking of over 10.7 billion U.S. dollars. The next largest insurance broker in the U.S. market - Aon - is a UK-based company with 7.7 billion U.S. dollars in brokerage revenue from the U.S. market.

Gross Written Premiums: Health data was reported at 8,514,432.553 SAR th in Sep 2023. This records a decrease from the previous number of 8,849,178.998 SAR th for Jun 2023. Gross Written Premiums: Health data is updated quarterly, averaging 4,573,232.320 SAR th from Mar 2009 (Median) to Sep 2023, with 59 observations. The data reached an all-time high of 12,555,928.187 SAR th in Mar 2023 and a record low of 821,126.645 SAR th in Jun 2009. Gross Written Premiums: Health data remains active status in CEIC and is reported by Saudi Central Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.Z020: Insurance Statistics. [COVID-19-IMPACT]

As of 2024, insurance industry premiums worldwide had experienced a real growth rate of 4.6 percent. This growth has been expected to continue as the compound annual growth rate (CAGR) worldwide from 2025 to 2026 has been expected to reach 2.5 percent.

A collection of insurance datasets from real insurers or mutual companies, mostly from Europe and North America. Datasets can be used to model and understand risks in both life and non-life insurance.

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.

Oregon workers' compensation data about insurers and self-insured employers. The data is presented in the Department of Consumer and Business Services report at https://www.oregon.gov/dcbs/reports/compensation/Pages/index.aspx. The attached pdf provides definitions of the data.

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.

Prediction of Insurance Charges Using Age, Gender, BMI

A Study of Customers Insurance Charges

By Bob Wakefield [source]

About this dataset

This dataset contains detailed information about insurance customers, including their age, sex, body mass index (BMI), number of children, smoking status and region. Having access to such valuable insights allows analysts to get a better view into customer behaviour and the factors that contribute to their insurance charges. By understanding the patterns in this data set we can gain useful insight into how age,gender and lifestyle choices can affect a person's insurance premiums. This could be of great value when setting up an insurance plan or marketing campaigns that target certain demographics. Furthermore, this dataset provides us with an opportunity to explore deeper questions such as what are some possible solutions for increasing affordability when it comes to dealing with high charges for certain groups?

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset can be used to predict insurance charges based on the age, sex, and BMI of a customer. The data has been gathered from a variety of sources and contains information such as age, gender, region and bmi values for each customer.

To make use of this dataset you will first need to understand the different variables present in it so you can understand which ones have an impact on predicting insurance charges. Age is expectedly one of the most important variables as younger or older customers may pay less or more respectively for their coverallsure policies. Similarly sex is also influential as traditionally gender roles dictate premiums with men paying more than women for the same coverage on many policies historically speaking. Lastly bmi should also be taken into account when making any predictions regarding insurance costs due to varying factors such as risk factors associated with obesity being taken into consideration by premium pricing decisions made by insurers.

Once having understood how all these elements influence pricing decisions it is then time to explore potential predictive models that could accurately calculate an appropriate amount/estimation based off what you know about a customer's characterisitcs. You may find regression based models most useful here however there are other options out there too so make sure you spend enough time researching before designing your systems architecture entirely around one particular model type.

The data provided should provide all that's required in order to ascertain these correlations between features however further refinements could result from additional customer related features being inputted such as driving history or past claims experience etc but again this information may not have been kept/provided within this dataset!

In conclusion this dataset provides a decent starting point for predicting accurate numerical output using various combinations of characteristic related inputs - have fun creating something amazing!

Research Ideas

• Using age, sex and bmi to create an algorithm for assessing life insurance costs.
• Predicting costs for certain patients based on their sex, age, bmi and region to help doctors decide what treatments work best financially for them.
• Creating a cost calculator that takes into account the patient’s age, sex, smoker status, region of residence and other factors to accurately predict the medical bills a person will pay in a year

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: insurance.csv | Column name | Description | |:--------------|:---------------------------------------------------| | Age | The age of the customer. (Integer) | | Children | The number of children the customer has. (Integer) | | Smoker | Whether or not the customer is a smoker. (Boolean) | | Region | The region the customer lives in. (String) | | Charges | The insurance charges for the customer. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Bob Wakefield.

Property Insurance Complaints Statistics (Insurance Industry Development Center)

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.

Dataset

This dataset was created by xiaomengsun

Contents

It is forecast that the global insurance market will grow by about ************ U.S. dollars between 2024 and 2029, reaching almost ** trillion U.S. dollars. How have gross premiums written evolved? Gross premiums written signify the total premiums collected by an insurer before deducting reinsurance and other related expenses. Between 2000 and 2020, the value of gross premiums written worldwide had more than doubled. The value of premiums written hit its peak in 2017, at approximately **** billion U.S. dollars, after which it continued to decline for the following years until 2019. However, in 2020, this figure grew by nearly **** percent as compared to the previous year. Which companies dominate the insurance market? In 2022, the leading global insurance companies by revenue were Berkshire Hathaway, Ping An Insurance and China Life Insurance. Considering the market capitalization of the largest insurance companies, Allianz occupied the first position with a valuation of nearly *** billion U.S. dollars. These industry titans, along with others such as AXA, AIA, MetLife, Chubb, etc., collectively shape the global insurance narrative through their extensive reach, diverse offerings, and significant market influence.

The Medical Insurance Cost dataset contains information about individuals’ demographic, lifestyle, and health-related factors, along with their corresponding medical insurance charges. It is typically used for predictive modeling, statistical analysis, and machine learning tasks such as regression.

Common Columns in the Dataset

Age Age of the insured person (in years).

Sex Gender of the insured individual (male/female).

BMI Body Mass Index, a measure of body fat based on height and weight.

Children Number of dependents covered by the insurance (e.g., 0, 1, 2, etc.).

Smoker Smoking status of the person (yes/no).

Region Residential area of the insured (e.g., northeast, northwest, southeast, southwest).

Charges Final medical insurance cost billed by the insurance company

Purpose of the Dataset

To analyze the factors influencing health insurance costs.

To build regression models predicting insurance charges.

To understand the relationship between lifestyle (like smoking, BMI) and medical expenses.

Useful for actuarial science, healthcare analytics, and machine learning projects.**

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 |

Data Description: The data at hand contains medical costs of people characterized by certain attributes. Domain: Healthcare Context: Leveraging customer information is paramount for most businesses. In the case of an insurance company, attributes of customers like the ones mentioned below can be crucial in making business decisions. Hence, knowing to explore and generate value out of such data can be an invaluable skill to have. Attribute Information: age: age of primary beneficiary sex: insurance contractor gender, female, male bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9 children: Number of children covered by health insurance / Number of dependents smoker: Smoking region: the beneficiary's residential area in the US, northeast, southeast, southwest, northwest. charges: Individual medical costs billed by health insurance. Learning Outcomes:  Exploratory Data Analysis  Practicing statistics using Python  Hypothesis testing

In 2023, the Health Insurance Market reached a value of USD 2,476 billion, and it is projected to surge to USD 3,974 billion by 2030.

The VIP Data Sets provide the text and numeric information extracted from Forms N-3, N-4 and N-6 – the registration forms for variable annuity contracts and contracts offering Index-Linked Options and/or Fixed Options subject to a contract adjustment – filed with the Commission in eXtensible Business Reporting Language (XBRL). The data is presented in a flat file format to assist users in constructing the data for analysis. The data has been automatically and directly taken from submissions created by the registrants and provided as filed with the Commission. The data sets only include publicly available information from filings that have been disseminated by the Commission.

The General Insurance Institution-level Statistics publication contains individual insurer and insurance group information about financial performance, position, and capital base and capital adequacy.

Reinsurance: Premium Adequacy to Claim Paid Ratio data was reported at 0.000 % mn in Feb 2025. This records a decrease from the previous number of 0.000 % mn for Jan 2025. Reinsurance: Premium Adequacy to Claim Paid Ratio data is updated monthly, averaging 0.000 % mn from Jan 2016 (Median) to Feb 2025, with 110 observations. The data reached an all-time high of 0.001 % mn in Jan 2024 and a record low of 0.000 % mn in Dec 2020. Reinsurance: Premium Adequacy to Claim Paid Ratio data remains active status in CEIC and is reported by Indonesia Financial Services Authority. The data is categorized under Indonesia Premium Database’s Insurance Sector – Table ID.RGA006: Insurance Statistics: Claim Ratio.

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 variable.