The data set contains the insurance company wise number of Life insurance claims settled. The information is as per the respective public disclosures of the insurance companies made on IRDAI portal.

China Life Insurance Company: Claim, Casualties & Medical Payment data was reported at 272,924.800 RMB mn in 2022. This records an increase from the previous number of 267,810.750 RMB mn for 2021. China Life Insurance Company: Claim, Casualties & Medical Payment data is updated yearly, averaging 29,625.780 RMB mn from Dec 1997 (Median) to 2022, with 26 observations. The data reached an all-time high of 272,924.800 RMB mn in 2022 and a record low of 1,076.110 RMB mn in 1997. China Life Insurance Company: Claim, Casualties & Medical Payment data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under Global Database’s China – Table CN.RGF: Insurance Industry Overview.

Yunnan: Insurance Premium: Life Insurance Company (LI) data was reported at 50,280.633 RMB mn in 2024. This records an increase from the previous number of 41,532.407 RMB mn for 2023. Yunnan: Insurance Premium: Life Insurance Company (LI) data is updated yearly, averaging 13,278.985 RMB mn from Dec 1999 (Median) to 2024, with 26 observations. The data reached an all-time high of 50,280.633 RMB mn in 2024 and a record low of 1,923.493 RMB mn in 1999. Yunnan: Insurance Premium: Life Insurance Company (LI) data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under China Premium Database’s Insurance Sector – Table CN.RGJ: Insurance Industry: Yunnan.

This Dataset contains year and insurance company-wise (life and non-life) total unclaimed amounts of policyholders transferred to Senior Citizens' Welfare Fund (SCWF) Note: Data is as of March 01, for each year The Senior Citizens’ Welfare Fund Act, 2015 (SCWF) as a part of the Finance Act, 2015, mandates the transfer of Unclaimed Amounts of Policyholders to the Fund (SCWF) after a period of 10 years.

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 |

The dataset contains year-, month- and company-wise complied data on 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

Zhejiang: Insurance Premium: Life Insurance Company (LI) data was reported at 241,100.000 RMB mn in 2024. This records an increase from the previous number of 208,900.000 RMB mn for 2023. Zhejiang: Insurance Premium: Life Insurance Company (LI) data is updated yearly, averaging 45,207.350 RMB mn from Dec 1999 (Median) to 2024, with 25 observations. The data reached an all-time high of 241,100.000 RMB mn in 2024 and a record low of 4,554.281 RMB mn in 1999. Zhejiang: Insurance Premium: Life Insurance Company (LI) data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under China Premium Database’s Insurance Sector – Table CN.RGJ: Insurance Industry: Zhejiang.

This dataset provides a comprehensive view of insurance policyholders, their demographic details, policy information, claims history, and churn status for both life and auto insurance products. It is designed to support predictive modeling of customer attrition, enabling insurers to identify at-risk customers and develop targeted retention strategies. The inclusion of satisfaction scores, contact history, and churn reasons makes it ideal for advanced analytics and customer experience optimization.

Graph and download economic data for Life Insurance Companies; Total Financial Assets, Level (BOGZ1FL544090005Q) from Q4 1945 to Q2 2025 about life, insurance, assets, and USA.

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

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?

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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.

China Life Insurance Company: Matured Payment data was reported at 242,218.930 RMB mn in 2022. This records a decrease from the previous number of 301,217.770 RMB mn for 2021. China Life Insurance Company: Matured Payment data is updated yearly, averaging 95,694.260 RMB mn from Dec 1998 (Median) to 2022, with 25 observations. The data reached an all-time high of 353,168.090 RMB mn in 2016 and a record low of 9,698.400 RMB mn in 2001. China Life Insurance Company: Matured Payment data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under Global Database’s China – Table CN.RGF: Insurance Industry Overview.

In 2023, AIG was deemed the life insurer with the highest customer satisfaction score as per the American Customer Satisfaction Index (ACSI). AIG achieved a customer satisfaction score of 82 out of 100 points, which was an increase of three points year-on-year. Between the years 2022 and 2023, John Hancock experienced the highest increase in its customer satisfaction score, rising from 75 to 80, indicating a growth of nearly five points. However, MassMutual exhibited the lowest customer satisfaction score among all the life insurers, with a score of 77 points.

Comprehensive dataset containing 34 verified Huatai Life Insurance Company Limited locations in China with complete contact information, ratings, reviews, and location data.

Comprehensive dataset containing 232 verified Life insurance agency businesses in Turkey with complete contact information, ratings, reviews, and location data.

Graph and download economic data for Life Insurance Companies; Multifamily Residential Mortgages; Asset, Level (BOGZ1FL543065405A) from 1945 to 2024 about multifamily, life, insurance, mortgage, family, residential, assets, and USA.

Yunnan: Insurance Payment: Life Insurance Company (LI) data was reported at 14,414.967 RMB mn in 2024. This records an increase from the previous number of 11,779.953 RMB mn for 2023. Yunnan: Insurance Payment: Life Insurance Company (LI) data is updated yearly, averaging 2,559.290 RMB mn from Dec 1999 (Median) to 2024, with 26 observations. The data reached an all-time high of 14,414.967 RMB mn in 2024 and a record low of 570.848 RMB mn in 2000. Yunnan: Insurance Payment: Life Insurance Company (LI) data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under China Premium Database’s Insurance Sector – Table CN.RGJ: Insurance Industry: Yunnan.

Hainan: Insurance Premium: Life Insurance Company (LI) data was reported at 11,995.399 RMB mn in 2024. This records an increase from the previous number of 11,583.615 RMB mn for 2023. Hainan: Insurance Premium: Life Insurance Company (LI) data is updated yearly, averaging 3,437.700 RMB mn from Dec 1999 (Median) to 2024, with 25 observations. The data reached an all-time high of 12,522.109 RMB mn in 2019 and a record low of 251.240 RMB mn in 1999. Hainan: Insurance Premium: Life Insurance Company (LI) data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under China Premium Database’s Insurance Sector – Table CN.RGJ: Insurance Industry: Hainan.

Hainan: Life Insurance Company: Insurance Surrender data was reported at 1,883.640 RMB mn in 2022. This records a decrease from the previous number of 2,075.360 RMB mn for 2021. Hainan: Life Insurance Company: Insurance Surrender data is updated yearly, averaging 188.360 RMB mn from Dec 1999 (Median) to 2022, with 23 observations. The data reached an all-time high of 2,775.100 RMB mn in 2017 and a record low of 52.078 RMB mn in 1999. Hainan: Life Insurance Company: Insurance Surrender data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under China Premium Database’s Insurance Sector – Table CN.RGJ: Insurance Industry: Hainan.

Zhejiang: Insurance Payment: Life Insurance Company (LI) data was reported at 54,900.000 RMB mn in 2024. This records an increase from the previous number of 39,600.000 RMB mn for 2023. Zhejiang: Insurance Payment: Life Insurance Company (LI) data is updated yearly, averaging 8,332.390 RMB mn from Dec 1999 (Median) to 2024, with 25 observations. The data reached an all-time high of 54,900.000 RMB mn in 2024 and a record low of 711.048 RMB mn in 1999. Zhejiang: Insurance Payment: Life Insurance Company (LI) data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under China Premium Database’s Insurance Sector – Table CN.RGJ: Insurance Industry: Zhejiang.