Content

Life insurance in force is the total value of life insurance policies that a company has issued. It is normally the sum of face amounts plus dividends outstanding that a company would have to pay out at the death of an individual. The amounts given here are for policyholders who reside in New York.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by rawpixel on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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 |

Adaptationist models predict that individuals at higher risk of death will be calibrated to prioritize immediate over future benefits. However, operationalizing individual mortality risk in empirical studies has proven challenging. We introduce and explore a novel method of operationalizing individual mortality risk: Using the risk ratings assigned by actuaries to purchasers of individual life insurance policies. Participants, who had recently gone through underwriting as part of the insurance application process, completed self-report instruments to assess personality traits related to present-future tradeoffs and a putative fast-slow continuum of life history strategy. Study 1 (n = 270) found that insurance-based mortality risk associated negatively with a measure of slow life strategy and positively with a measure of short-term mating orientation. Study 2 (n = 402), which was preregistered, found that insurance-based mortality risk associated positively with impulsivity and negativel..., , # Mortality risk estimates from life insurance policies predict individual differences in human behavioral traits

Data were collected from two U.S. online participant samples (N = 270 and N = 402), screened to include only individuals who had purchased individual life insurance policies within the past five years. In both data sets, participants were asked for the risk ratings they had been assigned by the insurance company, and to complete self-report instruments measuring constructs relevant to psychometric life history (especially the present-future trade-off). In the second data set, participants were also asked to indicate their self-estimated lifespan, and were asked to complete three instruments measuring recalled childhood environmental harshness. R code used to analyze the data is also provided.

Description of the Data and file structure

Lukaszewski-Manson-Study-1-R.csv

Key to column headings

female: 1 = yes

age_bin: (1 = younger than 25 years, 2 = 25-29, 3 =..., We have received explicit consent from our participants to publish the de-identified data in the public domain. We have de-identified the data by removing all individually identifying information (IP addresses, and for the six in-person participants in Study 2, their names) from data files before uploading them.

Insurance Dataset for Predicting Health Insurance Premiums in the US" is a collection of data on various factors that can influence medical costs and premiums for health insurance in the United States. The dataset includes information on 10 variables, including age, gender, body mass index (BMI), number of children, smoking status, region, income, education, occupation, and type of insurance plan. The dataset was created using a script that generated a million records of randomly sampled data points, ensuring that the data represented the population of insured individuals in the US. The dataset can be used to build and test machine learning models for predicting insurance premiums and exploring the relationship between different factors and medical costs.

This is a data set of individuals in Maryland, Michigan, New Mexico, Ohio, Puerto Rico, South Carolina, Texas, and Virginia who are looking for life insurance. The data can be segmented and ordered based on State, zip code, city, and age. The dates the data was collected were from 07/01/2022 - 10/04/2022. Please feel free to reach out if you have any questions about this data set.

End-Period-Cash-Flow Time Series for China Life Insurance Co Ltd A. China Life Insurance Company Limited, together with its subsidiaries, operates as a life insurance company in the People's Republic of China. The company operates through: Life Insurance Business, Health Insurance Business, Accident Insurance Business, and Other Businesses segments. It offers life, annuity, health, and accident insurance products to individuals and groups. The company was founded in 1949 and is based in Beijing, the People's Republic of China. China Life Insurance Company Limited is a subsidiary of China Life Insurance (Group) Company.

The Iowa Insurance Division is responsible for issuing licenses or authority for many types of regulated individuals dealing with insurance related products. Individuals interested in becoming either a resident or non-resident insurance producer licensed in the state of Iowa need to apply through the National Insurance Producer Registry (NIPR) online system. Those wishing to become a resident insurance producer licensed in the state of Iowa must successfully pass the appropriate Iowa producer licensing exam for that specific line of authority.

To add additional lines of authority, a resident or non-resident insurance producer licensed in the state of Iowa need to apply through the NIPR online system. Resident insurance producers wishing to add a line of authority must successfully pass the appropriate Iowa producer licensing exam for that specific line of authority.

This dataset provides a listing of resident and non-resident insurance producers licensed to sell to Iowans.

Total-Cash-From-Operating-Activities Time Series for China Life Insurance Co Ltd A. China Life Insurance Company Limited, together with its subsidiaries, operates as a life insurance company in the People's Republic of China. The company operates through: Life Insurance Business, Health Insurance Business, Accident Insurance Business, and Other Businesses segments. It offers life, annuity, health, and accident insurance products to individuals and groups. The company was founded in 1949 and is based in Beijing, the People's Republic of China. China Life Insurance Company Limited is a subsidiary of China Life Insurance (Group) Company.

Financial Access and Usage

Global Comparative Ratios Across 189 Jurisdictions

By International Monetary Fund [source]

About this dataset

This dataset provides an unprecedented opportunity to explore global financial access and usage trends from 2004-2016 from 189 of the world's reporting jurisdictions—which cover over 99 percent of the total adult population. With 152 time series and 47 indicator ratios, this Financial Access Survey gives insight into ways that access to and usage of financial services differ by households vs small/medium enterprises, life vs non-life insurance, deposits & microfinance institutions as well as credit unions & financial cooperatives. Utilizing this data, we can gain a better understanding of how policies or shifts in the global economy may influence or relate to access or utilization of services in certain regions while having comparable cross-economy comparisons. The IMF Monetary and Financial Statistics Manual Compilation Guide is utilized for all methodologies used in accumulating these datasets, while all data is available “as-is” with no guarantee provided either express or implied. Are you looking for ways to implement insightful macroeconomic analyses? Download FAS 2004–2016 now!

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The Financial Access Survey provides global supply-side data on access to and usage of financial services by households and firms for 189 reporting jurisdictions, covering 99 percent of the world’s adult population. With a robust selection of time series in this dataset, users can make meaningful insights into trends over time or across countries concerning various indicators related to access and usage of financial services. To help users navigate this large dataset, the following guide explains how to use the data most effectively.

Understanding The Dataset Columns

The columns in the dataset provide information about each indicator such as country name, indicator name, code for that indicator, its attribute (i.e., rate/ratio), when data is available for that particular indicator. Once you have identified an interesting measure/indicator whether it be credit union density or life insurance penetration rate measure in a given country during a certain year period then you can look up those numbers from the rows provided in this dataset .

Understanding The Different Years Available & Comparing Numbers Over Time

It is useful for users to compare different indicators over time by looking at specific years within this dataset which will allow us to see if rates are increasing or decreasing worldwide patterns across these trends among different countries based on these various measures listed provided in this survey such as mortgage lending rate or ratio GDP per capita etc that have been collected . We can therefore make use of our knowledge off economic changes that have occurred over time within certain parts of world , no matter if they are longer term economic effects due increases certain capabilities within a geographical area or shorter term changes due taxation laws by governments etc driving some people either towards using or away from using certain kinds financial products .

#### Comparing Between Countries

This datasets allows us direct comparisons between different countries with regards how many people are currently making use particular types off finances services , we certainly be able analyse current international relationships between services providers as well customers where ever concerned about particular attributes mentioned above whether being deposit interest rates small business credits terms tenders so forth . Knowing more about related dynamics helps build better user experiences with providers who understand needs risks impacts generating larger customer bases globally which often beneficial both parties involved exchange relationship so not forget always keep cross border motif whenever eye process from afar !

Research Ideas

• Comparing the access to and usage of financial services in different countries to better inform research policy decisions.
• Analyzing trends in financial access and usage by jurisdiction over time, to identify areas needing improvement in order to promote financial inclusion and stability.
• Cross-referencing FAS data with macroeconomic indicators such as GDP information to measure the potential impact of changes in level of access on economic growth or other metrics specific to a country or region of interest

Acknowledgements

If you use this dataset in yo...

Price-To-Book-Ratio Time Series for Samsung Life. Samsung Life Insurance Co., Ltd., together with its subsidiaries, engages in the life insurance business in South Korea and internationally. It operates through Domestic Insurance; Domestic Credit Card Service, Installment Financing and Leasing; and Overseas segments. The company offers pension, savings, life, death, life and death mixed, group insurance, and retirement pensions. insurance contracts, mortgage, secured, and home mortgage loans. It also provides credit card services, and installment financing and leasing services. In addition, the company engages in the provision of real estate lease, investment and trust management, specialized credit finance, collective and real estate collective investment vehicle, asset-backed securitization, and new technology business investment association services. It serves individuals and enterprises. The company was formerly known as Dongbang Life Insurance and changed its name to Samsung Life Insurance Co., Ltd. in July 1989. Samsung Life Insurance Co., Ltd. was founded in 1957 and is headquartered in Seoul, South Korea.

Interest-Expense Time Series for Samsung Life. Samsung Life Insurance Co., Ltd., together with its subsidiaries, engages in the life insurance business in South Korea and internationally. It operates through Domestic Insurance; Domestic Credit Card Service, Installment Financing and Leasing; and Overseas segments. The company offers pension, savings, life, death, life and death mixed, group insurance, and retirement pensions. insurance contracts, mortgage, secured, and home mortgage loans. It also provides credit card services, and installment financing and leasing services. In addition, the company engages in the provision of real estate lease, investment and trust management, specialized credit finance, collective and real estate collective investment vehicle, asset-backed securitization, and new technology business investment association services. It serves individuals and enterprises. The company was formerly known as Dongbang Life Insurance and changed its name to Samsung Life Insurance Co., Ltd. in July 1989. Samsung Life Insurance Co., Ltd. was founded in 1957 and is headquartered in Seoul, South Korea.

Net-Interest-Income Time Series for CNO Financial Group Inc. CNO Financial Group, Inc., through its subsidiaries, develops, markets, and administers health insurance, annuity, individual life insurance, insurance products, and financial services for middle-income pre-retiree and retired Americans in the United States. It offers Medicare supplement, supplemental health, and long-term care insurance policies; life insurance; and annuities, as well as Medicare advantage plans to individual consumers through phone, virtually, online, and face-to-face with agents. The company also focuses on sale of voluntary benefit life and health insurance products for businesses, associations, and other membership groups by interacting with customers at their place of employment. In addition, it provides fixed indexed annuities; fixed interest annuities, including fixed rate single and flexible premium deferred annuities; single premium immediate annuities; supplemental health products, such as specified disease, accident, and hospital indemnity products; and long-term care plans primarily to retirees, lesser degree, and older self-employed individuals in the middle-income market. Further, the company offers universal life and other interest-sensitive life products; and traditional life policies that include whole life, graded benefit life, term life, and single premium whole life products, as well as graded benefit life insurance products. It markets its products under the Bankers Life, Washington National, and Colonial Penn brand names. The company was founded in 1979 and is headquartered in Carmel, Indiana.

Minority-Interest-Expense Time Series for Samsung Life. Samsung Life Insurance Co., Ltd., together with its subsidiaries, engages in the life insurance business in South Korea and internationally. It operates through Domestic Insurance; Domestic Credit Card Service, Installment Financing and Leasing; and Overseas segments. The company offers pension, savings, life, death, life and death mixed, group insurance, and retirement pensions. insurance contracts, mortgage, secured, and home mortgage loans. It also provides credit card services, and installment financing and leasing services. In addition, the company engages in the provision of real estate lease, investment and trust management, specialized credit finance, collective and real estate collective investment vehicle, asset-backed securitization, and new technology business investment association services. It serves individuals and enterprises. The company was formerly known as Dongbang Life Insurance and changed its name to Samsung Life Insurance Co., Ltd. in July 1989. Samsung Life Insurance Co., Ltd. was founded in 1957 and is headquartered in Seoul, South Korea.

Total-Cash-From-Operating-Activities Time Series for Samsung Life. Samsung Life Insurance Co., Ltd., together with its subsidiaries, engages in the life insurance business in South Korea and internationally. It operates through Domestic Insurance; Domestic Credit Card Service, Installment Financing and Leasing; and Overseas segments. The company offers pension, savings, life, death, life and death mixed, group insurance, and retirement pensions. insurance contracts, mortgage, secured, and home mortgage loans. It also provides credit card services, and installment financing and leasing services. In addition, the company engages in the provision of real estate lease, investment and trust management, specialized credit finance, collective and real estate collective investment vehicle, asset-backed securitization, and new technology business investment association services. It serves individuals and enterprises. The company was formerly known as Dongbang Life Insurance and changed its name to Samsung Life Insurance Co., Ltd. in July 1989. Samsung Life Insurance Co., Ltd. was founded in 1957 and is headquartered in Seoul, South Korea.

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.

The Texas Department of Insurance (TDI) is responsible for licensing, registering, certifying, and regulating people who sell insurance or adjust property and casualty claims in Texas. This data set includes a row for each license held by a person. A person with more than one license will be listed in multiple rows. To view the list of agencies and business licensed by TDI, go to the Insurance agencies data set. To learn more about the type of licenses in this data set, go to TDI’s agent and adjuster licensing webpage.

First, a caveat: the NFIP data does NOT provide information specific to individual homes or parcels. This information is protected under federal law. All personal identifying information about policy holders has been redacted, and data has been anonymized to census tract, reported ZIP code, and one decimal point digit of latitute and longitude. If mapped, flood insurance policies and claims may appear to be clustered at a particular location due to this anonymization. What all that means: you cannot search for an address to see whether it has flooded. However, among many things, this data shows flooding trends in Norfolk over the last 40+ years. It shows the census tracts that flood most frequently. And it shows where the largest number and highest value of claims occur.

FEMA believes this historic release of NFIP data promotes transparency, reduces complexity related to public data requests, and improves how stakeholders interact with and understand the program. This is the largest, most comprehensive release of NFIP data coordinated by FEMA to date. This dataset allows for customizable searches to create reports, analyze and visualize present and historical NFIP data faster and easier than before. This data will help FEMA build a national culture of preparedness by providing claims and policy information people need to make better choices about their flood risk and the insurance they need to protect the life they've built. Norfolk's Open Data team extracted city-specific information from the FEMA dataset. The dataset included here represents almost 6,000 claims on record from 1977 through 2019, totaling 67 million dollars in damage in the City of Norfolk.

To view the most updated version of the dataset, please click here: https://data.norfolk.gov/Government/FEMA-NFIP-Claims/suf7-r643/about_data

SUMMARY This table contains data about women, ages 15 to 50, pregnant people, infants, children, and youths, up to age 24. It contains information about a wide range of health topics, including medical conditions, nutrition, dehydration, oral health, mental health, safety, access to health care, and basic needs, like housing. Local, county-level prevalence rates, time trends, and health disparities about national public health priorities, including preterm birth, infant death, childhood obesity, adolescent depression and substance use, and high blood pressure, diabetes, and kidney disease in young adults. The population data is from the 2023-2024 San Francisco Maternal Child and Adolescent Health needs assessment and is published on the Open Data Portal to share with community partners, plan services, and promote health. For more information see: Maternal, Child, and Adolescent Health Homepage Maternal, Child, and Adolescent Health Reports HOW THE DATASET IS CREATED The Maternal, Child, and Adolescent Health (MCAH) Needs Assessment for San Francisco included review of a wide range of citywide population data covering a ten-year span, from 2014 to 2023. Data from over 83,000 birth records, 59,000 death records, 261,000 emergency room visits, 66,000 hospital admissions, and 90,000 newborn screening discharges were gathered, along with citywide data from child welfare records, health screenings in childcare and schools, DMV records of first-time drivers, school surveys, and a state-run mailed survey of recent births (California Department of Public Health MIHA survey). The datasets provided information about approximately 700 health conditions. Each health condition was described in terms of the number of people affected or cases, and the rate affected, stratified by age, sex, race-ethnicity, insurance status, zip code, and time period. Rates were calculated by dividing the number of people or events by the population group estimate (e.g., total births or census estimates), then multiplying by 100 or 1,000 depending on the measure. Each rate was presented with its 95% confidence interval to support users to compare any two rates, either between groups or over time. Two rates differ “significantly” if their 95% confidence intervals do not overlap. The present dataset summarizes the group-level results for any age-, sex-, race-, insurance-, zip code-, and/or period-specific group that included at least 20 people or cases. Causes of death, health conditions that affected over 1000 people in the time frame, problems that got worse over time, and health disparities by insurance, race-ethnicity and/or zip code were flagged for the MCAH Needs Assessment. UPDATE PROCESS The dataset will be updated manually, bi-annually, each December and June. HOW TO USE THIS DATASET Population data from the MCAH needs assessment are shared in several formats, including aggregated datasets on DataSF.gov, downloadable PDF summary reports by age group, interactive online visualizations, data tables, trend graphs, and maps. Information about each variable is available in a linked data dictionary. The definition of each numerator and denominator depends on data source, life stage, and time. Health conditions may not be directly comparable across life stage, if the numerator definition includes age- or pregnancy-specific diagnosis codes (e.g. diabetes hospitalization). For small groups or rare conditions, consider combining time periods and/or groups. Data are suppressed if fewer than 20 cases happened in the group and period. Group-specific rates are available if the matched group-specific census estimates (denominator) were available. Census estim

Context

Getting proper data for survival analysis is often difficult.

Content

This data represents entry dates, departure dates and other information about fictional clients of a life insurance company. You have the age at which the insured entered the contract, the age at which he left, and the reason : either death or withdrawal, equivalent for us to right-censorship since the actual age at death of the person will no longer be observed. The data are left-truncated at the 1st of January 1820 : you only know if a client was present before that date, but you have no idea for how long he's been there.

Acknowledgements

Entirely generated using the numpy.random module, source code attached. For the survival analysis notebooks to come, my theoretical basis is the excellent course of Duration Models by Olivier Lopez at ENSAE Paris.

Inspiration

Develop some survival analysis and duration models tools to estimate death or departure of your clients as accurately as possible !

End-Period-Cash-Flow Time Series for CNO Financial Group Inc. CNO Financial Group, Inc., through its subsidiaries, develops, markets, and administers health insurance, annuity, individual life insurance, insurance products, and financial services for middle-income pre-retiree and retired Americans in the United States. It offers Medicare supplement, supplemental health, and long-term care insurance policies; life insurance; and annuities, as well as Medicare advantage plans to individual consumers through phone, virtually, online, and face-to-face with agents. The company also focuses on sale of voluntary benefit life and health insurance products for businesses, associations, and other membership groups by interacting with customers at their place of employment. In addition, it provides fixed indexed annuities; fixed interest annuities, including fixed rate single and flexible premium deferred annuities; single premium immediate annuities; supplemental health products, such as specified disease, accident, and hospital indemnity products; and long-term care plans primarily to retirees, lesser degree, and older self-employed individuals in the middle-income market. Further, the company offers universal life and other interest-sensitive life products; and traditional life policies that include whole life, graded benefit life, term life, and single premium whole life products, as well as graded benefit life insurance products. It markets its products under the Bankers Life, Washington National, and Colonial Penn brand names. The company was founded in 1979 and is headquartered in Carmel, Indiana.