Insurance Sector: Liabilities: Current Liabilities data was reported at 88,200.429 NGN mn in Jun 2018. This records a decrease from the previous number of 98,143.615 NGN mn for Mar 2018. Insurance Sector: Liabilities: Current Liabilities data is updated quarterly, averaging 41,258.196 NGN mn from Mar 2008 (Median) to Jun 2018, with 42 observations. The data reached an all-time high of 101,147.472 NGN mn in Dec 2017 and a record low of 16,268.164 NGN mn in Jun 2013. Insurance Sector: Liabilities: Current Liabilities data remains active status in CEIC and is reported by Central Bank of Nigeria. The data is categorized under Global Database’s Nigeria – Table NG.Z006: Insurance Statistics.

Life Insurance Premiums: First Year: General Insurance: Industry data was reported at 425.092 THB mn in Sep 2018. This records an increase from the previous number of 284.877 THB mn for Jun 2018. Life Insurance Premiums: First Year: General Insurance: Industry data is updated quarterly, averaging 304.219 THB mn from Mar 2016 (Median) to Sep 2018, with 11 observations. The data reached an all-time high of 650.465 THB mn in Dec 2017 and a record low of 121.086 THB mn in Mar 2016. Life Insurance Premiums: First Year: General Insurance: Industry data remains active status in CEIC and is reported by Office of Insurance Commission. The data is categorized under Global Database’s Thailand – Table TH.Z030: Life Insurance Statistics.

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.

Introduction

Pet Insurance Statistics: Pet insurance is a type of insurance coverage designed to help pet owners manage the costs associated with veterinary care for their pets.

It provides financial protection in case of unexpected accidents, illnesses, or injuries to pets. Just like health insurance for humans, pet insurance policies come with various coverage options, deductibles, and premium rates.

Historical series of financial data from European insurance enterprises updated on a annual basis.

EIOPA’s new insurance statistics are based on Solvency II regular reporting information from insurance undertakings and groups in the European Union and the European Economic Area (EEA). These statistics provide the most up-to-date and comprehensive picture of the European insurance sector, including country breakdowns and distributions of key variables, allowing for the comparability of high-quality data. Every publication is accompanied by a description of key aspects of the published statistics.

The Group annually publication provide indicators based on insurance group reporting. It includes "Solvency II balance sheet", "own funds/Solvency Capital Requirement (SCR)", and "premiums, claims and expenses" data . The information is available at EEA level.

The value of assets of insurance companies worldwide increased year-on-year from 2002 to 2021, except in 2008 and 2022 when a slight decrease was observed. In 2022, the assets of insurance companies globally amounted to approximately 35.7 trillion U.S. dollars - a decrease of almost five trillion U.S. dollars from the previous year. Overall, the value of assets of global insurance companies has grown by around 20 trillion U.S. dollars since 2002.

The Life Insurance Institution-level Statistics publication contains individual insurer level information about financial performance, position, and capital base and solvency data.

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.

Mandatory Insurance: Annual Benefit data was reported at 15,811.524 IDR bn in 2023. This records a decrease from the previous number of 18,741.891 IDR bn for 2022. Mandatory Insurance: Annual Benefit data is updated yearly, averaging 14,354.371 IDR bn from Dec 2014 (Median) to 2023, with 10 observations. The data reached an all-time high of 18,741.891 IDR bn in 2022 and a record low of 6,699.245 IDR bn in 2015. Mandatory Insurance: Annual Benefit 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.RGA001: Insurance Statistics: Key Indicators.

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.

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 Quarterly Life Insurance Performance Statistics publication provides industry aggregate summaries of financial performance, financial position, solvency, capital adequacy and management capital, as well as details of the performance of individual product groups.

The provided data asset is relational and consists of four distinct data files.

1. address.csv: contains address information

2. customer.csv: contains customer information.

3. demographic.csv: contains demographic data

4. termination.csv: includes customer termination information.

5. autoinsurance_churn.csv: includes merged customer churn data generated from this notebook.

All data sets are linked using either ADDRESS_ID or INDIVIDUAL_ID. The ADDRESS_ID pertains to a specific postal service address, while the INDIVIDUAL_ID is unique to each individual. It is important to note that multiple customers may be assigned to the same address, and not all customers have demographic information available.

Size of the data set

The data set includes 1,536,673 unique addresses and 2,280,321 unique customers, of which 2,112,579 have demographic information. Additionally, 269,259 customers cancelled their policies within the previous year.

Note

Please note that the data is synthetic, and all customer information provided is fictitious. While the latitude-longitude information can be mapped at a high level and generally refers to the Dallas-Fort Worth Metroplex in North Texas, it is important to note that drilling down too far may result in some data points that are located in the middle of Jerry World, DFW Airport, or Lake Grapevine. The physical addresses provided are fake and are unrelated to the corresponding lat/long.

The termination table includes the ACCT_SUSPD_DATE field, which can be used to derive a binary churn/did not churn variable. The data set is modelable, meaning that the other data available can be used to predict which customers churned and which did not. The underlying logic used to make these predictions should align with predicting auto insurance churn in the real world.

Find out about retirement trends in PBGC's data tables. The tables include statistics on the people and pensions that PBGC protects, including how many Americans are in PBGC-insured pension plans, how many get PBGC benefits, and where they live. This data set will be updated periodically. (Updated annually)

This dataset presents estimates of intermediate goods consumed by insurance companies operating in Qatar, disaggregated by company nationality and type of goods. Values are reported in thousands of Qatari Riyals (QR).

Insurance Statistic: Gross Claim: Life & Non Life Insurance data was reported at 229.310 IDR tn in 2023. This records a decrease from the previous number of 232.830 IDR tn for 2022. Insurance Statistic: Gross Claim: Life & Non Life Insurance data is updated yearly, averaging 123.215 IDR tn from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 232.830 IDR tn in 2022 and a record low of 41.268 IDR tn in 2008. Insurance Statistic: Gross Claim: Life & Non Life Insurance 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.RGA004: Insurance Statistics: Market Share.

As of 2023, nearly *** million people in the United States had some kind of health insurance, a significant increase from around *** million insured people in 2010. However, as of 2023, there were still approximately ** million people in the United States without any kind of health insurance. Insurance coverage The United States does not have universal health insurance, and so health care cost is mostly covered through different private and public insurance programs. In 2021, almost ** percent of the insured population of the United States were insured through employers, while **** percent of people were insured through Medicaid, and **** percent of people through Medicare. As of 2022, about *** percent of people were uninsured in the U.S., compared to ** percent in 2010. The Affordable Care Act The Affordable Care Act (ACA) significantly reduced the number of uninsured people in the United States, from **** million uninsured people in 2013 to **** million people in 2015. However, since the repeal of the individual mandate the number of people without health insurance has risen. Healthcare reform in the United States remains an ongoing political issue with public opinion on a Medicare-for-all plan consistently divided.

Dataset

This dataset was created by sidrajaved30

Released under Community Data License Agreement - Sharing - Version 1.0

Contents

Consumer Insurance Experience & Demographic Profile

This dataset provides a detailed view of how individuals engage with insurance products, paired with demographic and lifestyle attributes to enable powerful segmentation, behavioral analysis, and customer journey mapping. By combining real-world insurance experiences with contextual information about each respondent’s background and preferences, this dataset supports a wide range of data-driven decision-making for insurance providers, policy designers, marketing teams, and product strategists.

Value of the Dataset Understanding how consumers perceive and interact with insurance offerings is critical to building products that resonate and services that retain. This dataset offers that visibility across multiple dimensions—capturing not only what type of insurance consumers hold and how they purchased it, but also what drives their satisfaction, loyalty, and likelihood to switch. Paired with demographic details like income, education, family status, and lifestyle, this information becomes a foundation for more personalized outreach, better-designed offerings, and improved customer experiences.

Because the data reflects lived experiences across diverse markets, it is particularly valuable for benchmarking consumer sentiment in emerging economies, identifying service delivery gaps, or evaluating potential uptake of new policy formats such as digital or personalized insurance.

Example Use Cases 1. Targeted Product Design A health insurer looking to launch short-term, digital-first plans could filter this dataset for consumers with low policy tenure, high digital communication preference, and dissatisfaction with current providers. This segment would inform feature design and positioning.

1. Competitive Analysis A provider evaluating churn risk can identify patterns among users who have filed claims but report dissatisfaction—indicating operational areas that may be driving customer loss and where improvements could increase retention.

2. Communication Channel Optimization By analyzing preferred communication methods across different demographic segments, insurers can tailor outreach strategies (e.g., SMS vs. in-app chat) to improve engagement and reduce support costs.

3. Market Expansion & Localization International insurers can explore regional variations in satisfaction drivers, awareness levels, and price sensitivity to refine go-to-market strategies in countries like Senegal, Tanzania, or the UAE.

4. Personalized Policy Offer Design Using data on interest in personalized policies and lifestyle indicators, providers can build customizable offerings for consumers more likely to value flexibility, such as frequent travelers or those with irregular incomes.

Insurance-Specific Fields & Descriptions Current Insurance Type Captures the kind of insurance the individual currently holds, with a focus on health insurance in this dataset.

Purchase Method Indicates how the insurance was obtained—through an agent, online, employer, etc.—to understand acquisition channels.

Policy Length Duration of the current policy, categorized (e.g., less than 1 year, 1–3 years, more than 5 years) to analyze tenure-based behaviors.

Satisfaction Self-reported satisfaction with the current insurance provider, useful for benchmarking sentiment.

Top Factor in Choosing Provider Highlights what influenced the purchase decision most—such as coverage options, customer service, pricing, or brand reputation.

Policy Review Frequency Shows how often individuals revisit their policy details or compare with alternatives, revealing levels of engagement or passive behavior.

Filed Claim A yes/no indicator showing whether the consumer has ever filed a claim, useful for analyzing downstream service experiences.

Claim Satisfaction Measures satisfaction with how past claims were handled, providing insight into operational effectiveness.

Primary Value Sought Captures what consumers value most from their insurance—e.g., peace of mind, financial protection, access to quality care.

Likelihood to Recommend Acts as a proxy for Net Promoter Score (NPS), indicating brand advocacy and potential referral behavior.

Biggest Areas for Improvement Open-ended or multi-select responses identifying where insurers can do better—lower premiums, faster claims, more digital tools, etc.

Preferred Method of Communication Indicates how consumers want to be contacted—via online chat, phone, email, SMS—supporting channel strategy optimization.

Preferred Services Details the types of updates or services consumers want—such as claims status, policy changes, or coverage recommendations.

Insurance Awareness Score Self-reported awareness of how insurance works, including policy options, rights, and terms.

Interest in Personalized Policies Captures whether the individual is open to customized insurance plans, an important indicator for usage-ba...

This file contains premium data taken from the private motor National Claims Information Database (NCID). Premiums and policy numbers are presented on a “written” and “earned” policy basis and further broken down by different levels of cover - comprehensive and third party. To view the detailed NCID report refer to the Central Bank publication link under Additional Info.