Explore the booming Actuarial Software for Insurance Pricing market, projected to reach $860 million by 2025 with a 6.6% CAGR. Discover key drivers, trends, and segments shaping this critical industry sector.

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  📊 ActuarialGPT Conversations Dataset









  Precision Mathematical Conversations for Insurance Intelligence















  🎯 Quick Facts

Feature Description

Domain Actuarial Science, Insurance Analytics, Risk Management

Language English (Technical/Expert Level)… See the full description on the dataset page: https://huggingface.co/datasets/manuelcaccone/actuarial-gpt-conversations.

Context & Inspiration

Understanding the drivers of healthcare costs is a foundational challenge in actuarial science and predictive modeling. While traditional datasets often rely strictly on basic demographics (age, BMI, smoking status), this extended dataset provides a more holistic view of a beneficiary. By integrating lifestyle choices (exercise frequency), socioeconomic indicators (annual income, occupation risk), and broader health metrics (blood pressure, pre-existing conditions), this dataset allows for the development of highly nuanced, multidimensional regression models to predict medical premiums.

Column Descriptions

Instructions

1. Analyze the dataset using the claims data resource

Examine the data: Start by thoroughly examining the dataset within the Claims Data resource. Focus on key variables such as claim dates, types of claims, amounts claimed, and additional details about the incidents. Manipulate the data: Derive the missing values in columns F, O, P, and Q. Use hints if needed. This step emphasizes data manipulation, a key component of account pricing analysis. Identify patterns and anomalies: Conduct EDA using the data in the Claims Data resource. Identify patterns, trends, and anomalies. Utilize visual tools such as histograms, scatter plots, and bar charts within Excel to help you visualize and interpret the data. 2. Apply actuarial principles to the data

Risk assessment: Use the actuarial principles you learned in Task 1 to assess the risks associated with the claims data. Calculate key metrics such as claim frequency, severity, and loss ratios based on the data provided. Calculate premiums: Develop a pricing model using experience-based rating. This involves adjusting historical data from the Claims Data resource to project future claims costs, considering factors such as inflation and changes in exposure. 3. Develop comprehensive reports in Excel

Analysis report: Compile your findings: Organize your EDA into a well-structured section within the Excel workbook. This section should include a detailed evaluation of the Marine Liability insurance claims data, visualizations of key findings, and a commentary on observed trends and anomalies. Commentary on risks and uncertainties: Provide a clear commentary on the risks and uncertainties associated with your assessment. Discuss how different scenarios could impact the pricing model and the potential financial implications for Oceanic Shipping Co. Pricing calculation: Perform a numbers-based premium calculation: Use the Claims Data resource to calculate the appropriate premiums for the Marine Liability insurance policy. Apply actuarial principles such as loss frequency, loss severity, and pure premium calculation, and adjust for expenses and profit margins. Sensitivity analysis: Include a sensitivity analysis within the Excel workbook to assess how changes in key assumptions (e.g., an increase in loss severity) could impact the final premium. Document your calculations: Ensure your premium calculation section in Excel clearly documents your methodology, assumptions, and final premium recommendations. Discuss the potential risks and uncertainties in your pricing model, including any external factors that could impact future claims.

Model Validation for Actuarial Models Market Outlook

As per our latest research, the global model validation for actuarial models market size stood at USD 1.82 billion in 2024, with a robust growth trajectory expected over the next decade. The market is forecasted to reach USD 4.39 billion by 2033, expanding at a CAGR of 10.2% during the forecast period. A significant growth factor for this market is the rising regulatory scrutiny and the increasing complexity of actuarial models used in the insurance and financial sectors, which necessitate rigorous validation protocols to ensure model accuracy, reliability, and compliance.

The growth of the model validation for actuarial models market is underpinned by the escalating demand for robust risk assessment frameworks in the insurance industry. With insurers facing evolving risks from climate change, pandemics, and cyber threats, the reliance on sophisticated actuarial models has intensified. These models are fundamental in predicting future liabilities, setting premiums, and managing reserves. However, as models grow in complexity, so does the risk of model errors, bias, or mis-specification. This has prompted organizations to invest heavily in advanced model validation solutions, encompassing statistical testing, back-testing, and peer review, to mitigate operational and reputational risks. The trend is further accelerated by the adoption of artificial intelligence and machine learning techniques in actuarial science, which, while enhancing predictive accuracy, also introduce new validation challenges.

Another key driver is the tightening regulatory environment across global markets. Regulatory bodies such as the International Association of Insurance Supervisors (IAIS), Solvency II in Europe, and the National Association of Insurance Commissioners (NAIC) in North America have established stringent guidelines for model governance and validation. Compliance with these frameworks requires insurers and reinsurers to demonstrate the integrity of their actuarial models through rigorous validation processes, documentation, and independent review. The increasing frequency of regulatory audits and the imposition of penalties for non-compliance have compelled organizations to prioritize model validation as a critical component of their risk management and governance strategies. This regulatory push is expected to sustain long-term demand for model validation services and solutions.

Technological advancements are also playing a pivotal role in market expansion. The integration of big data analytics, cloud computing, and automation tools is revolutionizing the model validation landscape. These technologies enable real-time validation, facilitate the management of large and complex datasets, and enhance the transparency and reproducibility of validation outcomes. Moreover, the growing trend of outsourcing model validation services to specialized consulting firms is gaining traction, particularly among small and medium-sized insurers lacking in-house expertise. This shift is fostering innovation in validation methodologies and expanding the addressable market for service providers.

Regionally, North America remains the largest market for model validation in actuarial models, accounting for over 38% of global revenue in 2024, followed closely by Europe and the Asia Pacific. The dominance of North America is attributed to its mature insurance sector, advanced regulatory frameworks, and high adoption of technology-driven validation solutions. In contrast, the Asia Pacific region is witnessing the fastest growth, driven by the rapid expansion of insurance markets, increasing regulatory awareness, and the digital transformation of financial services. Latin America and the Middle East & Africa are also emerging as promising markets, albeit from a lower base, as insurers in these regions modernize their risk management practices and align with international regulatory standards.

Model Type Analysis

The model validation for actuarial models market is segmented by model type into Life Insurance Models, Health Insurance Models, Property & Casualty Insurance Models, Pension Models, and Others. Among these, Life Insurance Models hold the largest market share, underpinned by the sheer volume and complexity of long-term liabilities managed by life insurers. The validation of life insurance models is critical due to their reliance on numerous assumptions regarding mortality, lap

This dataset comprises weekly COVID-19 statistics for Jakarta from March 21, 2020, to December 31, 2022, including counts of active, recovered, and deceased cases. The dataset is intended to support rigorous epidemiological analyses, actuarial risk assessments, and public-health research that require temporally resolved measures of infection burden and clinical outcomes during the COVID-19 pandemic.

About This Dataset

This dataset contains a fully synthetic health insurance portfolio designed for actuarial pricing, risk modelling, and data science experimentation.

The data was generated programmatically using stochastic simulation techniques to replicate realistic relationships commonly observed in individual health insurance portfolios. It follows a frequency–severity framework, where claim counts and total medical charges are modelled separately and combined at the policyholder level.

All observations are artificial and were created solely for educational, analytical, and demonstration purposes. The dataset does not contain any real individuals, proprietary information, or personally identifiable data.

Dataset Structure

Each row represents a single insured individual with the following variables: - Age: Policyholder age (18–85) - Sex: Biological sex (male / female) - BMI: Body Mass Index, generated with demographic and behavioural effects - Smoker: Smoking status (yes / no) - Region: Geographic region (southeast, southwest, northeast, northwest) - Children: Number of dependants - ClaimCount: Annual number of health insurance claims (Poisson-distributed) - Charges: Total annual medical charges, conditional on claim occurrence (lognormal severity)

Modelling Assumptions

Claim frequency increases with age, BMI, smoking status, number of children, and regional risk factors.

Claim severity is modelled using a lognormal distribution with demographic and behavioural risk adjustments.

Total charges are computed as the product of claim count and individual claim severities.

These assumptions are intended to mirror common actuarial pricing logic rather than reproduce any specific insurer’s experience.

Intended Use Cases

This dataset is suitable for: - Health insurance pricing models - Frequency–severity modelling (Poisson, Gamma, Lognormal, Tweedie, etc.) - Generalized Linear Models (GLMs) - Machine learning applications in insurance - Teaching and demonstration of actuarial concepts - Kaggle notebooks and tutorials

Licence

The dataset is released under a CC0 (Public Domain) licence. It may be freely used, modified, and redistributed for both commercial and non-commercial purposes without restriction.

Satellite Deorbiting Service Liability Insurance Market Outlook

According to our latest research, the global Satellite Deorbiting Service Liability Insurance market size reached USD 1.14 billion in 2024, driven by the increasing deployment of satellites and growing regulatory scrutiny over space debris mitigation. The market is projected to expand at a robust CAGR of 12.7% from 2025 to 2033, reaching a forecasted value of USD 3.39 billion by 2033. This growth is underpinned by heightened awareness around orbital sustainability, the proliferation of commercial satellite launches, and evolving international liability frameworks. As per our latest research, the market is witnessing significant traction from both governmental and private sectors, with insurers rapidly innovating to address emerging risks in the orbital environment.

One of the primary growth factors propelling the Satellite Deorbiting Service Liability Insurance market is the exponential increase in satellite launches, particularly in Low Earth Orbit (LEO). The surge in mega-constellation projects by commercial enterprises such as SpaceX and OneWeb has led to a dramatic rise in the number of active satellites. This, in turn, has amplified concerns about space debris and the potential for catastrophic collisions. Regulatory bodies, including the United Nations and national space agencies, have intensified their focus on debris mitigation, compelling satellite operators to adopt deorbiting strategies and secure comprehensive liability insurance. The insurance sector is responding with tailored products that address third-party liability, in-orbit liability, and ground damage risks, thereby supporting the sustainable growth of the satellite industry.

Another significant driver is the evolution of international liability conventions and national regulations that govern space activities. The Outer Space Treaty and the Liability Convention, along with recent amendments and national legislations, place substantial liability on satellite operators for damages caused by their assets, both in-orbit and upon reentry. This legal landscape has created a pressing need for specialized liability insurance products that cover a broad spectrum of risks, from in-orbit collisions to ground damages during deorbiting. Insurance providers are leveraging advanced risk modeling, actuarial science, and collaboration with satellite deorbiting service providers to offer comprehensive coverage, fostering confidence among satellite operators and investors.

Technological advancements in deorbiting solutions are also fueling the expansion of the Satellite Deorbiting Service Liability Insurance market. The development of innovative deorbiting technologies, such as drag sails, propulsion modules, and robotic servicing, has improved the reliability and predictability of satellite end-of-life disposal. These advancements mitigate the risk of uncontrolled reentries and reduce the probability of liability claims, making it more attractive for insurers to underwrite such risks. Furthermore, the increasing participation of private sector players in satellite servicing and debris removal is creating new opportunities for insurance products tailored to the unique operational profiles and liability exposures of these emerging services.

From a regional perspective, North America continues to dominate the Satellite Deorbiting Service Liability Insurance market, accounting for over 38% of the global market in 2024. The region's leadership is attributed to the presence of major satellite operators, a mature insurance industry, and proactive regulatory frameworks. Europe follows closely, driven by robust governmental initiatives and a strong commercial satellite sector. The Asia Pacific region is witnessing rapid growth, fueled by ambitious space programs in countries like China, India, and Japan. Meanwhile, Latin America and the Middle East & Africa are gradually increasing their market share as local space industries mature and regulatory awareness improves.

Coverage Type Analysis

The Coverage Type segment of the Satellite Deorbiting Service Liability Insurance market encompasses Third-Party Liability, In-Orbit Liability, Ground Damage Liability, and Others. Third-Party Liability remains the most sought-after coverage, as it protects satellite operators from claims arising due to damages caused to other satellites or space assets. This coverag

snapshot-tab-pane Insurance Analytics Market Size 2025-2029The 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 InsightsNorth 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 2023By Component - Tools segment accounted for the largest market revenue share in 2023Market Size & ForecastMarket Opportunities: USD 328.64 million Market Future Opportunities 2024: USD 16123.20 millionCAGR from 2024 to 2029 : 16.7%Market SummaryThe 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 SampleThe 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 LandscapeIn 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 dashbo

The Brazilian vehicle fleet is intrinsically related to national economic growth, changes in consumer habits and market trends. It has already reached over one hundred million units and continues to grow. As a result, growth in insurance claims related to accidents, theft and fires is expected to occur. Additionally, while transfer of risk to insurer becomes essential, it causes a shift in demand for a higher quantity of precise and grounded premium estimates by insurance companies. Data analysis for automobile insurance requires actuarial methods that consider risk factors such as driver and vehicle features. Consequently, this study sought to estimate risk premium for different vehicle categories as classified by Superintendence of Private Insurance - SUSEP considering driver’s sex and age, as well as national region. To achieve this goal, regression models for claims frequency and severity of claims, using Poisson, Gaussian Inverse Poisson, and Negative Binomial for claims frequencies, as well as Gamma, Gaussian and Log-Gaussian for severity of claims.

Tweedie’s compound Poisson model is a popular method to model data with probability mass at zero and nonnegative, highly right-skewed distribution. Motivated by wide applications of the Tweedie model in various fields such as actuarial science, we investigate the grouped elastic net method for the Tweedie model in the context of the generalized linear model. To efficiently compute the estimation coefficients, we devise a two-layer algorithm that embeds the blockwise majorization descent method into an iteratively reweighted least square strategy. Integrated with the strong rule, the proposed algorithm is implemented in an easy-to-use R package HDtweedie, and is shown to compute the whole solution path very efficiently. Simulations are conducted to study the variable selection and model fitting performance of various lasso methods for the Tweedie model. The modeling applications in risk segmentation of insurance business are illustrated by analysis of an auto insurance claim dataset. Supplementary materials for this article are available online.

Context In the dataset freMTPL2freq risk features and claim numbers were collected for 677,991 motor third-part liability policies (observed on a year).

Content freMTPL2freq contains 11 columns (+IDpol): • IDpol The policy ID (used to link with the claims dataset). • ClaimNb Number of claims during the exposure period. • Exposure The exposure period. • Area The area code. • VehPower The power of the car (ordered categorical). • VehAge The vehicle age, in years. • DrivAge The driver age, in years (in France, people can drive a car at 18). • BonusMalus Bonus/malus, between 50 and 350: <100 means bonus, >100 means malus in France. • VehBrand The car brand (unknown categories). • VehGas The car gas, Diesel or regular. • Density The density of inhabitants (number of inhabitants per km2) in the city the driver of the car lives in. • Region The policy regions in France (based on a standard French classification)

Inspiration The Swiss Actuarial Society's data science tutorials ( https://www.actuarialdatascience.org/ADS-Tutorials/ ) are build on the original dataset (see above) . This copy enables the use of notebooks (kernels) to further study this interesting

Founded in 2014, Advanced Risk Analytics operates in Fintech offering analytics and consulting services focused on insurance, risk management, and catastrophic risk. The company specialises in catastrophe risk management, Insurance-Linked Securities, portfolio analytics, and risk optimisation, helping insurers and investors leverage advanced data science for informed decision-making. Its core offerings encompass ILS analytics, portfolio management solutions, and tailored consulting insights. With a strong emphasis on data-driven innovation and actuarial expertise, Advanced Risk Analytics aims to empower risk managers globally to achieve a balance of profitability and resilience.

Climate-Risk Underwriting Market Outlook

According to our latest research, the global climate-risk underwriting market size reached USD 2.18 billion in 2024, reflecting robust momentum as insurers and reinsurers increasingly integrate climate risk analytics into their underwriting workflows. The market is projected to grow at a CAGR of 22.7% from 2025 to 2033, reaching a forecasted value of USD 17.15 billion by the end of the period. This impressive growth is driven by heightened awareness of climate-related financial risks, evolving regulatory frameworks, and the urgent need for advanced risk assessment tools across global insurance sectors.

A primary growth factor for the climate-risk underwriting market is the escalating frequency and severity of extreme weather events, such as hurricanes, floods, wildfires, and droughts. These events have caused unprecedented losses for insurers, prompting a paradigm shift in risk management approaches. Insurers are increasingly leveraging advanced analytics, artificial intelligence, and geospatial data to better understand and price climate-related risks. This demand for sophisticated, real-time risk modeling solutions has spurred significant investments in climate-risk underwriting platforms, enabling insurance providers to enhance portfolio resilience and maintain profitability in an era of environmental volatility.

Another significant driver is the tightening of regulatory requirements and disclosure mandates related to climate risk. Regulatory bodies across North America, Europe, and parts of Asia Pacific are introducing guidelines that require insurers to assess, report, and actively manage climate-related risks within their portfolios. Initiatives such as the Task Force on Climate-related Financial Disclosures (TCFD) and the European Union’s Sustainable Finance Disclosure Regulation (SFDR) are compelling insurance organizations to adopt climate-risk underwriting solutions that offer transparency, traceability, and compliance. These regulatory pressures are accelerating digital transformation and fostering innovation in climate-risk analytics, further propelling market growth.

Additionally, the growing recognition of climate risk as a material financial risk has catalyzed collaboration between insurers, reinsurers, technology vendors, and data analytics providers. Stakeholders are forming strategic partnerships to co-develop tailored solutions that address the unique needs of various insurance segments, from property and casualty to agriculture and life insurance. This collaborative ecosystem is fostering the integration of climate science, actuarial modeling, and big data analytics, empowering underwriters to make more informed decisions and develop climate-resilient insurance products. As a result, the climate-risk underwriting market is witnessing an influx of innovative offerings that cater to both traditional and emerging risk categories.

From a regional perspective, North America and Europe currently dominate the climate-risk underwriting market, accounting for over 62% of global revenues in 2024. These regions benefit from mature insurance industries, proactive regulatory environments, and a high degree of climate risk awareness. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid urbanization, increasing exposure to natural catastrophes, and the adoption of advanced risk management technologies. Latin America and the Middle East & Africa are also witnessing gradual uptake, supported by growing investments in insurance infrastructure and climate adaptation initiatives. The regional landscape is expected to evolve dynamically as climate risk becomes a central theme in global insurance strategies.

Component Analysis

The component segment of the climate-risk underwriting market is bifurcated into software and services, each playing a pivotal role in enabling insurance organizations to navigate the complexities of climate risk. The software component, which includes risk analytics platforms, catastrophe modeling tools, and geospatial data integration solutions, accounted for the largest market share in 2024. Insurers are increasingly investing in cloud-based and AI-powered software to automate risk assessment, enhance underwriting accuracy, and improve operational efficiency. These platforms offer real-time insights, customizable risk models, and seamless integration with existing insurance workflows, makin

Climate Stress Testing for Insurers Market Outlook

According to our latest research, the global climate stress testing for insurers market size reached USD 1.42 billion in 2024, demonstrating robust momentum as insurers worldwide increasingly incorporate climate risk into their strategic frameworks. The market is expected to grow at a CAGR of 17.6% from 2025 to 2033, reaching a forecasted value of USD 6.07 billion by 2033. This growth is fueled by the stringent regulatory mandates, rising frequency of climate-related catastrophes, and the urgent need for insurers to align their portfolios with environmental, social, and governance (ESG) standards.

The primary drivers of the climate stress testing for insurers market are the growing regulatory pressures and evolving supervisory expectations. Global regulatory bodies such as the International Association of Insurance Supervisors (IAIS), the European Insurance and Occupational Pensions Authority (EIOPA), and the Bank of England have mandated climate stress testing as a critical risk management practice. Insurers are now required to evaluate the resilience of their portfolios under various climate scenarios, including physical risks from extreme weather events and transition risks arising from policy and market shifts. This regulatory push is compelling insurers to invest heavily in advanced climate risk analytics, scenario modeling software, and consulting services, thereby driving market expansion.

Another significant factor propelling the market is the increasing frequency and severity of climate-related disasters, which have heightened the urgency for robust risk assessment frameworks. Catastrophic events such as hurricanes, wildfires, and floods have resulted in substantial insured losses globally, exposing the vulnerability of traditional risk models. Insurers are recognizing the imperative to integrate climate science with actuarial modeling to better anticipate and mitigate future losses. The adoption of climate stress testing tools enables insurers to quantify potential financial impacts, optimize reinsurance strategies, and enhance underwriting accuracy, ultimately improving their operational resilience.

The growing investor and stakeholder emphasis on ESG compliance and sustainable finance is also shaping the climate stress testing for insurers market. Institutional investors, rating agencies, and policyholders are increasingly scrutinizing insurers’ climate risk disclosures and sustainability practices. This has prompted insurers to adopt comprehensive climate stress testing solutions that support transparent reporting, scenario analysis, and portfolio alignment with net-zero targets. As a result, the market is witnessing a surge in demand for integrated platforms that offer end-to-end climate risk assessment, regulatory compliance, and portfolio management capabilities.

Regionally, Europe has emerged as the frontrunner in the adoption of climate stress testing for insurers, driven by proactive regulatory frameworks and ambitious climate policies. North America is rapidly catching up, with major insurers and reinsurers leveraging advanced analytics to manage climate exposures. The Asia Pacific region is poised for significant growth, fueled by increasing climate vulnerability, regulatory reforms, and the expansion of the insurance sector. Latin America and the Middle East & Africa are gradually entering the market, supported by international collaborations and technology transfer initiatives. Overall, the global market is characterized by dynamic regional trends, with each geography presenting unique opportunities and challenges for insurers.

Component Analysis

The component segment of the climate stress testing for insurers market is categorized into software, services, and platforms, each playing a pivotal role in supporting insurers’ climate risk management initiatives. Software solutions are at the core of this segment, offering advanced analytics, scenario modeling, and data visualization ca

This dataset contains 10,000 synthetic life insurance customer records designed to support data science, machine learning, and actuarial analysis in the insurance domain.

It includes a balanced mix of demographic, financial, health, and policy-related features that are commonly used to study customer behavior, policy retention, and risk segmentation in life insurance products.

The dataset is intentionally label-agnostic, allowing practitioners to:

Engineer custom targets such as policy lapse or retention

Perform unsupervised customer segmentation

Build explainable ML models for underwriting and premium analysis

Practice feature engineering on realistic insurance data

All data is synthetically generated and does not contain any real personal information, making it safe for educational use, research, and public sharing.

This dataset is suitable for:

Beginners learning applied machine learning

Advanced practitioners testing modeling pipelines

Researchers exploring ethical synthetic data usage in regulated industries

Asia Pacific Journal of Risk and Insurance Impact Factor 2025-2026 - ResearchHelpDesk - As the official journal of the Asia-Pacific Risk and Insurance Association, the Asia-Pacific Journal of Risk and Insurance (APJRI) focuses on risk management and insurance issues of importance to the Asia-Pacific region. An interdisciplinary publication, APJRI facilitates the exchange of research in risk and insurance mathematics, economics, finance, and corporate practice. The journal welcomes theoretical and applied research papers on a variety of specific topics. Topics Actuarial pricing and reserving Insurance operations Economics and regulation Corporate/enterprise risk management and finance Catastrophe risk Social insurance and employee benefits Local/regional/international insurance markets Abstracting & Indexing Asia-Pacific Journal of Risk and Insurance is covered by the following services: Baidu Scholar Bibliography of Asian Studies Cabell's Whitelist CNKI Scholar (China National Knowledge Infrastructure) CNPIEC - cnpLINKer Dimensions EBSCO (relevant databases) EBSCO Discovery Service EconBiz EconLit ERIH PLUS (European Reference Index for the Humanities and Social Sciences) Genamics JournalSeek Google Scholar Index Islamicus J-Gate JournalGuide JournalTOCs KESLI-NDSL (Korean National Discovery for Science Leaders) Microsoft Academic MyScienceWork Naver Academic Naviga (Softweco) Norwegian Register for Scientific Journals, Series and Publishers Primo Central (ExLibris) ProQuest (relevant databases) Publons QOAM (Quality Open Access Market) ReadCube Research Papers in Economics (RePEc) Semantic Scholar Sherpa/RoMEO Summon (ProQuest) TDNet Ulrich's Periodicals Directory/ulrichsweb WanFang Data WorldCat (OCLC)

Asia Pacific Journal of Risk and Insurance Abbreviation ISO4 - ResearchHelpDesk - As the official journal of the Asia-Pacific Risk and Insurance Association, the Asia-Pacific Journal of Risk and Insurance (APJRI) focuses on risk management and insurance issues of importance to the Asia-Pacific region. An interdisciplinary publication, APJRI facilitates the exchange of research in risk and insurance mathematics, economics, finance, and corporate practice. The journal welcomes theoretical and applied research papers on a variety of specific topics. Topics Actuarial pricing and reserving Insurance operations Economics and regulation Corporate/enterprise risk management and finance Catastrophe risk Social insurance and employee benefits Local/regional/international insurance markets Abstracting & Indexing Asia-Pacific Journal of Risk and Insurance is covered by the following services: Baidu Scholar Bibliography of Asian Studies Cabell's Whitelist CNKI Scholar (China National Knowledge Infrastructure) CNPIEC - cnpLINKer Dimensions EBSCO (relevant databases) EBSCO Discovery Service EconBiz EconLit ERIH PLUS (European Reference Index for the Humanities and Social Sciences) Genamics JournalSeek Google Scholar Index Islamicus J-Gate JournalGuide JournalTOCs KESLI-NDSL (Korean National Discovery for Science Leaders) Microsoft Academic MyScienceWork Naver Academic Naviga (Softweco) Norwegian Register for Scientific Journals, Series and Publishers Primo Central (ExLibris) ProQuest (relevant databases) Publons QOAM (Quality Open Access Market) ReadCube Research Papers in Economics (RePEc) Semantic Scholar Sherpa/RoMEO Summon (ProQuest) TDNet Ulrich's Periodicals Directory/ulrichsweb WanFang Data WorldCat (OCLC)

Context

In the dataset freMTPL2freq risk features and claim numbers were collected for 677,991 motor third-part liability policies (observed on a year).

Content

freMTPL2freq contains 11 columns (+IDpol): • IDpol The policy ID (used to link with the claims dataset). • ClaimNb Number of claims during the exposure period. • Exposure The exposure period. • Area The area code. • VehPower The power of the car (ordered categorical). • VehAge The vehicle age, in years. • DrivAge The driver age, in years (in France, people can drive a car at 18). • BonusMalus Bonus/malus, between 50 and 350: <100 means bonus, >100 means malus in France. • VehBrand The car brand (unknown categories). • VehGas The car gas, Diesel or regular. • Density The density of inhabitants (number of inhabitants per km2) in the city the driver of the car lives in. • Region The policy regions in France (based on a standard French classification)

Acknowledgements

Source: R-Package CASDatasets, Version 1.0-6 (2016) by Christophe Dutang [aut, cre], Arthur Charpentier [ctb]

Inspiration

The Swiss Actuarial Society's data science tutorials ( https://www.actuarialdatascience.org/ADS-Tutorials/ ) are build on the original dataset (see above) . This copy enables the use of notebooks (kernels) to further study this interesting topic.