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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.

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.

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

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.

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.

Climate Risk Attribution Insurance Market Outlook 2025-2034

The global climate risk attribution insurance market was valued at $3.1 billion in 2025 and is projected to reach $22.1 billion by 2034, expanding at a compound annual growth rate (CAGR) of 18.9% during the forecast period 2026-2034. This emerging and rapidly evolving segment of the specialty insurance industry sits at the intersection of climate science, legal liability, and risk finance, providing policyholders with structured protection against losses that can be scientifically attributed to anthropogenic climate change. As attribution science matures - allowing researchers to quantify the degree to which a specific extreme weather event was intensified or made more probable by human-driven greenhouse gas emissions - insurers are increasingly developing bespoke products that harness these scientific findings to establish causation-based coverage triggers and liability defense mechanisms. The market has moved from niche academic discussion to active underwriting desks at the world's largest reinsurers, driven by a confluence of regulatory mandates, expanding judicial recognition of climate science, and unprecedented catastrophic loss events that are reshaping actuarial models globally.

The primary growth driver behind this market's remarkable trajectory is the rapid maturation and institutionalization of climate attribution science. Between 2019 and 2025, the volume of peer-reviewed attribution studies grew by more than 340%, with organizations such as the World Weather Attribution consortium producing real-time event attribution analyses within days of major disasters. By 2025, attribution science methodologies had been formally recognized in legal proceedings in at least 14 jurisdictions across North America, Europe, and Australia, creating a direct and legally admissible link between documented greenhouse gas emissions and identifiable economic losses. This judicial acceptance dramatically expanded the addressable market for climate risk attribution insurance, as utility operators, infrastructure owners, agricultural enterprises, and forestry companies facing litigation exposure began seeking purpose-built coverage solutions. Insurers offering liability defense products backed by causation data now command significant pricing power, with average premiums for litigation-support coverage lines growing at rates exceeding 22% annually through 2026. The convergence of science, law, and capital markets is creating one of the most dynamic specialty insurance niches of the decade.

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

Climate-Adaptive Property Insurance with Resilience Pricing Market Outlook 2025-2034

The global climate-adaptive property insurance with resilience pricing market was valued at $3.1 billion in 2025 and is projected to expand to $11.4 billion by 2034, registering a compound annual growth rate (CAGR) of 16.0% over the forecast period 2026-2034. This market sits at the intersection of climate science, actuarial modeling, and property underwriting, offering insurance products whose premiums, coverages, and deductibles are dynamically recalibrated based on a policyholder's physical resilience investments and the evolving hazard profile of their property location. Unlike conventional property insurance, which applies static risk tables, climate-adaptive products incorporate real-time satellite imagery, soil composition data, heat island mapping, and structural hardening audits to price policies with unprecedented granularity and fairness, rewarding property owners who invest in flood barriers, fire-resistant roofing, and hardened foundations with meaningful premium discounts.

The primary catalyst propelling this market is the historic escalation in climate-related insured losses globally. Between 2020 and 2025, average annual insured catastrophe losses surpassed $120 billion, more than double the decade-prior average, with hurricanes, wildfires, and inland flooding accounting for the majority of property claims. Traditional insurers have responded by withdrawing from high-risk geographies - including coastal Florida, California wildfire interface zones, and Gulf Coast corridors - creating acute coverage gaps that have become politically and economically untenable. Climate-adaptive insurers are filling this void by using granular parcel-level data to distinguish between high-risk and resilience-hardened properties within the same ZIP code, enabling profitable underwriting in markets that blanket-model insurers have abandoned. The entry of artificial intelligence platforms capable of ingesting LiDAR topography, NOAA storm-surge projections, and building materials data has compressed the time to generate a resilience-based quote from weeks to minutes, democratizing sophisticated risk differentiation for carriers of all sizes. Regulatory tailwinds are also accelerating adoption, as state insurance commissioners in California, Florida, Texas, and Louisiana have begun mandating resilience credits and requiring carriers to demonstrate climate-informed underwriting practices as a condition of market participation. The confluence of these forces - coverage gaps, regulatory pressure, and technological enablement - positions the climate-adaptive property insurance with resilience pricing market for sustained double-digit growth through 2034.

Dimensions and items obtained from external sources for the construction of a Country similarity index related to mortality in life insurance.

Climate Resilience Specialty MGA Market Outlook 2025-2034

The global Climate Resilience Specialty MGA market was valued at $0.66 billion in 2025 and is projected to surge to $5.8 billion by 2034, advancing at a remarkable compound annual growth rate (CAGR) of 28.0% over the forecast period 2026-2034. This extraordinary growth trajectory is driven by the convergence of accelerating physical climate risks, rapid innovation in parametric and index-based insurance structures, and a sharp increase in demand from underserved commercial, agricultural, and infrastructure segments that traditional insurers have failed to adequately cover.

Managing General Agents (MGAs) operating at the intersection of climate science and insurance underwriting have emerged as the most agile and technically sophisticated channel for distributing specialty climate risk products. Unlike conventional carriers bound by legacy actuarial frameworks, climate-focused MGAs leverage satellite imagery, IoT sensor arrays, machine learning-driven catastrophe models, and real-time geospatial data to price and trigger coverage in ways that are simultaneously more accurate and more scalable. The penetration of parametric structures - where payouts are triggered automatically by measurable physical parameters such as river gauge readings, wind speed thresholds, or soil moisture indices - has dramatically reduced loss adjustment friction and enabled MGAs to underwrite risks that were previously considered uninsurable. In 2025, parametric structures accounted for a rapidly expanding share of climate MGA gross written premiums, with flood parametric and drought parametric products attracting particular interest from agricultural cooperatives and municipal water authorities. The sector also benefits from growing reinsurance capacity dedicated specifically to climate-linked perils: major global reinsurers have established dedicated climate desks and are increasingly willing to back innovative MGA programs, providing the capacity pipeline necessary to absorb the volume ambitions of the leading players. As climate events continue to intensify - insured losses from natural catastrophes exceeded $130 billion globally in 2024 and are trending sharply higher - the urgency for businesses and governments to secure robust, responsive coverage has never been greater, positioning Climate Resilience Specialty MGAs as one of the fastest-growing segments in the entire global insurance value chain.

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

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.