Historical grade counts, GPA, A rates and semester results for R M I 920, available on MadEnroll.

👋 Connect with me on LinkedIn!

Manuel Caccone - Actuarial Data Scientist & Open Source Educator Let's discuss actuarial science, AI, and open source projects!

  📊 ActuarialGPT Conversations Dataset










  Precision Mathematical Conversations for Insurance Intelligence















  🎯 Quick Facts

Feature Description

Domain Actuarial Science, Insurance Analytics, Risk Management

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

Historical grade counts, GPA, A rates and semester results for R M I 930, available on MadEnroll.

Motor Insurance Risk Analytics Dataset

This dataset contains French Motor Third-Party Liability (MTPL) insurance policy and claims information, widely used in actuarial science, insurance risk modeling, and predictive analytics.

The dataset combines policy-level characteristics with claims information and can be used for:

• Insurance Risk Analytics
• Claims Frequency Modeling
• Claims Severity Modeling
• Expected Loss Estimation
• Portfolio Risk Assessment
• Pricing and Underwriting Research
• Machine Learning Applications
• Predictive Modeling and Forecasting

Files

freMTPL2freq.csv

Policy-level information including:

• Policy ID
• Exposure
• Driver Age
• Vehicle Age
• Vehicle Power
• Bonus-Malus
• Region
• Population Density
• Number of Claims

freMTPL2sev.csv

Claim-level information including:

• Policy ID
• Claim Amount

Potential Use Cases

• Risk Intelligence Dashboards
• Insurance Portfolio Analytics
• Actuarial Modeling
• Pricing Optimization
• Claims Analytics
• Risk Segmentation
• Data Science and Machine Learning Projects

Data Source

French Motor Third-Party Liability (MTPL) Dataset from the CASdatasets project.

Reference: Dutang, C. & Charpentier, A. CASdatasets – Actuarial and Insurance Datasets.

License: GPL-2 / GPL-3

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

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

Chinese-friendly edition / 中文友好版本

Added GLM_example_with_GLMs_Predictions_EN_CN.csv: 678,013 rows, 20 bilingual fields. Removes 43 design-matrix fields from Intercept through log_Exposure inclusive, preserving all retained values. Both original CSVs remain available.

新增中英双语字段版本，共 678,013 行、20 列。删除从 Intercept 到 log_Exposure 的 43 个设计矩阵字段，保留其余全部原值。两个原始 CSV 仍然保留。

See French_Motor_Insurance_AGENT_EN_CN.md for the bilingual field dictionary, formulas, verified counts and databook agent context. Its row count applies specifically to the supplied prediction file; the original context above describes the upstream dataset.

字段字典、计算公式、核验记录及数据手册智能体背景请见上述 Markdown 文件；678,013 行为本次预测文件的实测行数，上方原说明介绍的是上游数据。

Climate Stress Testing for Insurers Market Size and Growth Outlook

Growth Market Reports finds that 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.

Climate Stress Testing for Insurers Market by Component

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 an

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.