Data Science Workbenches for Actuarial Department
Integrated development environments that combine programming tools, statistical libraries, and visualization capabilities for actuarial data analysis and model development.
Other Actuarial Department
A comprehensive platform for actuaries to build, validate, and deploy predictive models. Includes support for GLMs, machine learning, and traditional actuarial methods with automated model documentation, validation tools, and regulatory reporting capabilities. Enables end-to-end model lifecycle management specific to insurance use cases.
An integrated development environment for R and Python with specific packages for actuarial analysis. Features include collaborative coding, secure environment for sensitive insurance data, reproducible research capabilities, integration with version control systems, and the ability to deploy models as web applications using Shiny.
A cloud-based platform enabling actuaries and data scientists to collaborate on large-scale data analysis. Supports both R and Python with scalable computing resources for complex actuarial modeling. Features include notebook-based development, version control, MLOps capabilities, and governance tools for ensuring regulatory compliance in insurance use cases.
A modeling platform specifically designed for actuaries in insurance. Includes a user-friendly interface for building complex actuarial models without programming knowledge. Features include scenario testing, IFRS 17 compliance tools, Solvency II reporting, and comprehensive audit trails for model governance.
A cloud-based platform that combines actuarial expertise with data science capabilities to analyze insurance portfolios. Features AI-driven insights for life insurance, predictive analytics for policyholder behavior, lapse prediction, and cross-sell opportunities. Includes data enrichment capabilities and advanced visualization tools specific to actuarial analysis.
A secure, governable platform for developing, deploying, and managing data science workflows in insurance environments. Provides a comprehensive Python and R environment with pre-built libraries for actuarial analysis. Features include package management, governance tools, environment replication, and secure deployment of models for production use cases in insurance.
An integrated development environment (IDE) for R, enabling users to create and manage R projects, run R scripts, visualize data, and build Shiny applications.
Automates machine learning workflows from data prep to model production. Features compliance, explainability, and robust collaboration tools ideal for large actuarial teams and risk management in insurance.
Graphical workflow interface for data integration, analytics, and machine learning. Extensible with actuarial and insurance-specific plug-ins; ideal for pricing, reserving, and risk model prototyping.
A cloud-based service for building, training, and deploying machine learning models, offering automated machine learning, drag-and-drop functionality, and integration with Jupyter notebooks.
