SAS Dynamic Actuarial Modeling
Reduce silos, automate processes and facilitate cross-departmental collaboration among actuaries, finance and IT.
-
Take a comprehensive approach to pricing transformation
Insurance technologies and revenues have changed considerably in recent years. To stay ahead of this evolution, insurers must optimize every area of the business. This is particularly true for the pricing process, which directly impacts your profitability, brand reputation, market penetration and growth. SAS Dynamic Actuarial Modeling simplifies and speeds up premium creation and maintenance processes, ensuring that they are well-governed, traceable and easily summarized in comprehensive reports.
-
Deliver your price to the market as it is intended to be with price execution & governance capabilities
SAS provides sophisticated modeling capabilities, including explainable machine learning models. This makes modeling more accessible for actuaries by giving them a choice of models and providing guidance throughout the pricing process.
-
Consistently apply GLMs/GAMs to risks using embedded AI capabilities
As data becomes more abundant, our solution enables you to capture most heterogeneity represented by the complexity of your entire portfolio and compute premiums that represent individual risks in a tailor-made way, thus producing more competitive rates.
-
Improve data quality by minimizing redundancies & inconsistencies across systems
Data duplication and outdated information complicate actuarial and decision-making processes, adding time to tasks such as model implementation and deployment. SAS Dynamic Actuarial Modeling empowers insurers with comprehensive data management capabilities, including automated data exploration, to enhance accuracy and streamline operations.
Key features
SAS provides software and services to enable a guided and governed actuarial process – from data preparation and modeling to automatic deployment and firmwide integrated reporting.
Interactive grouping node
Helps users modify and group continuous variables in a simple, intuitive way.
Rate-making node – sophisticated modeling capabilities
Includes explainable machine learning models. Makes modeling more accessible for actuaries by giving them a choice of models and guidance throughout the pricing process.
Flexible model options
Includes open-source models, models developed in Python or R, or models prebuilt for the firm.
Post-modeling modification of premium modeling parameters
Provides actuaries with a rate-making feature that enables controlled and traceable post-modeling modification of premium modeling parameters while accounting for risk factors.
Optimization capabilities
Lets you simulate renewal pricing scenarios and see the impact in the current portfolio profitability. Speeds decision making with a user-friendly interface for adding constraints and configuring the objective function, along with a visual reporting interface for exploring scenarios and making the data transparent.
Automatic deployment
Enables one-click porting of models to production, both online and in batch mode.
Guided & governed actuarial process
Spans data preparation, modeling, automatic deployment and firmwide integrated reporting.
Self-contained premium modeling process
Speeds deployment and provides full traceability with a single, self-contained tool that spans the entire premium modeling process – including interactive modeling, post-modeling and premium rate implementation.
Related products & solutions
- SAS® Intelligent DecisioningEasily create, manage and govern robust, analytically driven business rules to power decisioning at scale.
- SAS® Model ManagerSimplify the creation and management of your model collection with a web-based interface that easily automates the model management process.
- SAS® Model StudioMaximize productivity and trust with a unified, scalable and collaborative machine learning platform.
SAS Dynamic Actuarial Modeling frequently asked questions
What is SAS Dynamic Actuarial Modeling?
SAS Dynamic Actuarial Modeling is a comprehensive, end-to-end actuarial and insurance-pricing solution that supports data preparation, premium modeling and pricing deployment in a governed, auditable environment.
What does SAS Dynamic Actuarial Modeling do?
SAS Dynamic Actuarial Modeling enables insurers to:
- Ingest and clean data.
- Build pricing and underwriting models (including GLMs, GAMs, machine learning or Python/R models).
- Run frequency/severity or pure premium estimates.
- Deploy ratebooks or premiums automatically in production (online or batch).
What kinds of modeling techniques does SAS Dynamic Actuarial Modeling support?
SAS Dynamic Actuarial Modeling supports traditional actuarial models like GLMs and GAMs, as well as advanced machine learning or open source models (e.g., built in Python or R). It also offers explainable-AI outputs (e.g., SHAP, LIME, partial dependence) to help understand and govern model decisions.
How does SAS Dynamic Actuarial Modeling help with data management and quality?
SAS Dynamic Actuarial Modeling lets insurers load data from multiple sources, apply data quality checks, transform or enrich data (e.g., create new variables), visualize distributions/correlations and run prototype models – all via a user-friendly interface without needing to code.
Can SAS Dynamic Actuarial Modeling simulate pricing scenarios and test profitability impacts?
Yes – SAS Dynamic Actuarial Modeling includes an optimization node that lets users simulate renewal pricing scenarios, adjust constraints and evaluate the profitability impact of different pricing/risk strategies on their existing portfolio.
Who typically uses SAS Dynamic Actuarial Modeling?
Insurance companies – especially actuarial, underwriting, pricing and analytics teams – use SAS Dynamic Actuarial Modeling to streamline premium setting, reduce manual effort, improve pricing accuracy, and ensure governance and traceability throughout the pricing life cycle.
What are the main benefits of using SAS Dynamic Actuarial Modeling?
- Faster time to market for new pricing models and ratebooks.
- Flexibility to use a variety of modeling approaches (traditional + ML + open source) depending on complexity and business needs.
- Full auditability, role-based governance, and traceable model and pricing workflows.
- Improved data quality, consistency and reduction of silos across underwriting, pricing, IT and finance teams.
