SAS Dynamic Actuarial Modeling Features List
Premium modeling process
- Supports the end-to-end pricing process – from data preparation to modeling and deployment, both online and in batch mode.
- Enables both pure premium and gross premium modeling.
- Enables users to create and include underwriting rules, and to verify the impact of the new premium model, including new underwriting rules.
- Different users with different responsibilities/roles and rights can work on the same premium modeling process in a traceable and auditable environment.
- Makes machine learning more accessible to actuaries by offering them their choice of models and guidance along the pricing process.
- Dramatically reduces time to market for new pricing models.
- Lets you load data from various sources into the system.
- Enables the definition and application of data quality checks, and reports of any abnormalities that lie outside predefined ranges. An easy-to-use interface guides users in providing preconfigured rule sets and predefined ranges.
- Allows users to make changes in the data of interest, creating new columns based on formulas.
- Enables data visualization, correlation analysis, box plots and the ability to run prototype models for validating variables for use in the final models.
- Model estimation
- Provides templates for performing frequency/severity/aggregate modeling. Users can also build their own models and/or model templates.
- Enables the use of new modeling techniques – machine learning model, generalized additive model (GAM) or another SAS Visual Data Mining and Machine Learning model – through the user interface without the need to write SAS code into the pipeline.
- Enables the use of R or Python models in the modeling process.
- Supports model recalibration using different input data/assumptions.
- Allows benchmarking of the results of all selected models against the classical generalized linear model (GLM).
- Interactive grouping node (IGN)
- Enables easy and automatic variable grouping, providing users with complete visibility and control of the results. Users may also interactively change results coming from the automated model.
- If the grouping structure is already known, users may import it from a local file, saving time and ensuring consistency with previous runs.
- Optimization node
- Allows users to 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.
- Ratemaking node
- Provides a dedicated component for modeling the frequency and severity of claims using classical GLMs and more advanced models, with a final comparison of their performances.
- Enables the definition of the use of different variables in different models, and trains them simultaneously.
- Explainable AI
- Explainable AI elements are available as global and local interpretability indicators. These elements make the interpretation of the machine learning models easier and more transparent.
- The explainable AI indicators are:
- Local interpretable model-agnostic explanations (LIME).
- Individual conditional expectation (ICE).
- Partial dependence (PD).
- Shapley additive explanations (SHAP).
- At the variable level, a bias analysis for avoiding unfairness in pricing can be applied to any technique (including GLM).
- Fairness & bias
- Enables analysis of the fairness and biases of the modeling, giving the right insights directly in the results of the models.
- Enables the definition of underwriting and pricing rules via an edition of generic predefined rules or the creation of new ones.
- Allows users to deploy the score code created in the previous step.
- Enables simulation/testing of the full new premium structure on user-provided data. Results may be reviewed and differences with previous premium structures disclosed in a reporting tool, which includes maps and other segmentation variables.
- Enables post-modeling modification of premium modeling parameters.
- Cap and floor risk factor coefficients, with modifications tracked automatically in the report.
- Fix risk factor coefficients to a specific level, with modifications automatically displayed in the relativity plots and tracked in the report.
- Presents the ratebook with parameter estimates for each variable in a tabular format. Users may select any rate range, and manually modify an individual rate or range of rates.
- Allows users to discard any changes made and restart from the original output table, or save the modified table as a scoring code.
- Enables one-click porting to production – both online and in batch mode – using the business rules defined within the premium modeling process.