Solution Brief
Optimize insurance modeling and decisioning with advanced analytics
Drive higher profitability by speeding and improving actuarial and decisioning processes.
The issue
The technologies and revenues of the insurance world have changed considerably in recent years. To stay ahead of this evolution, insurers must optimize every area of the business – especially pricing. Pricing is a fundamental process that directly affects an insurer’s profitability, brand reputation, market penetration and growth. But many insurers still rely on fragmented systems for pricing and premium (i.e., insurance rate) modeling – an issue compounded by slow, inefficient processes.
To survive over the next few years, insurers must offer sophisticated premium modeling and get to market quickly while ensuring their process is fully traceable and auditable, and they must do it while reducing costs. Insurers also need to meet strict regulatory requirements in every region they cover – while striving to deploy all their models rapidly.
The challenge
Speed of adaptation
Insurers’ processes are often based on manual efforts and multiple touchpoints. To respond to changes effectively, insurers must adapt quickly to variable demands – which requires a drastic reduction in time spent deploying new premiums.
Continuous monitoring
Insurers need information in real time to stay ahead of an ever-changing competitive environment.
Premium update frequency
Deploying models faster means having the most adequate and competitive rates in the marketplace, and it ensures profitability.
Data silos and fragmentation
Redundancies in data and systems unnecessarily increase modeling and decisioning process complexity and create delays in implementing and deploying models.
Portfolio profitability
Accurate information about renewals and acquisitions helps insurers understand risk and retention outcomes, optimizing portfolio profitability.
Process integration
Insurers must consider regulatory compliance, governance, control and traceability across all processes.
Simple, intuitive modeling capabilities
Our approach
To stay ahead of the competition, insurers must deploy models faster and improve their actuarial and decisioning processes. SAS and our partners provide software and services to enable a guided and governed actuarial process – from data preparation and modeling to automatic deployment and firmwide integrated reporting. SAS’ scalable, comprehensive approach to the actuarial process is based on experience with more than 1,400 insurance companies.
We help you:
- Accelerate the deployment of rates into production with a modular end-to-end framework.
- Modernize the modeling process to improve speed, scalability, efficiency and governance using AI/ML models, a rate-making feature for controlled post-modeling parameter adjustments, and sophisticated modeling with explainable machine learning and fairness & bias features.
- Balance profitability and retention when planning rate changes with our Rate Change and Loss Ratio reporting throughout the premium modeling process that includes simulation, impact analysis, decision pipelines and advanced visualizations for improved business outcomes.
SAS difference
Insurers using SAS can independently manage the entire modeling and decisioning life cycle.
Our solution allows insurers to:
Deploy models and provide faster insights to boost the decisioning process
SAS provides a complete end-to-end solution that goes from data preparation, through modeling to operationalizing analytics. With an intuitive, drag-and-drop interface, users can enable real-time deployment and communication of results via REST APIs.
Innovate the model development process and integrate with open-source platforms
Through an agile methodology that includes machine learning techniques and the integration of open-source models (e.g., Python, R), insurers can keep models up to date by embedding optimization algorithms and monitoring.
View the entire modeling process in a consolidated view with transparency
The entire premium modeling process is self-contained in a single tool, which speeds deployment. Our solution also provides simple, intuitive ways to modify and group continuous variables, as well as a controlled and traceable approach to post-modeling modification of premium modeling parameters. SAS provides sophisticated modeling capabilities that include explainable machine learning models and fairness and bias features