Insurance Pricing Strategy
Achieve fairer insurance pricing strategy through competitive tariffs, optimised portfolios and real-time insurance pricing whilst building stronger customer relationships and regaining consumers’ confidence.
With the introduction of the Financial Conduct Authority (FCA)’s regulation: The abolition of “price walking” - the practice whereby existing customers are charged higher premiums than new customers, even if they have identical risk profiles - is imminent, and insurance companies will have to move fast to meet the requirements.
Using AI & Machine Learning, we can help you on your Actuarial Transformation journey and ensure insurance products are priced fairly for your customers.
Insurance Solutions Consultant,
Fairer Insurance pricing is not just a matter of regulatory compliance - it’s part of a fundamental shift in the way insurers think about customer relationships and lifetime value.”
Discover how you can achieve compliance success and build future-facing customer-centric capabilities
Download the FREE eBook
Document type: eBook | Format: PDF | Pages: 10
In this eBook, you'll learn how:
- Addressing price walking can be a starting point for a broader transformation.
- Getting ahead of regulatory requirements with a proactive approach to customer-centric pricing can create a competitive advantage – shifting customers’ decision-making criteria away from cost and towards the quality of service, relevancy of products and level of cover.
How do you maintain a competitive advantage in the insurance industry?
To remain competitive in this changing market, insurers need to:
- Adapt faster to the new FCA pricing regulation.
- Innovate to remain relevant and competitive.
- Create a more agile, digitally-enabled business with innovative tariff modelling based on Artificial Intelligence.
- Protect customer base by assigning the optimum price to your clients; optimise portfolios.
How you can modernise actuarial processes?
Explore how SAS can help you to add a mix of new capabilities to enable:
- Through an agile methodology that includes machine learning techniques and integration of open source languages (e.g., Python, R).
- By keeping models up to date through governance and monitoring capabilities.
- By embedding optimization algorithms in the pricing process.
- Using a complete end-to-end solution that goes from data preparation, through modelling to operationalising analytics.
- With a drag-and-drop, easy-to-use GUI, available for each building block of the process.
- By enabling real-time deployment and communication of results via REST APIs.
How you can make your data science and IT team to work smarter and faster?
As insurance companies are under increasing pressure to bring new products to market quicker, a more agile development process is required, including more granular risk segmentation while applying more models to more segments of customers to win more market share at the right price.
ModelOps helps data science and IT teams working closer together and moving new models into production faster. ModelOps focuses on getting models from the lab, to validation, to testing, to deployment as quickly as possible, while ensuring quality results.
It enables teams to:
- Develop and deploy models smoothly, efficiently and continuously
- Manage and scale models to meet demand
- Monitor models continuously to spot and fix early signs of degradation