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Actuarial Transformation

Through Competitive Tariffs. Optimized Portfolios. Pricing in Real-Time.

Get the ebook: Pragmatic AI for Insurance

Pragmatic AI for Insurance

Insurance companies have a long history of using analytics, but the advent of artificial intelligence (AI) has taken this to a new level. This ebook discusses how the insurance industry is benchmarked against other sectors in general, and discusses specific use cases and opportunities.

How do you maintain a competitive advantage in the insurance industry?

As technology and customer behavior change across markets, brand value is turning diffuse and price is becoming increasingly important to customers across the whole insurance market.  

Consumers are open to new value propositions based on new variables (mobility, limited coverage, etc.), making a dynamic pricing structure essential.  

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Competitive Tariffs. Optimized Portfolios. Real-Time Pricing.

To remain competitive in this changing market, insurers need to:

  • Adapt faster to the market.
  • Innovate to remain relevant and competitive.
  • Protect your customer base by assigning the optimum price to your clients; optimize portfolios.


Explore how SAS can help you modernize actuarial processes and evolve the rate-making process, adding a mix of new capabilities to enable:  

Better Pricing

  • 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.

Faster Pricing

  • Using a complete end-to-end solution that goes from data preparation, through modelling to soperationaliing 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. 
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OPERATIONALIZING ANALYTICS

What is ModelOps?

The key to a highly operational analytics life cycle.

As insurers 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
     

Connect with SAS and see what we can do for you.

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