In the competitive and highly regulated insurance industry, it’s easy to see why firms are hungry to unlock the value of their data. From tackling claims inflation and fraud to developing innovative new products and pricing strategies, they know that being able to make sense of this data quickly is key to remaining relevant and profitable.

Real-time advanced analytics – powered by artificial intelligence (AI) and machine learning (ML) – support better and faster decision-making, which enables firms to respond proactively to the challenges and opportunities they face.

In an industry where loyalty is notoriously challenging to achieve, the insights they gain can help to drive up customer lifetime value (CLV) – and ultimately underwrite more profit rather than mainly relying on acquisition. The FCA rules on fair pricing make a stronger case for investment in customer retention strategies.

Getting to know your customers as individuals, not just segments, allows you to deliver seamless experiences and up-sell and cross-sell relevant products and offers at the right time in the life cycle. Furthermore, if you can identify those most at risk of an accident or a loss, you can target your communications towards prevention to reduce claims costs.

Is rapid cloud deployment possible?

Being on the cloud is a prerequisite for any firm that wants to transform its marketing, customer service, risk, pricing, claims, complaints, and fraud processes.

The old way of adding more on-premises servers to boost computing power and manage data isn’t sustainable for insurance companies today. Especially as they amass more data with every customer interaction and claim. Therefore, moving data management to the cloud, and leveraging AI and ML, is the most powerful and cost-effective way to modernise your processes, improve decision-making and scale up the business.

But while insurers often recognise these benefits and are keen to press ahead with their cloud migration projects, the cost, time and risk involved mean it’s still a daunting prospect.

Depending on the vendor they choose, they may need to re-engineer code, ensuring it meets regulatory requirements, which is extremely costly, especially considering the current talent shortages in tech. Operational disruption must be factored in, too, as the impact it could have on underwriting policies, claims management and customer service.

Yet for companies that already use SAS, cloud deployment is a comparatively straightforward process that allows them to modernise by integrating, building, deploying rapidly, and managing models at scale.

We collaborate closely with in-house teams, so they don’t have to recruit more data scientists (another significant challenge in today’s jobs market). SAS also works in open-source environments, so it’s accessible to users who code in other languages, such as Python and Java. Since SAS is portable across cloud providers (Azure, AWS, Google Cloud), companies also avoid the operational risk of being locked into one provider.

Cloud deployment has created a new urgency for insurers who recognise how sectors such as retail and entertainment have driven up customer expectations. There’s also an arms race to leverage advanced analytics to grow profitably among insurers. They can’t afford to wait two years or more. And engage with consultants and vendors to start their cloud migration projects – they need to deliver the personalised and frictionless journeys that consumers expect right now.

Migration in action

We’ve seen how effective this route can be as more of our insurance customers look to move to the SAS^® Viya^® platform on Azure. One of them is the Markerstudy Group, the UK’s most prominent managing general agent, migrating its SAS capabilities to the platform. No longer limited by traditional rules-based operations, they’re on course to becoming truly data-driven and model-driven. Their approach is expected to bring several benefits, including:

• Improved underwriting profitability, gross written premium, and net promoter score by harnessing the most advanced data science capabilities.
• A single platform can be extended into customer insight, optimised pricing, claims automation, fraud detection & investigation and risk management, with real-time decisioning available for every business area on a fast and scalable cloud.
• Supports growth through acquisition and organic development because the operation can be scaled on demand at short notice and reduce the time to value.
• The phased migration approach minimises business interruption and complexity and avoids significant re-engineering costs.
• Increased adoption and minimal training through software known to large numbers of Markerstudy staff, accelerating time to value.
• Access to data science expertise at SAS to drive workforce in learning and enablement of advanced data science techniques.

As we’ve seen, cloud deployment with SAS reduces the barriers and risks associated with cloud migration projects, so firms benefit from faster decision-making based on fair, accountable, transparent, and explainable models.

Learn more about the SAS® Viya^® platform on Azure and its capabilities.