The Role of Analytics in Modern Insurance

Insurance has always been an analytics business. At the core, an insurer’s ability to set premiums, maintain reserves and meet capital needs is rooted in actuarial analysis. To accurately price risk for policies, insurers rely on advanced predictive models and data analysis. Widely, insurers need a range of analytical techniques to keep their businesses running smoothly and efficiently — but that’s just the tip of the iceberg.

Lately, the role of analytics in insurance has grown to cover more ground. Tailoring products and services to individual customers? Well, it requires digging into and segmenting customer data. To spot claims fraud, insurers are beginning to implement advanced learning algorithms that catch anomalies. Pay-as-you-go or non-traditional, parametric insurance products rely heavily on data analytics and technology, requiring insurers to keep a constant pulse on their data. It’s safe to say, analytics is becoming even more crucial in the insurance world.

This surge has sparked a variety of perspectives on how to embed analytics into the fabric of insurance operations. Four main models have emerged:

1. 1. Keeping it all under the IT department’s roof.
   2. Setting up a dedicated team outside of IT.
   3. Spreading the responsibility across business units.
   4. Mixing the above in a hybrid or matrix structure.

Choosing the right model depends on several factors — primarily, how a company views analytics. Is it a foundational skill everyone should have, or is it a skillset for only a select few? As analytics become more embedded in most avenues of business, this skillset will likely become necessary across the enterprise. However, the tools and techniques required are highly complex and less accessible. Some insurers may build out specialized teams, inside or outside IT, as a result.

Another key factor is data management. Conducting a meaningful analysis requires combining data from several sources, but as insurers continue to use more applications, data becomes scattered and difficult to manage and maintain. To properly exchange data across different systems and applications, well-assembled data is key. Whether this task falls to IT or is spread across multiple departments depends on how the data is used. In multi-use scenarios, data management tends to be an IT responsibility, while in analysis-only scenarios, each team tends to assemble its own fit-for-purpose data store.

The size of the company also plays a significant role. Smaller insurers might lean toward a more distributed approach to stay nimble, while larger companies with a wider range of resources can afford to centralize their analytics teams. Within these organizations, analysis goals and strategies impact governance as well. When analysis is leveraged to create business reports, a dedicated team tends to handle it. When analysis is leveraged to run the business, individual business units tend to lead their own efforts.

And finally, artificial intelligence. The ways an insurer plans to adopt AI can heavily influence their structure for analysis. The use of general-purpose AI tools could democratize analytics skills across the board, pushing insurers away from a centralized model. In contrast, developing bespoke AI solutions calls for a more specialized skillset, nudging insurers toward centralization. Commercial, special-purpose AI tools land somewhere in the middle.

With all these variables at play, there’s no one-size-fits-all answer. To optimize their analytics capabilities, insurers need to weigh all factors within the context of their unique situation. Though the path may seem clear, it’s not unusual — in fact, it’s beneficial — to revisit and make strategic adjustments. What’s crucial is maintaining analytics capabilities, regardless of where or how they’re managed. Not doing so can cause insurers to fall behind, but doing so correctly can place insurers in an optimal position as they navigate the fast-evolving landscape.