Why Insurers Need a Solid Data Strategy

“Sir Tim Berners-Lee famously said “Data is a precious thing and will last longer than the systems themselves”

In the insurance industry, data has been a critical driver of risk pricing and evaluation, underwriting, and claim settlement. The importance of data has become a differentiator for many insurance companies, brokers and agents in an increasingly dynamic and volatile market environment. Extreme events are more frequent and difficult to predict.

To respond to some of these extreme events, insurers can now transform their underwriting and pricing operations by investing in and managing internal and external data, developing, and constantly refining new models, and organizing and developing the necessary talent. Just one scenario that could provide a competitive advantage.

For example, in the P&C sector, best-in-class performers are edging out competitors by developing advanced data and analytics underwriting capabilities that can deliver significant value. According to McKinsey, “data-rich digitized underwriting tools can help even the largest insurers see loss ratios improve by three to five points, new business premiums increase by 10 to 15%, and retention in profitable segments increase by 5 to 10%.”

But challenges remain across the insurance sector and below are seven key observations:

1. Many are still “data” siloed by line of business and plagued by having no single view of a claim or portfolio. Disparate data is manually stitched together.
2. There is limited access to reliable data sources, including internal but crucially external data such as vehicle (e.g. Thatcham/DVLA), weather, and geospatial data, which often isn’t curated/automated and blended efficiently.
3. Companies have over-invested in data technology (datalakes, lakehouses, data warehouses etc.). As a consequence, the tech end of the spectrum has data architecture limitations that make it difficult to experiment quickly, deploy models, test, and accelerate the development of new data products. The impact is not realizing the ROI, impossible future cost avoidance, and inability to scale cost-effectively.
4. CIOs have had to deal with intense M&A activity over the years, which has added to the challenges of assimilating and harmonizing multiple organizations’ data sets.
5. Regulation that consumes resources tends to be highly reactive, especially if the data is not accessible or automated enough to meet regulatory requirements.
6. Executives who are not immersed in data. I.e. they say: “we want to be data-driven” or “AI-enabled” but fundamentally do not understand what that means or what it takes to get there, and therefore really don’t provide enough investment and support.
7. Cultural baggage and inertia create a specific organization that’s hard to change. This leads to unsuccessful data initiatives being delivered with minimal change, which turns into a costly exercise where data initiatives are abandoned.

These are not insignificant challenges that insurers face, which is why having a Data Strategy is critical to overcoming these obstacles and preparing for current and future opportunities.

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