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“New Normal” Requires an Insurance Analytics Evolution

According to cloud communications company Twilio, the pandemic shaved six years off digital transformation roadmaps on a global scale. After decades in the insurance industry, I believe the “new normal” has presented insurers with an opportunity to do something we arguably have done better than any industry from a historic basis – learn from internal and third-party data.

To protect customer relationships and growth, insurers need to look to the cloud, data, and machine learning (ML) to accelerate product innovation and to anticipate risks to insurers and the insured. This combination is what I believe will lay the foundation for an analytics revolution that when coupled with the wealth of new P&C data coming out of the pandemic will position insurers to better adapt to unexpected situations like pandemics or the rising risk of cybercrime.

Embrace the “Learning” in Machine Learning

Learning from data is what led insurers to become trusted advisors to the insured in the first place. This has traditionally meant understanding risks, customer considerations and behavior, and trends for potential new products and services. While the expertise of senior insurance professionals with 30+ years of experience is valuable, as companies age, this expert perspective is lost. With insurtechs with less industry experience and non-insurance companies entering the market selling intelligent insurance services, traditional insurers must embrace ML strategically to remain relevant.

Once insurers do, I believe the combination of the industry expertise coupled with technology will boost consumer confidence in working with their traditional yet modern insurer.

However, as Accenture revealed in a 2018 report about ML in the insurance industry, most insurance companies are only processing 10-15% of the data they have in their traditional databases. This is not the level of commitment that the insured is seeing from other industries.  Beyond the competitive considerations, consumers are used to ML impacting their cars, smart homes, and smart devices, and they expect to interact with companies that are invested in these areas.

For many insurers, they view terms like cloud, data, and ML as far out as astrophysics, but it does not have to be.

Jumpstarting the Analytics Revolution

To be positioned for the next level of intelligence that cloud, data, and ML provide, insurers should follow these three practical steps to get started.

  1. Ensure your internal data is accurate and reliable. Check your systems and processes for capturing and storing data to identify and resolve any major issues.
  2. Leverage modern data warehouse and business intelligence (BI) solutions that can be incorporated to further cleanse, standardize, and load internal data from multiple core systems into a single interface for easy access.
  3. Then use ML to cleanse data and leverage third-party as it can add a deeper layer of understanding on how to improve pricing, profitability, and risk management in areas where traditional analytics tools may miss. By combining internal data with new third-party data sources, insurers can increase confidence in output or decisions based on the information.  This level of intelligence can also identify ways to significantly improve business performance and processes to provide better customer service for policyholders.

This last year showed how valuable in-person interactions are, but also how quickly they can be replaced with intelligent, cloud-based technologies. Like many industries, insurance was caught off guard, but excuses when the next “hiccup” occurs will not be accepted so easily by customers after they have seen other insurers and other industries better protect themselves.

Instead of viewing cloud, data, and ML as a threat to tradition, insurers should embrace a new ally in their goal to grow rapidly and profitably. In hindsight, with the wealth of data that we have our industry should have been the leader in adopting ML technology instead of the laggard. There is still time to catch up and help drive broader cross-industry discussions around smart data analytics and ML.


Rob Clark is the founder and CEO of Cloverleaf Analytics. He can be reached for more information or comment at


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