While there’s a great deal of speculation surrounding the COVID-19 pandemic’s immediate and longer-term effects on the insurance industry, one key trend is sure to stick: commercial P/C insurers will continue pushing for more automation to drive steady gains in operational efficiency.

Executive Summary

The traditional approach of assessing a small commercial customer's risk at a single point in time—such as during the initial application process or annual policy renewal—using historical data is no longer acceptable, Carpe Data CEO Max Drucker suggests, noting COVID-19's impacts on the small business landscape. He describes the use of new sources of data to monitor accounts monthly—or more frequently—as changes happen and advocates qualifying data points to ensure their legitimacy relative to their source.

Today, carriers can choose from dozens of technology-driven approaches to achieve efficiencies and systematic improvements including business intelligence dashboards, AI tools, bots, custom models and analytics, generic data aggregators, and insurance-specific data providers to name a few. Yet for some insurers, small commercial business underwriting is still largely manual and requires human intervention. Emerging data sources that are new to the insurance industry can help automate critical business activities and streamline workflows for underwriting, but how can carriers ingestthem?

For starters, new data features must be easy to consume by an insurer’s systems to yield meaningful results. Automation, artificial intelligence and machine learning are being used to capture, analyze and apply new data to enhance carriers’ existing selection and underwriting and claims processes, as well as improve performance. From an underwriting perspective, qualified insights from a business’s customer won’t appear on an insurance application, yet that information could flag potential issues.

Member Only Content

To continue reading, purchase this article or become a member.

*Already have an account? Click here to login