Data collection in underwriting is transforming thanks to advances in artificial intelligence (AI) and the myriad semi-public and public data sources available to carriers. In years past, underwriters had to sift through data manually, then conduct analysis themselves. But a combination of agent, policy applicant, and third-party data paired with the right analytics platform can improve the underwriting process for all parties. While most vendors and data sources currently in this space lean toward small commercial carriers, this trend has positive implications for all lines of business.
Free, online information is prevalent; insurers can Google policyholders, take a look at properties via Google Maps, or read reviews on Yelp. Public and semi-public data sources can also include public social media information from individuals and businesses, news articles, business information from state government websites, and online city records. Each of these categories contains hundreds, if not thousands, of individual data sources.
Automation helps eliminate wasted manual effort on the part of underwriters and helps ensure all information being considered is relevant. Until recently, these public and semi-public data sources needed to be mined by humans. Now, however, screen scraping, integrations, AI, and advanced pattern matching are helping automate this process.
Computers have long lagged behind humans in their pattern-matching abilities; people can easily tell which squares of an image include traffic lights and which don’t, but the task is more difficult for a computer algorithm. Alternative spellings or spelling errors can trip up machines, as can multiple locations of the same business. Modern AI and big data platforms are getting better, though, and for the first time, machines can take over some of these tasks from human employees.
Some AI- and data analytics-enabled underwriting platforms can now handle gathering public and semi-public data, matching patterns via algorithms, pre-filling information, identifying and scoring risk, decision-making, and analyzing and validating portfolios. To learn more about the applications of AI-enabled data and analytics in underwriting as well as get an overview of vendors active in this space, read Novarica’s full report AI-Enabled Data and Analytics in P/C Underwriting: Overview and Prominent Providers.
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