Today, small commercial insurers can leverage AI/ML data and analytics as part of a holistic solution to transition from manual underwriting workflows that lean on lengthy applications and web research to one where quotes are issued and most policies are bound automatically and (nearly) instantly.
The journey has three broad phases.
Step 1: Prefill: Leveraging an array of data sources, including unstructured data sourced from computer vision algorithms, insurers can prefill application data on small commercial risks using just a business name and address. Human underwriters can then review this information against underwriting guidelines without having to chase down data through web searches or phone calls.
Step 2: Selective automation: Based on risk appetite, certain industry classes can be identified for automated underwriting. In this environment, application data is prefilled and then automatically analyzed against insurer underwriting guidelines to determine acceptance or whether additional information is required.
Step 3: Full-blown automation: As insurers learn from step two, it’s a short leap to step three, which is to fold additional businesses into the automated workflow. Even in a fully automated environment, there are some risk exposures that may trigger manual reviews of submissions. But by leveraging the efficiency gains delivered by application prefill and the automated underwriting of select industry classes, insurers can set themselves up to drive automation across a much wider array of risks than they ever thought possible.
Author(s): Tracey Waller
Publication Date: 29 Jun 2022
Publication Site: Digital Insurance