Insurance Underwriters

Review individual applications for insurance to evaluate degree of risk involved and determine acceptance of applications.

The occupation of Insurance Underwriters has an automation risk of 56.3%, closely tracking the base automation risk of 57.1% for this role. Insurance underwriters perform critical assessments to evaluate the risk associated with insuring individuals or properties. Many of their daily functions, such as examining documents to assess risk from applicant health, financial standing, and property condition, are highly standardized and rule-based, making them susceptible to automation by advanced algorithms and artificial intelligence. Additionally, tasks like declining excessive risks and communicating with field representatives or medical personnel to gather further information fall within the realm of structured, repetitive decision-making, which are increasingly managed efficiently by automated systems. As automation technology continues to evolve, these core duties are progressively being streamlined by digital workflows and data analytics software. Despite this vulnerability, there are key tasks within the insurance underwriting role that remain relatively resistant to automation. For example, the ability to authorize reinsurance for policies with exceptionally high risk relies on professional judgement and the evaluation of complex, non-standardized scenarios. Similarly, decreasing the value of policies for substandard risks, specifying endorsements, or applying nuanced ratings often require a deep understanding of reference materials and the application of context-sensitive criteria—skills that current AI and automated systems struggle to replicate. Reviewing company records to determine insurance amounts for individual or related groups also demands careful reasoning and sometimes access to legacy systems or information silos not easily automated. These tasks bookmark the upper boundary of human-centric functions within the occupation. The primary bottleneck skills slowing automation in this field are rooted in originality, albeit at relatively low levels—2.9% and 2.8%. Originality, involving the ability to craft new approaches to unusual cases or devise exceptions to underwriting rules, is a weakness for automation because current technologies excel at pattern recognition but falter in creative strategy or adapting to ambiguous, never-before-seen circumstances. While most underwriting decisions can be supported by data-driven models, the occasional novel or complex case still requires human creativity and nuanced judgement. As a result, while over half of the underwriting functions are likely to be automated, the persistence of such bottleneck skills provides continued, if limited, protection against full automation of the occupation.