For years, the insurance industry has talked about real-time underwriting as the goal: pull in current data, assess the risk, calculate a price, and deliver a quote almost instantly. But that definition works better for a personal auto policy than it does for a complex multimillion-dollar commercial risk. If insurers want to make real-time underwriting a reality across more of the industry, the definition needs to be broader. The goal doesn't have to be an instant decision, but rather using agentic AI and the most current information available to make better decisions, faster.
That distinction matters. Fully automated, straight-through processing can work when risks are relatively standard. But underwriting doesn't need to be automated from beginning to end to become more real-time. For complex risks, the opportunity is to automate certain areas, bring more current information into the decision, and give underwriters what they need to act faster.
Real-Time Underwriting Looks Different Across the Industry
While real-time underwriting will not look the same across every line of business, the ultimate goal can be the same: accurately assessing risk and acting on that information as quickly as possible. For standardized risks like personal auto, homeowners policies, and small commercial BOP, the result may be agentic AI solutions performing straight-through processing with human oversight. For large commercial risks, where experienced underwriters need to evaluate complex exposures, full automation may not be practical or desirable, but AI tools can expedite a number of manual processes.
Across those different types of risk, three principles can move underwriting closer to real time.
Automate the processes that can be automated. Automation should accelerate the underwriting process without removing the judgment that complex risks require. Agentic AI tools can ingest and structure submission data, identify missing information, check the risk against appetite, and summarize key information. Underwriters then review the recommendations, exceptions, and complex risk factors. By automating the work surrounding the decision, the underwriter can spend more time on the decision itself.
Incorporate more current information into risk decisions. Insurance has traditionally relied heavily on historical information. Loss histories, prior claims, and other historical data remain essential, but the past is not always an adequate predictor of what happens next.
Insurers now have access to an expanding range of external data that can provide a more current view of risk. Telematics, for example, can provide information about actual driving behavior. Climate-related risks can change the relevance of historical property loss data. A property with limited losses over the previous two decades isn't necessarily exposed to the same level of risk today. The challenge is turning that information into something underwriters can actually use by incorporating it into the models and workflows that drive decisions.
Continuously monitor risk after a policy is written. Risk doesn't stand still once a policy is bound. Events can happen during a policy term, including new claims and changes in financial conditions, which can alter the insurers' view of the risk. Rather than waiting until renewal to understand what has changed, insurers can continuously monitor meaningful developments across individual accounts and their broader portfolios.
That doesn't mean every new signal triggers changes in pricing or coverage. Instead, continuous risk assessment gives insurers a more current understanding of the risks already on their books and better information when it is time to make the next underwriting decision, including at renewal.
Putting the Real-Time Mindset Into Practice
None of this requires insurers to rebuild underwriting overnight. Three practical steps can begin moving the organization toward faster, more responsive decision-making.
Incorporate new data into the models that drive decisions. There's an expanding universe of external information. Beyond telematics, connected home devices and geospatial imagery provide more comprehensive information on property risk. Unstructured sources such as news and social media can provide valuable insight into litigation and financial conditions, for larger commercial risks. But more data does not automatically produce better underwriting. Carriers can determine which data meaningfully improves risk assessment and then update rating models, rules engines and underwriting workflows so those signals can influence decisions.
All of the rating engines don't need to be overhauled at once. Insurers can start with a specific line of business or risk factor, identify an external data source that can improve the assessment of that risk and test how the new information changes pricing and underwriting outcomes. Rating logic should also be flexible enough to consume new data through APIs or other integrations rather than requiring extensive system changes each time a new source is added.
Orchestrate data across the enterprise. Insurers often already possess valuable information that underwriters cannot easily access. Systems such as policy administration, claims, and billing may operate separately, leaving relevant information fragmented across the organization. A significant claim could occur for a commercial account, and an underwriter might not discover it until renewal.
This is not necessarily a data problem, but an access and orchestration problem. Connecting those workflows can make underwriting more responsive. Insurers should work toward creating a connected data layer that allows information to flow between systems and establishes consistent definitions and formats so it can be used across the organization. APIs and industry data standards can help insurers connect legacy and modern platforms without waiting for a wholesale core replacement.
- Automate the work surrounding the decision. Even when the underwriting decision itself requires human judgment, much of the work surrounding it may not. Submission triage is a good example. Rather than having an underwriter immediately begin reviewing every incoming submission, agentic AI powered solutions can first assess whether an account meets basic criteria, identify missing information, and summarize what's been provided. If information is missing, the solution can notify the broker immediately instead of discovering the gap later.
In addition to triaging, agentic AI and automation tools can increasingly handle other manual processes including data ingestion and summarization leaving underwriters to focus on the risks and decisions that actually require their expertise.
Real-time underwriting does not have to mean instant underwriting. It means making underwriting more responsive at every stage, using agentic AI solutions to automate where it makes sense, bringing current information into the decision, and maintaining a more current view of risk after the policy is written. For some risks, that may mean straight through processing. For others, the underwriter is the decisionmaker throughout. But the opportunity across all lines is to give underwriters enhanced information sooner so they can make better decisions.
