5 Ways Insurers Are Using Agentic AI Today

And while agentic AI is a popular topic in “future of insurance” talks, it’s actually not just the future - it’s the right now. Early adopters are already using agentic AI in their underwriting workflows today. 

Here’s 5 real-life examples of how underwriting organizations are putting AI into action.

1. Staying Up to Date on Potential Insured

When an underwriter is evaluating a submission, one of the first things they often do is run an internet search for recent news about the potential insured. While this can provide important additional information, sifting through the results for anything that might influence an underwriting decision can be tedious, time-consuming work. 

Agentic AI can undertake this search on the underwriter’s behalf, collate any relevant results, and summarize its findings for easy perusal. The underwriter gets what they need in seconds, and doesn’t have to interrupt their work to go digging through search results.

2. Communicating Faster with Partners

Good relationships with partners like agents and brokers are essential for underwriters, but underwriters often find themselves writing the same emails over and over again, whether it be requested more information, informing the partner that a submission has been declined, or even just giving a status update on a particular submission.

Agentic AI can scan a submission as it arrives, determine next steps based on the submission’s data, and then generate and send emails to the broker or agent. For instance, if the agentic AI workflow determines that a submission is clearly not a fit for the insurer’s appetite based on the data provided, it can draft a decline email and send it to the broker on the underwriter’s behalf. If the submission is missing crucial information that the underwriter needs to evaluate the risk, the AI can draft and send an email asking for the specific missing data.

This saves valuable time for underwriters, and also benefits their partners. If the submission isn’t a fit, the broker can get the “no” almost immediately and shift their attention to the next opportunity, rather than waiting days for a response while the underwriter works through their submission queue. Likewise, if the broker learns immediately that they need to submit additional information, they can do that and keep the submission moving forward.

3. Proactively Identifying Potential Issues

Underwriters are frequently buried in an overwhelming number of submissions, and it can be easy for important-not-urgent things to slip through the cracks (and set up bigger problems later).

Agentic AI can monitor things that underwriters often don’t have time to stay on top of, or might miss in a sea of data. This includes things like analyzing all upcoming renewals and flagging risky ones for underwriters review, or identifying when the organization is approaching a geographic accumulation risk. The heads-up allows underwriting organizations to course-correct in advance, and avoid issues that might otherwise become big headaches down the line.

4. Support Workflow Efficiency

One of the things that makes agentic AI so powerful is that it can be trained to reason through problems like a human would, and that it can continually learn from human feedback to improve its decisionmaking. This makes it ideal for workflows that help underwriters through their tasks, as it can learn what underwriters are looking for and surface it for their attention - and the more it’s used, the better it gets at delivering useful information.

One example is suggesting exclusions based on the type of risk, something the AI can learn and continually improve on. Another is checking a contract against a list of requirements, detecting if all requirements have been met without needing the underwriter to manually review a long, complex document.

5. Creating and Analyzing Reports

Reporting has long been a challenge for underwriting organizations, who often rely on cobbled-together BI tool dashboards and manual processes to try to get accurate portfolio information - which is often still at least a month out of date by the time it reaches underwriting managers’ hands. Often, this makes it difficult-to-impossible for underwriting teams to know the actual status of their portfolio at any given time. 

With AI (and its ability to process massive amounts of data in very little time), it’s finally possible for underwriters to get real-time data on the status of their portfolio. Agentic AI takes this a step further, allowing insurers to generate full reports that include data analysis. One example is generating summaries of each week’s bound business and its impact to portfolio goals; another example is to generate reports for capacity providers to demonstrate the organization’s performance and underwriting discipline. 

With agentic AI, insurers can dramatically improve productivity and accelerate their underwriting. We’ve shared a few examples of how our customers are using agentic AI today, but today is just the beginning. We can’t wait to see what you build next.

Learn more about how Federatos’ Orchestrate tool helps insurance IT teams easily create and ship agentic AI workflows, or get a live demo of the Federato platform today.