Insurance is built on decisions (thousands of them). Every claim, every policy, every flagged anomaly starts with a trigger and leads to a series of steps, handoffs, and evaluations. In most organizations, those steps are still stitched together manually or with rules-based systems that struggle when exceptions arise.
For example, what happens when a claimant submits photos that don’t match the vehicle details on file? A human has to notice the mismatch, flag it for fraud, request further documentation, and route it to the right team. Everything slows down, and workflows break.
Agentic automation handles these moments differently. Agents recognize when something doesn't fit, decide what to do next, and take action on their own. They move cases forward without waiting, using real-time context and policy data to guide each step. Let's see what this looks like in practice.
How Agentic AI Supports Insurance Automation
Agentic AI in insurance enables orchestration that adapts as each situation unfolds. Instead of relying on fixed workflows or expecting teams to monitor every step, orchestrated agents respond to real-time data, take initiative, and push work forward when it matters most. This approach forms the foundation for practical, end-to-end insurance automation.
Claims: Smoother Progress, Even When Things Go Wrong
Claim processes rarely follow a straight path. A customer might upload photos of an accident, but forget to include a police report. That missing document creates a gap, and in most systems, that means the claim stalls until someone notices and intervenes. With agentic automation, an agent detects the gap right away. It pauses downstream activity, contacts the claimant, and resumes the process automatically once the required file arrives.
The key benefit is continuity (which also impacts speed). When claims involve multiple teams or external systems (likedamage assessment, benefits review, or third-party liability) agents ensure that work moves forward in the rightsequence, without duplication or confusion. In other words, claims keep moving, even when they start to go off-script.
Underwriting: Less Churn, Better Focus
Underwriting often requires gathering information from many sources before making a decision. In a traditional setup, underwriters wait for each piece to arrive, checking systems manually or requesting updates from brokers. Underwriting with agentic AI reduces that friction. As soon as an application is submitted, agents can begin collecting third-party data, comparing it against underwriting rules, and identifying missing or conflicting details.
Rather than sitting idle, the process builds momentum from the start. When something needs review, the underwriter sees it in context, with the relevant data already in place. In more complex cases, like large commercial policies, agents also help behind the scenes. They handle coordination between inspections, pricing tools, and risk systems, so the underwriter isn’t stuck chasing pieces that should already be aligned.
Fraud: Targeted Action Without Slowing the Flow
Fraud detection often forces a tradeoff between thoroughness and efficiency. Static rules and scoring models can only catch what they’re programmed to see, and manual review slows everything down. For example, if two claims arrive from different clinics but report the same injury just days apart, a rules-based system may treat them as unrelated.
A fraud analyst would need to spot the pattern manually, compare claim histories, and piece together the overlap. In the meantime, both claims might move forward unchecked or stall entirely once someone steps in, disrupting the flow for everyone involved.
Agentic AI in insurance gives fraud teams another option: act early, act precisely, and keep the clean claims moving. When a suspicious claim comes in, say, a repeated submission from a flagged provider, an agent doesn’t need to wait for human instruction.
It isolates the case, gathers supporting information, and alerts the fraud unit, all without affecting other files in the queue. If a claim passes basic checks, it continues untouched. That separation preserves both trust and throughput.
As fraud patterns evolve, agents update their decision-making logic based on new data and feedback. They adapt without requiring the entire system to be redesigned, which keeps the process resilient over time.
How We Can Help
At BP3, we work with insurers to design and implement agentic automation that fits the way your business really runs. We don’t drop in generic AI models or hand you abstract tools. Instead, we help you identify the workflows, claims, underwriting, and fraud where orchestrated agents can make decisions, reduce friction, and keep operations moving without adding risk.
We bring deep expertise in insurance automation and a practical approach to agentic AI. From pilot to scale, we focus on what delivers measurable value and long-term resilience.
If you’re ready to make your workflows more intelligent and responsive, let’s talk.