AI's expanding impact on insurance decision-making

The potential for artificial intelligence in the insurance industry is widely acknowledged, even if adoption has been slower and more uneven than early ambitious forecasts had hoped. Still, across core insurance functions including fraud detection to underwriting to claims processing and customer service, AI is increasingly proving its value as a decision-support tool.

Used correctly, AI can help insurers move faster, identify risk more accurately, and reduce costs without replacing human expertise.

Fraud detection as an early-warning system

With fraud detection, the idea is to find tiny signals buried in a mountain of noise. This is where AI shines.

Consider an auto insurer that begins to notice a rise in rear-end collision claims filed within a short window across the same metropolitan area. On their own, the claims look routine: consistent police reports, similar damage photos, no immediate red flags. But when an AI-driven fraud detection system reviews the data, patterns emerge.

The system flags overlapping repair shops, shared medical providers, and policyholders who recently increased coverage limits. By cross-referencing historical claims data, timing and geographic proximity, the AI identifies a network of connections that would be difficult for investigators to uncover quickly. The insurer's special investigations unit is alerted, prioritizing the claims for review. Ultimately, the accidents are determined to be staged.

In this scenario, AI doesn't replace investigators. It gives them a headstart before losses escalate.

Underwriting with better signal, less noise

AI also has growing relevance in underwriting, particularly as risk models become more data-intensive.

Take this workflow for example. An AI system could review claims history, credit-based indicators, property records and regional risk data simultaneously. It could flag inconsistencies, highlight emerging risks and identify applications that warrant closer scrutiny.

In the case of homeowners insurance, AI could add further precision by analyzing aerial imagery, construction details, roof condition, prior losses and neighborhood-level hazard data. When combined with local weather and catastrophe trends, these systems can surface exposure to hail, wildfire or windstorm risk earlier in the process.

Underwriters retain full authority over pricing and acceptance, but AI helps focus attention where it matters most.

Claims processing and next best actions

Claims is another area where AI can drive measurable efficiencies. Once a claim is filed, AI tools can review photos, estimates, policy language, and prior claims history to assess complexity and urgency. Straightforward claims may be routed for expedited handling, while more complex losses are flagged for experienced adjusters.

As claims progress, AI can recommend next best actions. It could request additional documentation, schedule inspections, or initiate subrogation, helping adjusters manage caseloads more effectively.

The result is faster resolution, fewer delays, and greater consistency, without removing human judgment from the process.

Customer support and coverage decisions

On the customer-facing side, AI can assist with drafting early versions of communications, generating real-time quotes, and summarize policy provisions during coverage discussions.
AI can also help claims and legal teams review policy language alongside claim facts and supporting evidence to offer well-reasoned coverage positions. Final determinations still rest with humans, but AI can significantly reduce research time.

Moving forward intentionally

For senior executives, the challenge is not whether AI will shape insurance operations, but how to deploy it responsibly. Bias, transparency, and governance must remain front and center. AI works best as a tool that sharpens human decision-making, not one that replaces it.

In that role, its impact on insurance is already taking shape.