Herbscapes.com

Overview

As artificial intelligence becomes central to insurance claims handling, system reliability has emerged as a critical regulatory concern.

AI-driven claims systems enable faster processing, scalability, and automation. However, they also introduce a dependency on technology infrastructure that must remain available under all conditions — particularly during high-demand events.

When these systems fail, the impact is immediate: claims cannot be processed, communication is disrupted, and insureds are left without access to essential services.

System reliability is not simply an operational issue — it is a matter of business continuity, consumer protection, and regulatory compliance.

The Emerging Risk

AI claims platforms often rely on:

• cloud-based infrastructure

• third-party vendors and integrations

• real-time data processing systems

• automated workflows and communication tools

While these components increase efficiency, they also create potential points of failure.

During periods of peak demand — such as catastrophic weather events — systems may experience:

• performance degradation

• processing delays

• partial or complete outages

If contingency measures are not in place, claims operations may be significantly disrupted.

Why Regulators Will Care

Departments of Insurance (DOIs) and regulatory bodies expect carriers to:

• maintain continuity of claims operations

• provide timely service to insureds

• handle claims effectively during catastrophic events

• protect consumers from service disruption

If AI systems fail and no alternative processes are available, regulators may question:

• whether the carrier maintained adequate business continuity planning

• whether claims were handled in a timely manner

• whether insureds were properly supported during the disruption

This may raise concerns related to:

• Unfair Claims Settlement Practices

• failure to maintain operational readiness

• inadequate disaster response

The Dependency Risk

Traditional claims operations rely on human adjusters and decentralized processes, which can continue functioning even under challenging conditions.

AI-driven systems, however, introduce centralized dependencies:

• a single platform may handle large volumes of claims

• outages can affect multiple functions simultaneously

• system failures can halt operations entirely

This creates a dependency risk, where the availability of the system directly determines the ability to handle claims.

Consequences of System Failure

When AI claims systems become unavailable:

• insureds may be unable to submit or track claims

• communication between carriers and insureds may be disrupted

• claim decisions may be delayed

• backlogs may develop after system restoration

Even temporary outages can have lasting effects on claim timelines and customer experience.

Link to Failure Scenario

This risk is illustrated in the Failure Library scenario:

“Failure to Maintain Claim Operations During AI System Outage”

In that scenario:

• a surge in claims overwhelms the system

• the platform becomes unavailable

• no fallback process is activated

• claims remain unprocessed during the outage

This demonstrates how system failure directly impacts claims operations.

Regulatory Risk Indicators

Carriers implementing AI in claims handling should monitor for:

• System outages or performance issues during peak demand

• Lack of backup or failover capabilities

• Inability to process claims during disruptions

• Absence of contingency or manual processing plans

• Delays following system restoration

These indicators may signal weaknesses in system reliability and resilience.

Gold Standard Approach

To mitigate system reliability risk, carriers should implement robust continuity and resilience strategies.

1. Build Redundant Systems

Ensure backup infrastructure and failover capabilities are in place.

2. Maintain Alternative Processing Methods

Enable manual or hybrid claim handling during system outages.

3. Develop Business Continuity Plans

Prepare for high-demand scenarios and system disruptions.

4. Communicate with Insureds

Provide clear and timely updates during service interruptions.

ClaimSurance Insight

Reliability is tested when systems are under pressure — not when they are operating normally.

AI systems must be designed to perform not only efficiently, but consistently, even during periods of stress and disruption.

Bottom Line

As AI continues to shape the future of claims handling, regulators will expect carriers to demonstrate that their systems are reliable and resilient.

The key question will be:

Can claims still be handled when the system is under strain — or unavailable?

If the answer is no, the risk extends beyond technology to the core obligations of the claims process.