Claims management: Toward a resilient model for catastrophic events and volume surges

Claims management has become the primary operational and strategic pressure point for property and casualty insurers. The intensification of climate-related risks, combined with rapidly evolving customer expectations, is putting significant strain on operating models historically designed for relatively stable volumes.

In Canada, insured losses from natural catastrophes reached a record $9.2 billion in 2024, nearly three times the level observed in 2023. While 2025 was less extreme, losses still exceeded $2.4 billion, confirming a structurally elevated trend. Over the long term, the annual average has increased from $557 million (1983–2008) to approximately $2.8 billion since 2009, representing a fivefold increase. a fivefold increase.

This pressure translates directly into operations. In 2024 alone, insurers processed over 278,000 catastrophe-related claims, including 228,000 in a single month, highlighting unprecedented volume surges. These figures clearly expose the structural limitations of current operating models.

Globally, insured losses exceeded US$108 billion in 2025, significantly above the historical average. This volatility is no longer cyclical—it has become the new normal for the industry.

A transformation driven by artificial intelligence

In this context, artificial intelligence (AI) is emerging as a critical strategic lever. It enables insurers to absorb increasing claim volumes while simultaneously improving operational performance and customer experience.

However, this transformation goes far beyond technology. It requires:

• a redefinition of roles and responsibilities
• the evolution of workforce capabilities
• the integration of AI into enterprise risk management frameworks

Claims functions are therefore evolving from a traditional cost center into a value creation engine, where speed, decision quality, and customer experience become key differentiators.

The central role of the target operating model

The Target Operating Model (TOM) is the key enabler of this transformation. It aligns strategy, processes, technology, and workforce within a highly volatile environment shaped by AI integration.

This is not about layering new technologies onto existing processes. It requires a fundamental redesign of operations, shifting from a reactive model to a predictive, scalable, and customer-centric model.

In practical terms, this includes:

• end-to-end automation (STP) for simple claims
• AI-assisted decision-making for complex cases
• repositioning of experts toward exception handling and high-value activities

Despite progress, several challenges remain:

• fragmented processes exist
• governance and trust concerns to deal with
• technology integration complexity
• difficulty in rapidly demonstrating business value.

Without a clearly defined operating model, AI initiatives risk remaining fragmented and failing to scale effectively.

Toward a resilient and adaptive operating model

Leading insurers are converging toward operating models designed to effectively manage catastrophic events and volume surges. These models are built around five core capabilities.

1. Surge absorption at First Notice of Loss (FNOL)

Insurers must be able to handle massive inflows of claims through digital channels and automation. AI enables early-stage triage at the point of intake, reducing delays and improving accessibility.

→ Impact: reduced initial waiting times and enhanced customer access

2. Intelligent triage and dynamic assignment

Claims are automatically prioritized based on complexity and severity. Simple cases are processed through automation, while complex claims are routed to appropriate experts.

→ Impact: improved cycle times and optimized resource allocation

3. Adaptive and augmented workforce

The workforce model evolves toward a hybrid approach combining internal staff, external partners, and temporary resources, supported by AI tools. Experts are increasingly focused on high-value decision making.

→ Impact: increased flexibility and improved productivity

4. Real-time orchestration

Command centers provide real-time visibility into volumes and performance, enabling dynamic operational adjustments.

→ Impact: proactive management and reduced operational bottleneck

5. Integrated ecosystem

Claims management increasingly relies on an extended ecosystem of partners, including repair networks, inspection technologies (e.g., drones), and geospatial analytics tools.

→ Impact: faster processing cycles and improved execution quality

Evolution in claims management

Claims management is evolving toward an event-driven model capable of dynamically adapting to market conditions and volume surges. In this context, the ability to industrialize operations while maintaining high-quality customer experience is becoming a critical competitive advantage.

Insurers that succeed will be those able to combine operational agility, structured AI integration, and real-time ecosystem orchestration—while maintaining the flexibility to respond to external shocks.