Allianz

In property and casualty insurance, natural catastrophe events create surge conditions where claims volume spikes simultaneously across all complexity tiers. For Allianz in Australia, this dynamic produced a compounding bottleneck: when storms or floods struck, food spoilage claims — typically under AUD$500 and straightforward to assess — accumulated in the same queues as high-complexity structural and liability claims, competing for the same adjusters' attention. Rather than being resolved quickly, these routine claims sat unprocessed for four or more days, leaving policyholders waiting through already stressful circumstances. Adjuster capacity was consumed by volume rather than complexity, driving delays, customer dissatisfaction, and a backlog that scaled with every catastrophe event.

Allianz deployed Project Nemo in July 2025, an agentic AI system purpose-built to automate end-to-end processing of low-complexity food spoilage claims in Australia. Built on generative AI, the system orchestrates seven specialized agents operating in sequence: a planner agent for routing and coordination, and domain-specific agents covering cyber risk, coverage verification, weather data correlation, fraud detection, payout calculation, and audit trail generation. Each agent handles a discrete workflow step and passes structured outputs to the next. The architecture preserves human authority throughout — AI agents compile a complete recommendation and summary for review, but a licensed claims professional retains final payout approval. Integrated with Allianz's existing claims infrastructure, the system went live in under 100 days — a deliberate scope decision that reduced implementation risk and enabled rapid validation before broader rollout.

Project Nemo reduced food spoilage claims processing time by 80%, compressing a multi-day queue into same-day or sub-hour resolution. The full automated workflow — from initial claim submission through AI analysis to human review — completes in under 5 minutes, against a previous baseline of four or more days. The solution reached production in under 100 days, validating the scoped, modular deployment approach. Additional outcomes include:

• Claims adjusters were freed from high-volume routine tasks to focus on higher-severity catastrophe claims arriving simultaneously
• The human-in-the-loop governance model was maintained throughout, supporting regulatory compliance and policyholder trust
• The implementation established a reusable architecture blueprint for extending agentic AI to additional claim types and Allianz markets globally

• Scoping the initial deployment to a single, well-bounded claim type (food spoilage under AUD$500) enabled a sub-100-day go-live and provided clear, measurable ROI from day one.
• Multi-agent architectures with discrete, specialized agents are more auditable and maintainable than monolithic AI for multi-step insurance workflows — each agent's responsibility is traceable.
• Human-in-the-loop is a design principle, not a compromise — preserving final human authority satisfies regulatory requirements while still delivering substantial efficiency gains.
• Catastrophe surge scenarios are ideal pilot contexts for agentic AI: high claim volume, repetitive task structure, and an immediate business case make success easy to demonstrate and defend.