6 MINUTE READ
Catastrophic (CAT) events strain every part of the insurance value chain, and reinsurers face their own version of that pressure. While primary insurers handle the direct surge in claims from policyholders, reinsurers are responsible for processing large volumes of aggregated claims data submitted by multiple cedents (primary carriers). These reports often arrive in inconsistent formats and at different times, often missing critical data. This blog explores how AI supports reinsurance teams under pressure.
Why CAT Events Stress Reinsurance Operations
Reinsurers feel the impact of natural catastrophe losses most acutely. According to Swiss Re’s latest Sigma report, global insured losses from natural catastrophes totaled $ 137 billion in 2024, trending toward $145 billion in 2025, approximately a 6% increase. These figures reflect a rising frequency of multi-billion-dollar events happening across geographies and insurance lines, often within weeks of each other. In 2024, for instance, Hurricane Idalia struck the U.S. just weeks after a record-breaking derecho event in the Midwest.
For reinsurers, this surge in claims activity creates a perfect storm of operational stress:
- Thousands of loss reports from multiple cedents (main carriers) arrive at once, each in different formats and levels of detail.
- Complex treaty terms, each requiring different validation and documentation.
- Key details like loss dates, financial exposure, policy terms, and supporting documentation are often missing or delayed.
- Data required to model exposure or validate recoverables is scattered across emails, spreadsheets, and documents with little consistency.
- Internal resources are quickly overwhelmed, and teams shift into reactive mode.
In theory, reinsurers rely on defined thresholds to get involved in a claim. But when those thresholds are crossed late (meaning the claim only exceeds the reinsurance attachment point well after initial reporting) and the underlying claim information is still unstructured or incomplete, critical decisions — such as reserve setting, cash flow forecasting, and recovery planning — get delayed, placing business continuity at risk.
Traditional Claims Tools Break Under CAT Pressure
Reinsurance claims processes are often dependent on manual inputs, reactive workflows, and file-based communications. CAT events expose the limits of these approaches immediately.
Consider how most cedents notify reinsurers of a qualifying claim. They may send a loss bordereau via spreadsheet, attach a PDF with notes from the adjuster, and follow up with a separate email containing the policy declaration. None of this data is structured. None of it feeds into a centralized system automatically. And none of it arrives in a standardized format that allows for quick analysis.
As a result, adjusters and analysts spend hours reformatting and interpreting raw claim data. Documents are logged in one system, communications tracked in another, and treaty terms reviewed elsewhere, creating unnecessary complexity. Key decisions are delayed not due to lack of expertise, but because teams are stuck trying to make sense of messy, inconsistent inputs.
This inefficiency has downstream effects. Reinsurers often discover too late that a claim doesn’t meet treaty requirements, or that key documentation is missing. This leads to disputes with cedents, payment delays, and increased audit risk. And it happens precisely when speed and accuracy are needed most.
AI Brings Order to Chaos in Reinsurance CAT Claims
AI in reinsurance claims is not about replacing human judgment. It’s about helping claims professionals process and understand data at a scale that manual methods simply can’t handle—especially during catastrophic events. With the advanced technology of AI in claims now available, specifically AI agents designed to handle claims lifecycles, reinsurers can handle CAT events at scale without overwhelming internal teams.
Here’s how AI supports reinsurance claims operations in practice:
Automatic claim intake
AI can ingest emails, PDFs, spreadsheets, and broker correspondence directly from cedents. AI agents read each document, extract relevant claim fields (like loss location, policyholder, reserve amount), and create a structured digital record.
Document classification and field extraction
AI agents automatically detect the type of each incoming file — loss notice, policy schedule, repair estimate — and extract the specific data fields relevant to that document.
Standardization across cedents
One cedent may write “settlement total,” another may say “indemnity paid.” AI agents are trained to recognize these as the same value, and automatically normalize the data. This enables apples-to-apples comparison across sources, making aggregation and analysis reliable.
Real-time alerts and threshold tracking
AI agents continuously monitor incoming claims data. When a loss crosses a treaty threshold — for example, a $250,000 exposure under an XOL agreement — they can automatically trigger a workflow, flag it to the appropriate team, and log the event for audit purposes.
Compliance and treaty validation
AI agents can verify whether each claim submission meets the requirements of the relevant reinsurance contract. For instance, if a facultative treaty requires specific documentation within 10 days of FNOL, the AI agent tracks that timeline and alerts users to missing files or deadlines at risk.
These capabilities eliminate delays caused by formatting issues or incomplete data, and give reinsurers full visibility even while events are still unfolding. Teams spend less time organizing inputs and more time making decisions, making it possible to respond faster, avoid disputes, and support cedents with confidence.
Better Claims Outcomes, Better Cedent Relationships
When claims are handled faster and with fewer errors, reinsurers improve more than just efficiency, they strengthen their position across the entire value chain.
Automated workflows lead to fewer disputes and smoother relationships with cedents, helping to build trust when it matters most. Faster claim resolutions also means more accurate and timely reserve setting, which reduces capital volatility and supports better financial planning. With structured data flowing in near real-time, reinsurers can spot loss trends earlier, forecast exposure with more confidence, and respond to developing events instead of reacting after the fact.
Ultimately, with the right AI tools, reinsurers gain the ability to manage high-stakes, high-volume claims situations with calm and consistency. That translates to better outcomes for all parties — reinsurer, cedent, and policyholder alike.
How Clive™ AI Supports CAT Reinsurance at Scale
Clive™ is Five Sigma’s AI Claims Adjuster, built to support reinsurance teams with automation, real time insights and 360° claim visibility.
Clive offers dynamic oversight by continuously analyzing inbound claim data, identifying key risks, matching claims to treaties, and surfacing opportunities for faster recoveries.
He can detect anomalies, assist with complex claim validation, improve accuracy, and streamline reinsurance workflows – connecting to any existing system.
Here’s how Clive supports reinsurance claims during CAT events:
Instant structuring of cedent data
Clive reads incoming loss notices, spreadsheets, and emails and turns them into clean, usable claim records without manual entry.
Threshold monitoring and alerting
Clive continuously tracks claim values and exposure indicators, triggering alerts the moment a claim qualifies under a reinsurance treaty.
Treaty validation and documentation tracking
Clive checks that each claim aligns with the terms of the relevant treaty, flags missing documentation, and tracks deadlines, helping avoid compliance issues or payment delays.
Centralized claim visibility across cedents
All claims, communication, and supporting documents are consolidated into one view, giving reinsurers real-time insight across multiple events, regions, and lines of business.
Reinsurers Handle CAT Claims Better with AI
Reinsurance exists to provide stability when primary insurers reach their limits. But as catastrophic events become more frequent and more expensive, traditional, manual processes can no longer keep pace with the volume and complexity of claims. AI brings structure to that chaos—eliminating repetitive tasks, improving accuracy, and giving reinsurers the visibility they need to respond faster and more effectively
