How Can AI Support Catastrophe Response Across Delegated Portfolios?

After a major event, managing agents need to understand which delegated risks and claims may be affected.

The available data is rarely complete at the same moment. Risk bordereaux may be several weeks old, new claims may arrive in local formats, and the geographic or temporal definition of the event can develop as more information becomes available.

AI can help teams connect these sources and focus requests for missing information. Early results must remain provisional, traceable and subject to exposure and claims expertise.

Major events create a fast-changing data problem

Routine exposure monitoring builds a continuing view of accumulations by location, peril and portfolio. Catastrophe response asks a more immediate question: which records may relate to this particular event, and what is still unknown?

Delegated portfolios make that question harder because data comes from several coverholders and DCAs. Addresses, coordinates, location identifiers and cause-of-loss descriptions may be inconsistent. Some recent risks may not yet appear in the latest accepted bordereau.

Early claims notifications add another source. A loss description may name a storm, describe damage without naming it or use a local event code. The response team needs to connect likely records without presenting an incomplete match as a confirmed loss.

Event definitions and deterministic filters come first

Start with an authorised event definition. Record the relevant catastrophe code where one has been issued, the event name, date range, perils and geographic scope. Every change should be versioned because later information may expand or narrow the definition.

Build an inventory of the latest risk, location and claims data from each delegated partner. Show reporting dates and known gaps. Deterministic filters can identify clear matches using dates, coordinates, postcodes, administrative areas and official event codes.

The baseline should preserve the source data and distinguish potentially exposed risks from notified claims. A risk inside an event footprint is not automatically a claim, and a reported claim still requires normal coverage and handling decisions.

AI can connect ambiguous risks and early claims

AI can support records that deterministic filters cannot resolve. It can parse inconsistent addresses, interpret loss narratives, recognise alternative place names and rank likely links between early claims and the defined event.

It can also group missing-data issues by coverholder, identify duplicate notifications and prioritise records whose uncertainty could materially affect the provisional view. This helps specialists direct questions where they are most useful.

Each suggested match should show the source evidence, method and confidence. A model may confuse places with similar names, infer the wrong event from a general storm description or miss a risk whose address is a head office rather than the insured location.

Exposure and claims specialists should review uncertain or material cases. Approved decisions can improve future matching patterns, but they should not erase the original evidence.

Provisional insight needs visible uncertainty

An early event view should state when each source was received, which partners have not reported and which records remain ambiguous. Counts and financial values should separate confirmed claims, potential matches and unassessed gaps.

Reconciliation is continuous. New bordereaux, claims notifications and corrected locations may add, remove or reclassify records. Each published version needs a timestamp, event-definition version and explanation of material changes.

AI-supported matching should be monitored for false positives and missed records. Access controls and audit trails are particularly important because catastrophe data can influence operational priorities, market reporting and senior decisions.

The workflow supports response coordination. It does not calculate the final loss, set reserves or determine coverage. Those conclusions require the relevant models, evidence and accountable professionals.

Example

A hypothetical managing agent needs an early view of property business potentially affected by a named windstorm across several coverholders.

Controlled date and location filters identify clear risk and claim matches. AI ranks records with incomplete addresses and compares early loss descriptions with the authorised event definition. It also highlights two coverholders whose latest risk data predates recent underwriting activity.

Exposure and claims specialists review the material ambiguous cases. They publish a provisional view that separates confirmed claims, potential exposure and missing information, with the source date and confidence visible.

FAQs

• Is catastrophe response the same as aggregate exposure monitoring?

  No. Aggregate exposure monitoring is an ongoing view of concentrations. Catastrophe response applies a specific, changing event definition to risks, early claims and reporting gaps so teams can coordinate their response.

• Can AI estimate the final loss from early bordereaux?

  This workflow identifies and organises potentially relevant data. Final-loss estimation, catastrophe modelling and reserve setting require different evidence, methods and professional judgement, especially while early reports remain incomplete.

• How should uncertain event matches be handled?

  Retain the source evidence, assign confidence and route material cases to exposure or claims specialists. Keep the match provisional until it is confirmed rather than forcing every record into or out of the event.