When a wildfire, hurricane, tornado or flood is unfolding, the news media plays a critical role in alerting local populations. But in the decade my colleagues and I have spent working through these events alongside carriers, I have seen how little of that coverage actually helps the claims executive who has to respond.

The “noise” from the swarm of media reports on an unfolding disaster can lead to misdirected resources, delays in dispatching adjusters, and inaccurate notifications to customers about their risks and potential damage. News media coverage that is built to inform the public doesn’t translate into actionable information a claims team can use to understand which of its insured locations sits inside the footprint.

The teams that respond well treat a catastrophe as three connected phases rather than a single scramble: the planning done long before the event; the monitoring that runs whether or not anything ever hits the book; and the claims response that follows once the footprint is known. Precise analytics change what is possible in each.

Before the Event: Planning

Effective, timely response begins long before the event, with a thorough knowledge of the carrier’s aggregated exposure and a current view of the individual risks on the book. The carrier that knows where exposure is concentrated before an event strikes is the one who can move fast when it does. An accumulation-based view of that exposure, assessed without frequency or severity assumptions, is a guard against being caught overexposed in any single region or zone. Deterministic “what-if” scenarios built from recent events will also help a team rehearse how a repeat would land on today’s book.

That view is only as good as the underlying data, so keeping exposure current and clean is part of the planning work. Insurance portfolios are not static. Values are constantly being revised, policies move in and out as they are written and expire, and new assets come onto existing accounts. This is why the exposure data should be refreshed regularly, ideally through an API that keeps it current with minimal manual effort.

Data quality matters just as much, and geocoding precision most of all, because it determines whether the response tools hold up later. There is little point in overlaying a four-meter-resolution flood footprint on locations geocoded only to a ZIP-code centroid; the footprint is far more precise than the exposure it is scoring, and the resulting read will be wrong in ways that are easy to miss. Getting geocoding right before an event is what lets high-resolution footprints deliver on their promise afterward.

This is also where the limits of the usual tools show. Probabilistic models are effective for pricing and capacity, but they were never designed for the location-level, deterministic reads that response demands—and they still produce surprises. Losses modeled as 1-in-1,000-year events have clustered within a few years of one another, and catastrophes such as the 2021 Texas freeze (Winter Storm Uri) and the 2018 Camp Fire were not captured by the models in use at the time.

The planning that matters for response is knowing your accumulations, not refining a price.

During the Event: Monitoring

Once an event is on the horizon, monitoring runs whether or not it ever reaches your book. Accurate, high-granularity forecasts and real-time event tracking let a team watch an approaching storm or fire and read how it may or may not affect the portfolio as it develops. Across the more than 40 data and modeling partners we work with, I have watched good monitoring turn a chaotic first 48 hours into something a claims leader can actually manage.

Here the choice of dataset matters as much as the decision to monitor at all. Storm tracks and the familiar cone of uncertainty are fine for news media and general public awareness, but they are poor tools for assessing potential impact across a portfolio of locations or for prioritizing where to direct resources. That is where high-granularity forecasts carrying the right intensity measures are crucial.

For an approaching hurricane, for example, wind-probability footprints and ensembles of peak wind gusts provide a far more informative view of likely impact than a track line ever could. This information tells a claims team not merely where the storm may go but how hard specific insured locations are likely to be hit.

Monitoring also drives smarter moratorium decisions. Regularly updated forecasts at the highest available granularity let carrier underwriters set moratorium criteria that reflect how the projected intensity of an event varies across geography and over time. As a hurricane approaches, for example, high-granularity forecasts show how projected intensity varies from one location to the next and how it shifts as the storm tracks in. A moratorium can, therefore, be targeted to the areas genuinely at risk and timed to the event rather than imposed across an entire state.

A team that can see the event precisely can stay open for business when competitors have shut down and reopen sooner once the risk has passed. Blanket, overly conservative moratoriums carry a real opportunity cost across a season.

It is also the moment to get ahead of the customer conversation. By generating targeted, actionable alerts about an unfolding event that may affect specific properties, a carrier can reach policyholders proactively and deliver real value when and where it matters most, rather than waiting for the first notice of loss.

After the Event: Claims Response

Once the event has passed, the work shifts to claims, where high-resolution footprints and imagery are critical. They let a team see which insured locations actually fall inside the affected area, rather than inferring it from a map on the evening news.

But knowing which locations were hit is only half the answer; the claims executive needs the financial impact. That means turning the hazard into dollars by applying vulnerability functions to translate the footprint’s intensity into expected physical damage, then financial loss modeling to run that damage through policy, facultative and treaty terms to an insured figure.

It does not have to be elaborate. Even relatively simple vulnerability curves and a straightforward application of terms produce a location-level loss estimate far faster, and far more credibly, than working backward from a probabilistic model that was never designed to generate one.

Those estimates are what let a team reserve tightly and communicate with confidence to stakeholders. Speed is the constraint—after a large-scale catastrophe, credible impact estimates from models can take weeks or months. In the events we have worked through with carriers, those delays are exactly where decisions get made on thin information. Analytics purpose-built for response compress that timeline. With elevated catastrophe exposures now a permanent reality, that speed translates directly into smarter decisions, stronger customer relationships and better overall performance.

How that analysis reaches people matters as much as the analysis itself, because this is when clear communication matters most. Claims professionals working an event rarely have time to sit at a desktop running analyses. Automated alerts delivered to mobile devices, combined with AI-based natural-language querying, keep things simple for staff in the field and busy executives on the road, who can ask a plain-language question and get a location-level answer back wherever they are.

Three Questions to Ask

Whether you build these capabilities in-house or buy them, the InsurTech market is crowded with geo-visualization, monitoring, exposure management and response in different combinations, along with many narrow point solutions for a single regional peril. For event response, I would judge any workflow on three things:

1. Speed. How fast does it turn an unfolding event into an actionable, location-level read? In response, hours matter; a tool that takes days to produce impact estimates is answering the wrong question.
2. Coverage. Does it span the full event life cycle—before, during and after—and all the perils and regions you write, in a single environment, without forcing you to move data between tools?
3. Accuracy. Can you trust and trace the numbers? High-resolution inputs, transparent vulnerability and financial assumptions, and a clear calculation trail you can adjust, so you can stand behind both the reserve and the message to the customer. This matters even more as AI enters the workflow: natural-language querying is only as trustworthy as the calculation trail behind it, so the assumptions and math must stay visible and auditable rather than hidden in a black box.

Catastrophe exposure is not going to ease for carriers or their insureds. But the teams that respond best will not be the ones watching the same media coverage as everyone else. Across every phase—planning before, monitoring during and settling claims after—they will be the ones who have turned precise, timely analytics into faster loss estimates, better-informed moratorium calls and a proactive conversation with the policyholder, reading the event clearly while others are still reacting to the noise.