---
title: "Insured Catastrophe Losses"
slug: catastrophe-losses
type: data-index
sector: insurance
canonical_url: https://deepstoryresearch.com/data/catastrophe-losses
series: [provider_spread, annual_losses, forecast_vs_actual]
series_count: 3
data_point_count: 8
data_as_of: 2025
source_quality: curated_snapshot
tier: free
generated_at: 2026-08-11
---

# Insured Catastrophe Losses
_Four numbers for one year, and a forecast that overshot by 35%_

> Deepstory Research context file · **Free tier** · <https://deepstoryresearch.com/data/catastrophe-losses>
> Self-contained AI briefing. Drop into an LLM window or RAG pipeline.

## AI research use contract

- Treat this file as source-grounded context, not a live database. Quote `source_ref` and `data_as_of` when using numbers.
- Separate `FACT`, `ESTIMATE`, `FORECAST`, `TARGET`, and `INFERENCE`. If a row basis is unclear, say it is unclear.
- Prefer T1/T2 sources for hard facts; use T3/T4 sources as context that should be checked before money, legal, medical, operational, or policy decisions.
- Keep answer structure clean: first say what the data says, then what you infer, then what would change the conclusion.

## Research upgrade checklist

- Never treat different providers' loss estimates as measurements of one number; event scope differs.
- Distinguish an originally published figure from a later restatement by the same provider.
- Separate insured losses from economic losses; the protection gap is the difference.
- Add a prompt on why a trend projection is not a prediction of any single year.

## TL;DR — index summary

Published estimates of 2024 global insured catastrophe losses span $137bn to $154bn — a 12% range, two of them from Swiss Re alone ($137bn as published, $141bn restated). 2025 came in at $107bn, down 24% but still the sixth consecutive year above $100bn, against a Swiss Re projection of $145bn.

## What this index covers

Three series: 2024 estimates by provider, insured losses by year, and the 2025 forecast against the 2025 outcome.

**Series in this index:**

- `provider_spread` — What 2024 catastrophes cost insurers, by estimating body.
- `annual_losses` — Insured natural-catastrophe losses by year.
- `forecast_vs_actual` — Swiss Re's 2025 projection against the outcome.

**Entities tracked:** Swiss Re, Munich Re, Gallagher Re.

## Key findings

- 2024 estimates range $137bn–$154bn across four published figures (swissre-2025-pr / gallagherre-154).
- Swiss Re published $137bn and later restated to $141bn; Munich Re independently landed at $140bn (munichre-140).
- 2025 insured losses were $107bn, down 24%, the sixth straight year above $100bn (swissre-2025-pr).
- Swiss Re's sigma 1/2025 projected $145bn for 2025 — a 35% overshoot (swissre-sigma-1-2025).

## Evidence basis map

The free tables above show `source_ref` but not the row-level `value_basis`. This section mirrors the visible rows only, so AI tools can separate observed values from estimates, forecasts, targets, and derived values.

| series | row | source_ref | value_basis |
| --- | --- | --- | --- |
| provider_spread | provider=Swiss Re (as first published); usd_b=137 | swissre-sigma-1-2025 | Swiss Re sigma originally published 2024 global insured nat-cat losses at USD 137bn — the most widely quoted figure |
| provider_spread | provider=Munich Re; usd_b=140 | munichre-140 | Munich Re estimates 2024 insured nat-cat losses at USD 140bn (economic losses USD 320bn) |
| provider_spread | provider=Swiss Re (restated); usd_b=141 | swissre-2025-pr | Swiss Re uses USD 141bn as the 2024 base when reporting 2025 at USD 107bn, a stated 24% decline (107 ÷ 141 − 1 = −24.1%) |
| provider_spread | provider=Gallagher Re; usd_b=154 | gallagherre-154 | Gallagher Re: insurers covered USD 154bn of USD 417bn total 2024 economic losses — wider event scope |
| annual_losses | year=2024; usd_b=141 | swissre-2025-pr | Swiss Re restated 2024 basis, USD 141bn (see provider-spread chart for the USD 137bn originally published) |
| annual_losses | year=2025; usd_b=107 | swissre-2025-pr | USD 107bn insured nat-cat losses in 2025 — sixth consecutive year above USD 100bn |
| forecast_vs_actual | basis=2025 forecast (sigma 1/2025); usd_b=145 | swissre-sigma-1-2025 | Swiss Re sigma 1/2025 projected 2025 insured losses on trend to USD 145bn — FORECAST |
| forecast_vs_actual | basis=2025 actual; usd_b=107 | swissre-2025-pr | Actual 2025 insured nat-cat losses USD 107bn — the forecast overshot by ~35% |

## The series (per-chart briefings)

### Provider spread — hbar
**Direct answer:** What 2024 catastrophes cost insurers, by estimating body.
**Time bracket:** 2024

| provider | usd_b | source_ref |
| --- | --- | --- |
| Swiss Re (as first published) | 137 | swissre-sigma-1-2025 |
| Munich Re | 140 | munichre-140 |
| Swiss Re (restated) | 141 | swissre-2025-pr |
| Gallagher Re | 154 | gallagherre-154 |

**Read:** The most-quoted figure ($137bn) is no longer the current one.
**Caveat:** Gallagher Re's wider event scope explains most of the gap to $154bn.

### Annual losses — bar
**Direct answer:** Insured natural-catastrophe losses by year.
**Time bracket:** annual

| year | usd_b | source_ref |
| --- | --- | --- |
| 2024 | 141 | swissre-2025-pr |
| 2025 | 107 | swissre-2025-pr |

**Read:** Sixth consecutive year above $100bn.
**Caveat:** On Swiss Re's current basis.

### Forecast vs actual — hbar
**Direct answer:** Swiss Re's 2025 projection against the outcome.
**Time bracket:** 2025

| basis | usd_b | source_ref |
| --- | --- | --- |
| 2025 forecast (sigma 1/2025) | 145 | swissre-sigma-1-2025 |
| 2025 actual | 107 | swissre-2025-pr |

**Read:** The forecast overshot by 35%.

## Cross-series synthesis — why this matters

The provider_spread and forecast_vs_actual charts together make one point: catastrophe loss figures are estimates with wide error bars in both directions — across providers for a past year, and against outcome for a future one. The long-run 5–7% real annual increase is the durable signal; any single year is weather.

Swiss Re puts roughly a one-in-ten chance on a $300bn peak year. Against a $107bn actual, that tail is the number that matters for capital planning, not the central trend.

## Entities & relationships

| Entity | What they do | Key stat | Relationship |
| --- | --- | --- | --- |
| Swiss Re | Primary estimator | $137bn then $141bn for 2024; $107bn for 2025 | Both the most-quoted source and the restater |
| Munich Re | Independent estimator | $140bn for 2024 | Corroborates the ~$140bn cluster |
| Gallagher Re | Broadest scope | $154bn for 2024 | Wider event definition explains the outlier |

## Timeline of key events & decisions

- **2024** — Four published estimates spanning $137–154bn → the scope-dependence of loss data
- **sigma 1/2025** — Swiss Re projects $145bn for 2025 → the trend extrapolation
- **2025** — Actual $107bn, down 24% → the 35% overshoot

## Cross-industry ripple

- **Home insurance:** Loss experience feeds directly into the rate filings tracked in home-insurance-premiums. _(inference)_
- **Reinsurance pricing:** A quiet year plus a wide estimate range complicates renewal negotiation. _(inference)_
- **Municipal / sovereign risk:** The protection gap — economic losses above insured losses — lands on public balance sheets. _(inference)_

## Non-obvious reads (interpretation)

_This section is interpretation, not sourced fact — each item names the observation it is built on and a confidence level._

- **Observation:** A single quiet hurricane season moved the outcome 35% below a trend projection.
  **Read:** Annual catastrophe figures carry very little information about the trend. Insurers and regulators reacting to one year's result are reacting mostly to weather variance, not to a change in exposure. _(confidence: high)_

## Glossary / key terms

- **Insured loss** — The portion of economic damage covered by insurance.
- **Protection gap** — Economic losses minus insured losses.
- **sigma** — Swiss Re's research publication series.

## How to use this with AI

Paste this file into an LLM context window (or a RAG store) and ask cross-series questions. The tables carry a `source_ref` per row; the source registry below maps each ref to a named source and a trust tier.

### Suggested prompts (multi-series)

```text
Using provider_spread, explain why averaging the four 2024 figures would be wrong.
```

```text
Using annual_losses and forecast_vs_actual, argue how much weight a single year should carry in reinsurance pricing.
```

## Sources & trust

| ref | source | trust tier | url |
| --- | --- | --- | --- |
| swissre-2025-pr | Swiss Re — 2025 marks sixth year insured nat-cat losses exceed USD 100 billion | T3 · Reputable press / research org / OWID | https://www.swissre.com/press-release/2025-marks-sixth-year-insured-natural-catastrophe-losses-exceed-USD-100-billion-finds-Swiss-Re-Institute/f710c271-58c8-4c48-9004-05203634d1e0 |
| swissre-sigma-1-2025 | Swiss Re sigma 1/2025 — Natural catastrophes: insured losses on trend to USD 145 billion in 2025 | T3 · Reputable press / research org / OWID | https://www.swissre.com/institute/research/sigma-research/sigma-2025-01-natural-catastrophes-trend.html |
| munichre-140 | Artemis — Munich Re estimates 2024 insured catastrophe losses at $140bn | T3 · Reputable press / research org / OWID | https://www.artemis.bm/news/munich-re-estimates-2024-insured-catastrophe-losses-at-140bn/ |
| gallagherre-154 | Risk & Insurance — Gallagher Re: natural disasters cost the global economy $417bn in 2024 | T3 · Reputable press / research org / OWID | https://riskandinsurance.com/natural-disasters-cost-global-economy-417-billion-in-2024-gallagher-re/ |

## Caveats & what this index cannot answer

- Peril-level or regional breakdowns.
- Economic (uninsured) losses.
- Whether 2025 signals a change in trend.
- Estimates and forward targets are labelled in the working dataset; never read a labelled estimate or forecast as a settled figure.
- This is an observational data index, not investment, legal, or medical advice.

## Data freshness & methodology

- **Last updated:** 2026-08-11
- **Data as of:** 2025
- **What changed most recently:** 2025 insured losses came in at $107bn against a $145bn projection.

**Methodology (as seeded):**

> Insured losses only — the portion of catastrophe damage covered by
> insurance, always far below total economic loss (Gallagher Re put 2024
> economic losses at $417bn against $154bn insured; Munich Re at $320bn
> against $140bn). The difference is the protection gap.
> 
> THE 2024 NUMBER IS CONTESTED, and two of the four figures are Swiss
> Re’s own. $137bn is what Swiss Re originally published and remains by
> far the most widely quoted. $141bn is the restated basis Swiss Re
> itself uses when reporting 2025 at $107bn as "a 24% decline"
> (107 ÷ 141 − 1 = −24.1%, which only works from 141). Munich Re
> independently lands at $140bn. Gallagher Re’s $154bn reflects a wider
> event scope, not a different view of the same events.
> 
> The annual trend chart uses Swiss Re’s CURRENT basis ($141bn for 2024)
> so it is internally consistent with the 2025 figure beside it. The
> provider-spread chart exists so that choice is visible rather than
> silent.
> 
> OMITTED: the commonly cited $118bn for 2023 is a TREND value, not an
> actual, and is not plotted alongside actuals.
> 
> CAVEAT: the 2025 forecast-vs-actual chart compares a trend projection
> ($145bn) with an outcome ($107bn). The 35% overshoot is not a modelling
> failure — sigma projects a trend line, not any particular year’s
> weather, and one quiet hurricane season moves the outcome far more
> than the trend does.
> 
> Re-verified 2026-08-10.

---

_Free tier. The full row-level dataset and per-source detail live on the live page and in the working file. Canonical: <https://deepstoryresearch.com/data/catastrophe-losses>. Deepstory Research · https://deepstoryresearch.com_