Lloyd’s box status

BOX 64 Marine Liability Ports & Terminals Energy Cargo Political Violence / Terror

BOX 114, 115, 115A Crisis Management Cyber Property Treaty Int & US Direct & Facultative Property

Lloyd’s box status

BOX 64 Marine Liability Ports & Terminals Energy Cargo Political Violence / Terror

BOX 114, 115, 115A Crisis Management Cyber Property Treaty Int & US Direct & Facultative Property

In an increasingly unpredictable world, how do you assess risks from riots and  civil commotion (SRCC)?

Matt Hopwood

Man-Made Catastrophe Research analyst

THE PROBLEM?

Civil unrest is on the rise.

With a rise of 55% from 2023 to 2024*1in civil unrest events globally, events like urban protests or mass gatherings can have cascading impacts, from property damage to business interruption.

2023

2024

Traditional assessment methods often fall short of capturing the dynamic nature of these risks and insurers require ever more sophisticated tools to understand the potential accumulation of risk

This is where innovative solutions like crowd modelling tools come into play.

By simulating real-world scenarios, these tools enable underwriters to pinpoint high-risk areas, identify vulnerable buildings, groups of potential target buildings, and run tailored analyses of potential unrest which help them to stay ahead of emerging risks.

THE SOLUTION

IQUW’s Catastrophe Research Analyst, Matt Hopwood, has developed a unique Non-Natural Catastrophe Crowd Modelling Tool to enhance SRCC (Strikes, RIOTS and Civl Commotion) risk assessment.

The model provides insights into crowd  extent during riots or large gatherings.

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Enabling comprehensive assessment  of risks to nearby infrastructure.

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By analysing crowd formation and movement in urban areas.

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It maps building footprints near such events.

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This analysis supports underwriters in preparing for civil unrest scenarios with data-driven insights to evaluate exposure effectively.

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This tool is the first step toward creating a probabilistic framework  for IQUW’s SRCC risk assessment and, in future, will incorporate probabilistic methods to refine risk predictions, enabling us to anticipate and prepare for an even broader range of possible events.

Crowd Source Model

We’ve built an initial crowd foot-printing model to simulate crowds move during civil events such as protests, strikes, and riots. This forms the analytical core for understanding unrest dynamics.

Crowd Source Model

Building on the footprint generated by the crowd model, we’re going to introduce a probabilistic simulation layer. For every simulated event, we will build a corresponding loss distribution to estimate potential impacts. These distributions capture property damage and will be customisable by event intensity, or region. This allows us to not only model where and when events may occur, but also what their likely consequences are.

Crowd Source Model

We’ll broaden the model’s scope to capture a wider range of civil occurrences. The result is a more comprehensive, forward-looking understanding of civil unrest and its ripple effects.

Insurance Direct & Facultative Property

Insurance Political Violence & Terrorism

Better Modelling Means
Swifter Underwriting Decisions

THE METHOD?

How does the model work to analyse risk to nearby infrastructure?

WE LEVERAGE

We leverage open-source  street network data

WE BUILD

Then build a mesh-work to analyse potential crowd density limits

WE ANALYSE

We widen the mesh’s footprint to review the impact were a crowd to spill to a wider area

WE OVERLAY

We overlay with existing coverages  to understand portfolio impact

THE BENEFITS

Approximates crowd footprint area

Enables calculation of an approximate area a crowd occupies on a street network, revealing its potential impact on surrounding areas.

Identifies building risk

Automatically highlights buildings within a defined proximity to the crowd, categorising them by risk levels based on distance and exposure.

Provides Customisation and Scenario Analysis

Adjusts parameters like crowd size or can simulate unrest across multiple locations, helping to model complex, real-world scenarios with precision.

Supports swift underwriting decisions

The tool is fast and efficient, generating a static footprint in seconds.

CASE STUDY

Let’s look at a real-world example – the 2021 Capitol Riots. The tool’s capabilities are highlighted through real-world scenarios, such as the Capitol riots in 2021, demonstrating how it assesses risk and exposure

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Another day on Earth

Another day on Earth 1

Another day on Earth 2

THE IMPACT

The Impact on Critical Infrastructure

In this example, the insurer covers a metro station one block from the Capitol, which could become unusable and more vulnerable to damage during such an event.

By integrating advanced crowd dynamics data into underwriting, IQUW’s enhanced SRCC exposure tool provides deeper loss insights. This innovation enables informed decision-making, early identification of high-risk properties, effective risk mitigation, and tailored coverage solutions.