Strikes, Riots, and Civil Commotion (SRCC) Risk Model

Our model gives you a realistic estimation of losses to quantify the impact of SRCC events in the US—and could change how you approach the increasing risk posed by civil unrest.

Rising risk and losses for SRCC-related events

SRCC events are emerging as the top concern in the political violence insurance market. Relying on historical event data and country-level aggregates of risk might not provide all the answers you need.

Discover the first SRCC catastrophe model to quantify risk

For the first time, access numerical estimates of potential losses from SRCC events and business interruption in the US to enable more informed decisions on insurance pricing, capital allocation, risk management, and mitigation.

Confidently price SRCC risk

The SRCC probabilistic model delivers a dollar loss output, allowing for direct input into pricing and capital modeling —and more informed decisions about (re)insurance.

Improve your SRCC underwriting strategy

Uncover areas of highest predicted risk to improve your underwriting strategy, selecting appropriate risks vs. premium and identifying areas for growth.

Plan for the plausible

Assess tail risk more accurately through a catalog of stochastic events featuring scenarios that are inherently plausible, but far more severe than any actual historical occurrences.

Regulatory stress testing

Stress test extreme disaster scenarios, using our model to help reveal potential vulnerabilities and fulfill your risk management and regulatory requirements—especially given recent regulatory focus.

Features and capabilities

Commercial and municipal property in the US

For US-located assets, specifically estimate commercial and municipal property losses and business interruption from SRCC-related damage (fire, looting and vandalism), down to the ZIP code level.

Loss roll-ups

Roll up SRCC modeled losses with natural peril modeled losses for a truly comprehensive, probabilistic enterprise view of risk.

Transparent methodology

Use our model to predict the severity of events by evaluating social and economic trends, political factors, and historical protest patterns.

500,000-year stochastic catalog

Reduce uncertainty in the variability of losses by tapping into an exhaustive catalog.

A complete view of SRCC risk

Combine with our Global SRCC Predictive Scores and qualitative services to create a complete view of SRCC risk in the US.

Gain a deeper understanding of SRCC risk

Developed as part of the Lloyd’s Lab accelerator program, Global SRCC Predictive Scores enable you to forecast and monitor geospatial SRCC risks for more than 50,000 counties and districts globally, for 12 months into the future.

Learn about Global SRCC Predictive Scores

Gain a deeper understanding of SRCC risk

Do more with the Verisk ecosystem

Learn more about how we can help you with political violence (re)insurance.

Contact us to learn more

Telephone support is available at the following offices, please call your local provider at one of the listed numbers below:

Support numbers:

Boston +1 6172676645

London: +44 (0)20 37640300

Munich: +49 (0)89 2323735 0

Hyderabad: +91 40 66002266

Beijing: +86 10 5965 2780

Singapore: +65 6249 0770

Tokyo: +81 (0)3 6551 2840

Sales/Support

First Name

Last Name

Business Email

Company

Title

Country

How can we help you?

Strikes, Riots, and Civil Commotion (SRCC) Risk Model

Rising risk and losses for SRCC-related events

Discover the first SRCC catastrophe model to quantify risk

Confidently price SRCC risk

Improve your SRCC underwriting strategy

Plan for the plausible

Regulatory stress testing

Features and capabilities

Commercial and municipal property in the US

Loss roll-ups

Transparent methodology

500,000-year stochastic catalog

A complete view of SRCC risk

Gain a deeper understanding of SRCC risk

Gain a deeper understanding of SRCC risk

Do more with the Verisk ecosystem

The enterprise risk modeling platform

Verisk Synergy Studio

Geopolitical and Country Risk

Contact us to learn more

Support numbers: