The RMS RiskScore is a single number representing the results of a big data catastrophe simulation. Its simplicity stands in contrast to the massive complexity of the process that creates it.

Capturing Catastrophe Insight Without Running a Catastrophe Model

For the point of underwriting, a RiskScore concisely captures the science of modeling by expressing risk as a 1 to 10 score with clear definitions for each value. Based on industry leading risk models, RMS RiskScores are calibrated on billions of dollars of historical claims.

The models creating these RiskScores are grounded in today's reality, providing the scientific insight to capture the risk of tomorrow’s catastrophe.

Pre-Compiled Insight for Quick Decisions

RMS pre-compiles its cat simulations into risk scores for every significant combination of exposure characteristic (construction, height, year built, and others) to provide instant insight into a risk’s damage potential at key return periods. For example: a 100-year earthquake risk score of 8 corresponds to 30-40% damage, enabling quick decisions about whether to decline, quote, or refer.

The Most Accurate and Holistic Score on the Market

For years, the industry has relied on deficient hazard-only scores that failed to distinguish different types of building construction and ages. RMS RiskScores reflect these differentiations, enabling better decision making at the point of underwriting.

High Resolution Insight for High-Resolution Perils

For events like flood and wildfire, where small differences in location can translate to large differences in loss – precision counts. RMS provides the resolution needed to compete in today’s competitive market.

Use Risk Scoring to Separate the Wheat from the Chaff

Given 10 locations in a single neighborhood that look the same on paper, some will score very differently using RMS RiskScores. Based on hazard, vulnerability, and precise site conditions, the scores provide deep insight into location-level catastrophe risk to drive better risk selection and avoid adverse selection.