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RMS launches probabilistic cyber risk model

March 07, 2018

Catastrophe modelling firm RMS has released third generation of its cyber risk management platform, marking the launch of the (re)insurance industry’s first probabilistic model for the peril.

Dubbed RMS Cyber Solutions, the upgraded platform provides losses at different return periods for all five of the major cyber loss processes, including events such as cloud outages, contagious malware and denial of service attacks, enabling insurers to allocate capital to cyber risk in a rigorous and quantitative way.

The latest release also adds additional functionality to apply to reinsurance of cyber losses, providing financial perspectives to all reinsurance stakeholders. Furthermore, it provides tools to allow model users to incorporate their own loss experience into the model and develop their own view of risk.

The platform also extends the functionality for carriers to analyse their 'silent' cyber exposure in other classes of insurance, such as property damage, energy, and marine, by relating policy terms and conditions and linking to portfolios of property exposure data in the RMS EDM, the data standard used by most of the insurance industry.

RMS Cyber Solutions also incorporates new analytics for selecting and pricing individual accounts, and for quantifying loss probabilities for cyber risk rating factors.

Commenting on the release, RMS’ head of cyber solutions Adam Sandler said: "RMS clients are seeing demand for cyber insurance growing rapidly and their ability to pursue this opportunity is constrained by their ability to allocate risk capital with confidence.

“Cyber is still relatively unknown and doesn't behave like other perils. Our clients' highest priority request to RMS over the past couple of years has been for cyber loss probabilities, particularly for our accumulation scenarios, to assess the cost of capital needed to support this growth opportunity. With this release, we are answering that need.”

Head of cyber model development at RMS, Dr. Christos Mitas added: "Statistical experience data only provides a few years of benchmarking, and the patterns of loss continue to shift. Our models show that loss processes such as contagious malware have the capability to scale and trigger large losses much more easily than others, such as data exfiltration, where attackers target individual companies to steal their sensitive data, or cloud outage, which is currently limited by the customer base of cloud service providers."