Guidewire’s data chief Eugene Lee explains why acquisition of Cyence can significantly boost risk understanding
Guidewire can signficantly boost brokers and insurers cyber risk understanding thanks to its Cyence acquisition, says the tech firm’s data boss Eugene Lee.
Data and analytics vice president Eugene Lee, speaking to Insurance Times following Guidewire’s $265m acquisition of Cyence, says the tech giant has the solution to help insurers and brokers better understand 21st century risks - such as cyber, reputation and forms of business interruption - which have historic claims data issues.
Cyber claims data issue
“Historically, insurers have used actuarial science based on the premises of historical losses, like claims data, and used it to project future risk. That is the classical approach.
“The problem we see many insurance companies having, is that there are new classes of risk, for which the historical loss data is not that helpful,” he says.
Lee says the problem is that losses are quite infrequent, meaning there is no great mass of historical claims data currently out there.
In this environment, insurers struggle to grasp the accumulation of risk from their internal data sources.
“There are no obvious patterns of accumulation.
“The best parallel would be cat risk. If you are an insurance company, you make sure you don’t write all your business in a single flood plane. You ensure the risk is diversified.
“In cyber, it is similar accumulation of risk but based on things a bit less obvious.
“For example the hosting provider. If you were an insurer, you would know the risk of Microsoft - just taking that as an example - but the problem is, how many of your commercial clients are actually using Microsoft hosting services? And what happens if Microsoft services has an attack - how much of an accumulation of risk do you have with these points?”, Lee says.
Lee says insurance actuarial teams are facing a near impossible task quantifying risk using just their own internal data, but if combined with highly useful external data from Cyence, understanding the risk becomes much clearer.
Cyence scours a vast amount of data sources - onlnie information from companies, organisations, individual information and public sources - and then refines it into useful data that can be employed by insurance teams.
“Cyence listens to hundreds of data sources, public or private, that are relevant to a given type of risk.
“They then curate those and use machine learning to ID which types of data are relevant to the quantification of the risk and run it through risk models to provide insurers, brokers and ratings agencies on the qualification of cyber risk,” he says.
Lee says that Cyence can link into Guidewire.
For example, if there was a particular book of business on PolicyCenter, Cyence could link into that book.
Cyence will probably be more commonly used as a standalone solution that insurance and broking firms use, he adds.
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