News & Insights
The key barrier to offering meaningful limits and affordable pricing for products such as cyber is a lack of historical data. However, insurers are increasingly recognising that the past is no longer necessarily an accurate proxy for future claims experience.
Identifying the most attractive segments in a market requires an exhaustive view of the entire insurable population at the resolution of each insurable risk. A comparison between population performance vs. portfolio performance enables a cycle of continual optimisation
The ‘middle’ of risk is disappearing, and gathering comprehensive data from new sources is now central to an insurer's ability to price effectively.
Traditionally insurers gathered this information by asking insureds questions or manually collating it from various sources. Now construction data is increasingly accessible from third party datasets.
Historically, there was a huge gap between an insurer's estimate of a risk and the manifestation of the risk in reality. As more data has become available, this gap has begun to close.
What is machine learning and how it can be used in an insurance context?
Using AI, insurers can generate a more factual, robust representation of commercial risk that is built from more than just historical claims data.
"I'm delighted to be working with Cytora. They have a fantastic technology and approach to building their business. It's a great opportunity to be working with a talented team as they look to disrupt the insurance market."
Ted will play a key role in advancing Cytora's approach to understanding commercial risk by leveraging unstructured web data, along with fellow University of Cambridge Professor Bill Byrne, who also joined the company recently.
Data analytics company Cytora has announced that Bill Byrne, Professor of Information Engineering at the University of Cambridge, will join their scientific advisory board this month.