A data driven understanding of cyber insurance liabilities

Exponential growth - existential problem

The insurance industry’s global cyber risk exposure is estimated to be in the region of £100 billion. Annual worldwide losses from cyber is set to quadruple to more than US$2.1 trillion by 2019.

Typical loss estimation methods used by insurers underestimate exposure (source article here).

Inadequate understanding of your exposures could be catastrophic, particularly around policies that could be triggered even though they were never intended to respond (so-called “silent cyber” liabilities).

AI technology driving a more scientific approach


The only way to be sure of your exposure is to trawl through your entire insurance policy portfolio, cyber or otherwise. But this is time consuming and costly.

ThoughtRiver uses artificial intelligence and natural language processing technology to obtain insights from unstructured text documents such as insurance policies. Each policy takes only 2 minutes to review. This gives you a scientific read on potential exposures to cyber and other latent liabilities.

How can ThoughtRiver help Insurers



A cyber-attack can impact multiple businesses globally, potentially affecting many of an insurers’ clients simultaneously. Insurers use ThoughtRiver to trawl through their existing insurance policy portfolios to map out the size of their ‘cyber’ exposure.  Read our recent article in Insurance Times on how our software can highlight the dangers lurking in your insurance policies.


How can ThoughtRiver help Brokers



Cyber risk has been described as ‘the risk of the century’ and therefore ‘the single biggest opportunity for the industry’, however organisations are struggling to keep pace. Brokers use information gleaned from ThoughtRiver’s analytics to get closer to clients by helping them evaluate their vulnerabilities.

To download our Cyber Insurance Liabilities Information sheet or to get a demo of the software, please give us a call on 0207 382 8000 or complete the form below:

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