Insuring commercial liability amid technological and scientific change is not without its challenges. Science-based risk, for example, can lead to liability catastrophe, as seen with asbestos.

Historically, the insurance industry has not had the ability to manage this risk.

But a new report from Lloyd’s and “liability catastrophe” modelling company Praedicat sheds light on the power of Big Data analytics to overcome this problem.

Claims data typically cannot help predict the next liability catastrophe, which presents a challenge for actuarial modelling.

When a liability catastrophe occurs, the products or business practices involved are usually discontinued and the companies that sold them may cease to exist. As a result, each new liability catastrophe is likely to happen in a different industry and in a different way.

Liability catastrophes – where large-scale adverse effects of a product or substance result in an accumulation of claims across portfolios – can generate significant losses for insurers.

The report says the explosion of external data describing bodily injury and property damage risks in recent years could help solve this problem.

Praedicat uses Big Data analytics to identify and investigate early signs of potential liability catastrophes and to improve insurers’ understanding of liability risk.

New technology makes it possible to mine data from scientific research associated with potential liability risks, and estimate the probability that exposure to a substance or product will cause a particular form of injury.

This information is then overlaid on an insurer’s portfolio to identify potential accumulations of liability risk. The analysis can be used to develop quantitative estimates of mass litigation, allowing a liability catastrophe model to be built.

The report says Big Data innovations can create more robust liability risk management for insurers.

“Further developments in liability catastrophe modelling using Big Data could offer insurers a means of managing liability accumulations while also identifying opportunities to increase exposure to certain risks where the accumulation is consistent with their risk appetite.”

With science-based liability cat modelling, insurers may profitably incorporate the precautionary principle into their businesses.

Big Data makes “identification, contextualisation, projection and quantification” of emerging risks possible. With this information, casualty insurers can create the incentives needed for their clients to take appropriate precautionary actions.

“With Big Data about science informing insurer decision-making, liability insurers can help make the world cleaner, healthier and safer for the public and their investors alike,” the Lloyd’s/Praedicat report says.