There's currently a lot of hype around artificial intelligence (AI) and automated data processing for a number of reasons. But why is that there are relatively few real-world examples of data science teams in insurance applying machine learning? How can AI and machine learning improve certain areas of the insurance process? How to make money and actually create real value for the customer from it? What are the specific real world scenarios where automation can be used?

With mass tort style litigations reaching new highs in 2019, insurance and reinsurance companies should increasingly be thinking about how to capitalise on this emerging opportunity, according to Robert Reville, CEO of insurtech risk modelling and analytics firm Praedicat. Speaking at the Reinsurance Rendezvous event in Monte Carlo, Reville explained that a number of factors had come together to drive the increase in mass litigation activity this year. For example, hedge funds and other sources of capital are now helping to finance litigation in the US, making it possible for the plaintiff's to continue to drive very expensive forms of litigation. What's more, Praedicat believes there is a sense that the jury pool in the US is increasingly suspicious of corporations, and are more willing to accept that they could have been involved in nefarious actions. Finally – and of most relevance to the re/insurance industry – is the decades of accumulated science that is being brought to bear to describe what corporations are selling and doing that could potentially cause bodily or property damage, or environmental damage.