Risk Spatial founder Nick Hassam says Open Modelling and Big Data can disrupt the catastrophe modelling process – but who will implement it?
Despite an overwhelming number of catastrophe risk professionals anticipating disruption to the hazard modelling process from emerging technologies, they are uncertain if their own organisations can implement the change.
This was a key outcome from a survey of senior catastrophe risk professionals across North America, Europe and Asia, performed recently to gauge opinion on advances in capabilities such as Open Modelling and Big Data in the catastrophe modelling world, as part of a presentation to the 15th Catastrophe Insurance Conference in Asia.
Confidence is high that the field of hazard modelling for the insurance industry can continue to evolve with emerging technologies in the short to medium term. But what is less certain is the ability for the insurance industry to implement them.
Time to disruption
The survey suggested that 13% of respondents believe wholesale disruption to the catastrophe modelling process could occur within the next two years, with the clear majority believing that the following two to five years would be telling. Less than 20% felt changes are unlikely to occur until the longer term, while only seven percent of respondents believed there would be any significant disruption.
With the explosion in interest in insurance technology across the broader industry, it is not surprising those in the CAT modelling world are optimistic about the potential for advancement in the field. Our natural platform is the intersection of technology and insurance, and the goal of using emerging capabilities to further enhance the industry is something we have always sought.
Sources of Disruption
So where will the disruption come from? The survey identified the usual suspects, with the concepts of Open Modelling, Big Data and Artificial Intelligence proving most popular.
The move towards Open Modelling, both by commercial vendors and in-house proprietary systems on platforms such as OASIS is increasing dramatically. As the original ‘InsurTech’, catastrophe modelling has already implemented technologies, such as Big Data, that are only now emerging in the broader industry. Artificial Intelligence has a natural fit with perils such as cyber and terrorism, and the proliferation of self-aware infrastructure via IoT brings exciting ideas around exposure data collection and real-time monitoring.
Obstacles to Disruption
While hazard modellers have certainty in the technologies that will enable change, their confidence in who can enable this transformation is not as high. In response to a question on the potential obstacles to evolution, respondents suggested that those organisations already present in the industry lack the ability to adapt to change. Reluctance of incumbents to embrace emerging technology was cited as the most significant obstacle to wide scale disruption.
It is intriguing that the market’s perception of the biggest potential obstacle to progression are the very companies within which insurance professionals work. What this says, is that the principle challenges faced by catastrophe modelling professionals are rarely technical; it is more likely they are slowed down by the pace of broader organisational change, and the current approach to widespread advancement of the insurance process.
Putting aside the pessimism of organisational challenges, as ground-breakers in technological advancement in the insurance industry, CAT modellers are well placed to manage evolving disruption. We were developed specifically to implement technological change; this is the basis on which our science originated.
However, a word of caution in thinking CAT modelling is ahead of the game; if we as a field become complacent about our role in the insurance business process, we become at risk to potential disruptions in the broader industry. End-to-end solutions such as those provided by Distributed Ledger and Smart Contract technologies have the potential to mechanise the insurance transaction, and we need to be ready to embrace the automation that follows. A