An explosion of data
It is no secret that the marine industry is becoming better connected and this, along with the growth in high-performance computing, gives insurers access to more and more accurate data.
Using this information on a real-time basis, insurers can gain a deeper understanding of the risk profile of fleets and vessels, and therefore determine the appropriate policies and prices.
This is exactly what marine insurers need to achieve profitability in an extremely challenging market.
But, can all of this new data be analysed and interpreted in a meaningful and regular way using traditional methods? The simple answer is no.
How risk analysis and pricing is currently done
Underwriters rely on actuaries for accurate pricing models. These are built using statistical models which analyse a small number of static variables.
One or two actuaries will run a small number of rating factors through up to 100 statistical iterations. This will help them segment their portfolio to come up with a new pricing model, or optimise their current model, in the hope of generating more profit.
This generally happens once a year, but sometimes far less, and can take up to six months.
But, would the current process stand up to all of this new dynamic, and often complex, data? The simple answer is it wouldn’t.
The future is machine learning
In comparison to today’s manual and laborious process, underwriters and actuaries could work together using machine learning. They could run historic, static and new behavioural data through tens, if not hundreds of models, and hundreds of thousands of iterations.
Rather than once a year, or less, they could do this on a regular, even daily basis, if they wanted to, at the push of a button.
Find out more about machine learning for marine insurance - what it is, what outcomes it would generate, and why the time to start adopting it is now at the Concirrus’ blog.