Deploying machine learning in insurance pricing | pwc blog


Insurers have been investigating the deployment of machine learning techniques in the pricing arena, seeking to exploit the speed at which models can be built and refreshed compared to the use of more traditional generalised linear modelling techniques.

Machine learning techniques offer significant advantages over these traditional models, including the availability of various types of non-linear models which can lead to a wide range of new insights. However, these new models are more difficult to explain to both brokers and to customers (especially when these more sophisticated models suggest significant changes compared to the expiring prices), and there is a degree of resistance to what might be viewed as a black box technology by management and marketing teams.

At the same time, the actual improvements offered by these new models over the traditional models may not be as marked as might be expected given the hype surrounding machine learning.

So how can these twin challenges be overcome; namely, deploying these new pricing models in a way that can be readily understood by all stakeholders, and secondly how to actually get the most out of these new types of model?

Read complete article here:

Deploying machine learning in insurance pricing – The data blog.