Adopting a new model can seem like a big step. We are able to deploy machine learning models in the cloud for visualisation and communication purposes. We can implement final models both within client based systems, or as independent cloud based solutions.
In traditional GLM models interactions between predictors must be carefully isolated and inserted manually. In contrast, the non-parametric nature of many machine learning algorithms means that all interactions can be included by default. Beyond simply using these models for predictions, we have a suite of tools to analyse the interactions present in those models, which can be used as pointers to refine and improve traditional models.
Applying modern machine learning techniques to traditional personal lines pricing data, we were able to provide predictive models which were more accurate (when measured against an independent test set) than a standard GLM model. Such techniques enable clients to exploit their data to the fullest.
Our consultants have hands-on experience of delivering internal model applications. Using our understanding of Solvency II directives and PRA requirements, we supported a client with the production of various deliverables feeding into their model application.
Several of our consultants were seconded to a large personal lines insurer to give technical support across a number of model development areas important to the client. Our deep expertise allowed us to develop, implement and justify new and market leading approaches to complex technical problems.