Blog

I was delighted to present the first masterclass in the series as part of the short-term insurance practicing initiative of the Association of South African Black Actuarial Professionals and Old Mutual Insure. The title was “Reserving with the Cape Cod Method” and the attached slides cover everything from the basics all the way up to advanced methods of setting the parameters using machine learning. More materials can be found at the GitHub link on the title slide.

I was delighted to speak at the Actuarial Society of South Africa (ASSA)‘s annual short term insurance seminar, on Discrimination Free Insurance Pricing and our new work on multi-task networks. My slides are below.

Thanks so much to Mathias Lindholm, Andreas Tsanakas and Mario Wüthrich for this collaboration!

We are excited to post a new paper, covering feature selection in our explainable deep learning architecture, the LocalGLMnet. Deep learning models are often criticized for not being explainable nor allowing for variable selection. Here, we show how group LASSO regularization can be implemented within the LocalGLMnet architecture so that we receive feature sparsity for variable selection. On several examples, we find that the proposed methods can identify less important variables successfully, even on smaller datasets! The figure below shows output from the model fit to the famous bike sharing dataset, where randomly permuted variables receive zero importance after regularization.

The paper can be found here:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3927187

Excited to post a new paper with Mario Wüthrich on a local GLM model parametrized using a neural network:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3892015

Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models. The disadvantage of deep learning models is that their solutions are difficult to interpret and explain, and variable selection is not easily possible because deep learning models solve feature engineering and variable selection internally in a nontransparent way. Inspired by the appealing structure of generalized linear models, we propose a new network architecture that shares similar features as generalized linear models, but provides superior predictive power benefiting from the art of representation learning. This new architecture allows for variable selection of tabular data and for interpretation of the calibrated deep learning model, in fact, our approach provides an additive decomposition in the spirit of Shapley values and integrated gradients.

Very excited to give a keynote talk at the European Actuarial Academy GmbH‘s conference on Data Science and Data Ethics later today. The theme of my talk is AI in Actuarial Science, Two Years On. Slides are attached to this post.

Excited to post a new paper on safely incorporating deep learning models into the actuarial toolkit. The paper covers several important aspects of deep learning models have not yet been studied in detail in the actuarial literature: the effect of hyperparameter choice on the accuracy and stability of network predictions, methods for producing uncertainty estimates and the design of deep learning models for explainability.

This paper was written under the research grant program of the AFIR-ERM section of the International Actuarial Association, to whom I am very grateful.

Here is an exhibit from the paper showing confidence bands derived using quantile regression:

Here is another exhibit, showing the impact of different hyperparameter choices:

Please read the full paper here if of interest:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3857693