In the face of the ever-increasing competition and mounting regulatory pressure, actuarial precision and accuracy shape the art of setting the price in the non-life insurance sector. Generalized Linear Models (GLM) are the standard pricing method of non-life insurance products, leading to a multiplicative tariff that is immediately interpretable and operationally efficient. In recent years, the advent of Machine Learning has been termed the next frontier of innovation and productivity, focusing on prediction performance and capturing the inherent non-linearity of the data. However, there is a need to associate these complex models with interpretability techniques.
We introduce an Explainable Boosting Machine (EBM) model that combines both intrinsically interpretable characteristics and high prediction performance. This approach is described as a glass-box model and relies on the use of a Generalized Additive Model (GAM). In this web session, rather than explaining Machine Learning models, we aim to build models that are intrinsically interpretable.
Firstly, we recall the parametric structure of the GLM model as well as the non-parametric structure of Machine Learning models. Some general principles of Machine Learning methods are also presented such as model aggregation.
Secondly, we focus on the GAM model and its declinations. We present its semi-parametric structure and the smoothing-learning paradigm within the shape functions. The EBM model is then introduced as an example of GAM combining Machine Learning functions.
The third part gives a general overview of interpretability techniques and aims to position the EBM interpretability among them.
Lastly, we provide an application of EBM using a Jupyter notebook designed around a P&C actuarial use case.
