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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.
Your early-bird registration fee is € 150.00 (net) / € 178.50
(incl. VAT, if applicable) for bookings by 7 February 2025. After
this date, the fee will be € 195.00 (net) / € 232.05 (incl. VAT, if
applicable). |
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