  This paper evaluates Machine Learning (ML) in establishing ratemaking for new
insurance schemes. To make the evaluation feasible, we established expected
indemnities as premiums. Then, we use ML to forecast indemnities using a
minimum set of variables. The analysis simulates the introduction of an income
insurance scheme, the so-called Income Stabilization Tool (IST), in Italy as a
case study using farm-level data from the FADN from 2008-2018. We predicted the
expected IST indemnities using three ML tools, LASSO, Elastic Net, and
Boosting, that perform variable selection, comparing with the Generalized
Linear Model (baseline) usually adopted in insurance investigations.
Furthermore, Tweedie distribution is implemented to consider the peculiarity
shape of the indemnities function, characterized by zero-inflated, no-negative
value, and asymmetric fat-tail. The robustness of the results was evaluated by
comparing the econometric and economic performance of the models. Specifically,
ML has obtained the best goodness-of-fit than baseline, using a small and
stable selection of regressors and significantly reducing the gathering cost of
information. However, Boosting enabled it to obtain the best economic
performance, balancing the most and most minor risky subjects optimally and
achieving good economic sustainability. These findings suggest how machine
learning can be successfully applied in agricultural insurance.This study
represents one of the first to use ML and Tweedie distribution in agricultural
insurance, demonstrating its potential to overcome multiple issues.
