Applications of Machine Learning for the Ratemaking in Agricultural Insurances

TL;DR

This paper demonstrates how machine learning techniques, including LASSO, Elastic Net, and Boosting, can improve ratemaking in agricultural insurance by providing more accurate indemnity forecasts and better economic performance than traditional models.

Contribution

It introduces the application of ML methods and Tweedie distribution in agricultural insurance ratemaking, showing improved predictive accuracy and economic outcomes.

Findings

ML outperforms baseline models in goodness-of-fit.

Boosting achieves the best economic performance.

ML reduces data collection costs.

Abstract

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.