English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Applications of Machine Learning for the Ratemaking in Agricultural Insurances