Abstract
The application of machine learning to insurance risk prediction requires learning from sensitive data. This raises multiple ethical and legal issues. One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption. Our objective with this work is to assess the impact of such privacy preservation techniques in the accuracy of ML models. We instantiate the problem in three general, but plausible Use Cases involving the prediction of insurance claims within a 1-year horizon. Our preliminary experiments suggest that discretization and encryption have negligible impact in the accuracy of ML models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The application of machine learning to insurance risk prediction requires learning from sensitive data. This raises multiple ethical and legal issues. One of the most relevant ones is privacy. However, privacy-preserving methods can potentially hinder the predictive potential of machine learning models. In this paper, we present preliminary experiments with life insurance data using two privacy-preserving techniques: discretization and encryption. Our objective with this work is to assess the impact of such privacy preservation techniques in the accuracy of ML models. We instantiate the problem in three general, but plausible Use Cases involving the prediction of insurance claims within a 1-year horizon. Our preliminary experiments suggest that discretization and encryption have negligible impact in the accuracy of ML models.