2026-04-13T01:06:20Zhttps://recercat.cat/oai/requestoai:recercat.cat:2445/2228682026-01-22T22:42:52Zcom_2072_1057col_2072_478917col_2072_478919
RECERCAT
author
Bermúdez, Lluís
author
Anaya Luque, David
author
Belles Sampera, Jaume
2025-09-01T10:22:47Z2025-09-01T10:22:47Z2025-09-012025-09-01T10:22:47Z
Explainable Artificial Intelligence (xAI) plays a crucial role in enhancing our understanding of decision-making processes within black-box Machine Learning models. Our objective is to introduce various xAI methodologies, providing risk managers with accessible approaches to model interpretation. To exemplify this, we present a case study focused on mitigating surrender risk in insurance savings products. We begin by using real data from universal life policies to build logistic regression and tree-based models. Using a range of xAI techniques, we gain valuable insight into the inner workings of tree-based models. We then propose a novel supervised clustering approach that integrates Shapley values with a Kohonen neural network (KNN). The process involves three main steps: computing Shapley values from a supervised tree-based model; clustering individuals into homogeneous profiles using an unsupervised KNN; and interpreting these profiles with a supervised decision tree model. Finally, we present several key findings derived from the application of xAI techniques, which ha</span>
cc-by-nc-nd (c) Bermúdez, L. et al., 2025 http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess
Assegurances de vida
Aprenentatge automàtic
Risc (Assegurances)
Life insurance
Machine learning
Risk (Insurance)
Leveraging xAI for enhanced surrender risk management in life insurance products
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion