2026-04-17T22:54:48Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/2228682026-01-22T22:42:52Zcom_2072_1057col_2072_478917col_2072_478919

00925njm 22002777a 4500 dc Bermúdez, Lluís author Anaya Luque, David author Belles Sampera, Jaume author 2025-09-01T10:22:47Z 2025-09-01T10:22:47Z 2025-09-01 2025-09-01T10:22:47Z 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> Assegurances de vida Aprenentatge automàtic Risc (Assegurances) Life insurance Machine learning Risk (Insurance) Leveraging xAI for enhanced surrender risk management in life insurance products