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Leveraging xAI for enhanced surrender risk management in life insurance products
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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
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BERMÚDEZ, Lluís, ANAYA LUQUE, David and BELLES SAMPERA, Jaume. Leveraging xAI for enhanced surrender risk management in life insurance products. European Research on Management and Business Economics. 2025. Vol. 31, num. 3, pags. 1-11. ISSN 2444-8834. [consulted: 6 of October of 2026]. Available at: https://hdl.handle.net/2445/222868