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Understanding and predicting lapses in mortgage life insurance using a machine learning approach
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Resumo(s)
Mortgage life insurance (MLI) offers lucrative opportunities for insurers in Portugal but retaining
customers has become challenging amid regulatory changes and fierce competition. After 2009, the
market has been reshaped by new competitors with aggressive low premium strategies, posing
difficulties for conventional insurers and banks to retain MLI customers. Increasing policy cancellations
have become a pressing concern for these established financial institutions.
To address this complex and adverse context, this study has tried to develop a predictive model to
identify MLI policies prone to lapse and unravel the underlying factors driving this behavior. Using a
powerful combination of data from an insurance company and its affiliated bank, the capabilities of
four different machine learning (ML) models were explored: Logistic Regression, Random Forest,
Neural Networks and XGBoost, with the latter showing the most consistent results. Although the
model performs reasonably well, the difficulty in balancing model complexity and generalizability and
optimizing Precision and Recall reveals room for improvement.
Two novel approaches were introduced by narrowing the focus of the study to one specific insurance
protection product, the MLI, and by integrating bank data, to capture multidimensional drivers of lapse
behavior, and emphasizing the value of a holistic perspective.
Applying SHAP enhanced the interpretability of XGBoost by identifying and explaining the most
influential features affecting the predictive model. Notably, the value of external data is underscored
as the top four features originated from the bank data.
From the insurance company's point of view, this study introduces advanced ML techniques to improve
the accuracy of policy lapse prediction, allowing the company to identify and target customers at risk
of lapse proactively.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for Marketing
Palavras-chave
Mortgage life insurance Lapse risk Machine learning External Data Sources SDG 3 - Good health and well-being SDG 4 - Quality education SDG 9 - Industry, innovation and infrastructure SDG 12 - Responsible production and consumption SDG 13 - Climate action