Abstract
We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well.
Highlights
The performance of the insurance sector is undergoing a transformation
The gap of the non-life insurance sector may be the manifestation of the inability of traditional insurance companies to successfully complete the so-called “last mile”: the effective communication to the final users of the importance of covering risks, either because they are not using the right tools or because they can not offer the protection the customers need
We propose applying an accurate and explainable machine learning method, based on Shapley values, to the non-life insurance industry, which can help to turn “black box” unexplainable algorithms into something closer to a white box
Summary
The performance of the insurance sector is undergoing a transformation. While life insurance products are performing well in term of market penetration, non-life products are lagging behind.This may be detrimental to the society, as the aim of the insurance industry is, in its essence, a protective one, which serves as an hedge against the risk of contingent or uncertain losses, generating efficiency.The gap of the non-life insurance sector may be the manifestation of the inability of traditional insurance companies to successfully complete the so-called “last mile”: the effective communication to the final users of the importance of covering risks, either because they are not using the right tools or because they can not offer the protection the customers need. While life insurance products are performing well in term of market penetration, non-life products are lagging behind. This may be detrimental to the society, as the aim of the insurance industry is, in its essence, a protective one, which serves as an hedge against the risk of contingent or uncertain losses, generating efficiency. The gap of the non-life insurance sector may be the manifestation of the inability of traditional insurance companies to successfully complete the so-called “last mile”: the effective communication to the final users of the importance of covering risks, either because they are not using the right tools or because they can not offer the protection the customers need. Technology based insurance (Insurtech), which is based on the application of Artificial Intelligence methods to data retrieved from users’ engagement via smartphones, can close the gap between non-life insurance providers and consumers, thereby improving the protection and resilience of our societies
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