A spatial machine learning model for analysing customers’ lapse behaviour in life insurance

Abstract

Spatial analysis ranges from simple univariate descriptive statistics to complex multivariate analyses and is typically used to investigate spatial patterns or to identify spatially linked consumer behaviours in insurance. This paper investigates if the incorporation of publicly available spatially linked demographic census data at population level is useful in modelling customers’ lapse behaviour (i.e. stopping payment of premiums) in life insurance policies, based on data provided by an insurance company in Ireland. From the insurance company’s perspective, identifying and assessing such lapsing risks in advance permit engagement to prevent such incidents, saving money by re-evaluating customer acquisition channels and improving capital reserve calculation and preparation. Incorporating spatial analysis in lapse modelling is expected to improve lapse prediction. Therefore, a hybrid approach to lapse prediction is proposed – spatial clustering using census data is used to reveal the underlying spatial structure of customers of the Irish life insurer, in conjunction with traditional statistical models for lapse prediction based on the company data. The primary contribution of this work is to consider the spatial characteristics of customers for life insurance lapse behaviour, via the integration of reliable government provided census demographics, which has not been considered previously in actuarial literature. Company decision-makers can use the insights gleaned from this analysis to identify customer subsets to target with personalized promotions to reduce lapse rates, and to reduce overall company risk.

Exploring temporal varying demographic and economic disparities in COVID-19 infections in four U.S. areas: based on OLS, GWR, and random forest models

“…However, when the number of observations is small, the RF model may not be better than OLS even. We encourage future researchers to combine the GWR and RF, introducing the spatial analysis into machine learning (Al-Abadi & Shahid, 2016 ; Hu et al, 2021 ).…”

Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

“…Over the last decade, machine learning techniques have won a lot of attention in the area of insurance analytics (Denuit et al 2019a(Denuit et al , 2019bQuan and Valdez 2018). Machine learning methods are applied in the context of ratemaking (Dalkilic et al 2009;Huang and Meng 2019;Lowe and Pryor 1996;Pelessoni and Picech 1998;Richman 2018), fraud detection (Li et al 2018;Wang and Xu 2018), extreme value theory (Velthoen et al 2021), forecasting (Perla et al 2020), and in the explanation of the lapse behavior of customers (Guelman et al 2012;Hu et al 2020;Staudt and Wagner 2020), among others. While such models are used to select relevant risk factors and automate the creation of categories for continuous variables (Dougherty et al 1995;Henckaerts et al 2018), full-pricing applications are scarce, see, e.g., Guelman (2012) and Henckaerts et al (2020).…”

IoT-based Route Recommendation for an Intelligent Waste Management System

“…AIbased techniques such as Artificial Neural Network, multilayer perception, Adaptive Neuro-Fuzzy Inference System, and Evolutionary Algorithms have been implemented to address air pollution and emissions reduction [18]- [20]. Moreover, different technologies, such as cloud and edge computing [21]- [25], are also embedded in today's urban infrastructures [26]- [29]. These technologies are used to store and process data from almost anywhere.…”