Dependence modeling of multivariate longitudinal hybrid insurance data with dropout

• Published in: Expert systems with applications Vol. 185; p. 115552
• Main Authors: Frees, Edward W., Bolancé, Catalina, Guillen, Montserrat, Valdez, Emiliano A.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.12.2021; Elsevier BV
• Subjects: Brand loyalty; Communications systems; Copula; Customers; Datasets; Generalized method of moments; Insurance; Insurance industry; Insurance lapsation; Joint longitudinal and time-to-event; Method of moments; Multivariate analysis; Tweedie distribution; Copula; Tweedie distribution; Joint longitudinal and time-to-event; Insurance lapsation; Generalized method of moments
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.115552

Financial services industries, such as insurance, increasingly use data from their broad cross-section of customers and follow these customers over time. In other areas such as medicine, engineering, and communication systems, it is well known that following subjects over time may result in biased data, for example, the so-called ”dropout effect”. This paper introduces techniques to address dropout commonly encountered in the insurance domain. Specifically, in the insurance context, multivariate claims outcomes may be related to a customer’s dropout or decision to lapse a policy. Insurance claims outcomes are also naturally a hybrid with both discrete and continuous components, which adds complexity to model calibration. Decision makers in the insurance industry will find our work provides helpful guidance in integrating customer loyalty, especially with bundled coverages. This paper introduces a generalized method of moments technique to estimate dependence parameters where associations are represented using copulas. This is especially useful for large data sets. The paper describes how the joint model provides new information that insurers can use to better manage their portfolios of risks. An application to a Spanish insurer data set is presented. •The cause–effect pattern in customer lapses and prices is usually bidirectional.•A method to estimate the association between price and lapsing is presented.•The approach is suitable for large longitudinal data sets and multivariate processes.•An insurance case study shows the implications for assessing interrelated risks.