Association Rules for Understanding Policyholder Lapses

Abstract

For automobile insurance, it has long been implied that when a policyholder made at least one claim in the prior year, the subsequent premium is likely to increase. When this happens, the policyholder may seek to switch to another insurance company to possibly avoid paying for a higher premium. In such situations, insurers may be faced with the challenges of policyholder retention by keeping premiums low in the face of competition. In this paper, we seek to find empirical evidence of possible association between policyholder switching after a claim and the associated change in premium. In accomplishing this goal, we employ the method of association rule learning, a data mining technique that has its origins in marketing for analyzing and understanding consumer purchase behavior. We apply this unique technique in two stages. In the first stage, we identify policyholder and vehicle characteristics that affect the size of the claim and resulting change in premium regardless of policy switch. In the second stage, together with policyholder and vehicle characteristics, we identify the association among the size of the claim, the level of premium increase and policy switch. This empirical process is often challenging to insurers because they are unable to observe the new premium for those policyholders who switched. However, we used a 9-year claims data for the entire Singapore automobile insurance market that allowed us to track information before and after the switch. Our results provide evidence of a strong association among the size of the claim, the level of premium increase and policy switch. We attribute this to the possible inefficiency of the insurance market because of the lack of sharing and exchange of claims history among the companies.

Non-life insurance cancellation: a systematic quantitative literature review

“…The publications selected were analysed according to their methodological approach. Eight papers are theoretical (Larsson and Broström 2019;Brophy 2015;Gallo 2014;Van Gelder et al 2018;Guillenet al 2008;Naujoks et al 2017;Rawson et al 2013;Julie Robson 2015) and 18 are empirical; of the latter, 16 are quantitative (Bolancé et al 2016;Brockett et al 2008;de la Llave et al 2019;Felício and Freire 2016;Frank and Lamiraud 2009;Gamble et al 2009;Guillen et al 2003Guillen et al , 2009Guillén et al 2012;Günther et al 2014;Haugen and Moger 2016;Jeong et al 2018;Lin 2010;López-Díaz et al 2017;Paredes 2018;Staudt and Wagner 2018), one is qualitative (Robson and Sekhon 2011) and one uses a mixed methodology (Dominique-Ferreira 2018). This sample indicates that the study of the cancellation of insurance policies is predominantly conducted through empirical quantitative analysis.…”

“…Regarding the methods of data collection for the empirical research, longitudinal analysis predominates, applied in 11 papers (Bolancé et al 2016;Brockett et al 2008;de la Llave et al 2019;Guillen et al 2003Guillen et al , 2009Guillén et al 2012;Günther et al 2014;Haugen and Moger 2016;Jeong et al 2018;Paredes 2018;Staudt and Wagner 2018), over cross-sectional analysis, used in six papers (Frank and Lamiraud 2009;Gamble et al 2009;Lin 2010;Robson and Sekhon 2011;López-Díaz et al 2017), including the paper that used a mixed qualitative and quantitative methodology (Dominique-Ferreira 2018). In the paper by Felício and Freire (2016), both longitudinal and cross-sectional approaches were applied.…”

“…Structural equations were applied in two articles (Felício and Freire 2016;Dominique-Ferreira 2018). Other techniques used in the reviewed documents were probit regression (de la Llave et al 2019), OLS multiple linear regression analyses (Gamble et al 2009), factor analysis (Lin 2010), association rule (Jeong et al 2018) and ROC curves to compare methods to predict lapsing (Bolancé et al 2016).…”

“…Customer attrition rates are pivotal business indicators for the management of insurance companies (López-Díaz et al 2017). Jeong et al (2018) highlight the positive effect of policyholder retention on a company's reputation for building good customer relationships, which could further help attract new customers. De la Llave et al (2019) attribute the negative effects of losing customers both to sales revenue, as a consequence of the high cost of attracting new customers to replace those who left, and to the company's reputation and brand image, considering that churners tend to give negative feedback about the company which may then negatively influence potential customers.…”

“…The relationship between price (in absolute value or considering its variance from the previous period) and policy cancellation was analysed by de la Llave et al (2019), Guillen et al (2003Guillen et al ( , 2008, Jeong et al (2018) and Paredes (2018).…”