A Logistic Regression Approach to Estimating Customer Profit Loss Due to Lapses in Insurance

Abstract

This article focuses on business risk management in the insurance industry. A methodology for estimating the profit loss caused by each customer in the portfolio due to policy cancellation is proposed. Using data from a European insurance company, customer behaviour over time is analyzed in order to estimate the probability of policy cancelation and the resulting potential profit loss due to cancellation. Customers may have up to two different lines of business contracts: motor insurance and other diverse insurance (such as, home contents, life or accident insurance). Implications for understanding customer cancellation behaviour as the core of business risk management are outlined.

Logistic regression in modeling and assessment of transport services

“…This value falls within the range [0, 1] and is defined as follows [22], if: (13) it is assumed that an event has occurred (ŷ = 1). In the opposite situation, whenπ (x) ≤ π 0 (14) it is assumed that an event has not occurred (ŷ = 0). The notions of sensitivity and specificity are related to the cut-off point [23].…”

“…The answer to the limitations formulated for linear models are non-linear models [12,13]. Due to their versatility and greater freedom in the selection of variables, they are used in many sectors [14,15], including transport [16,17]. Therefore, they have also become a tool used in this publication, which aimed primarily to indicate the possibility to mathematically analyze selected elements of the company's activity affecting the quality of services provided.…”

Price and Profit Optimization for Financial Services

“…Schweidel et al (2011) observe the same phenomenon in data from a multi-line insurance provider in Denmark, which took into account historical information and anticipated lapses. Guillen et al (2012) find future profit prospects in a similar context to be very informative (see also Guillen et al 2011;Guelman et al 2012). Kaishev et al (2013) present a profit model specifically designed for products for which the profit consists of a stochastic income at the point of sale, minus the cost of contacting a specific customer and the stochastic cost generated by the customer's actions.…”

A survey of personalized treatment models for pricing strategies in insurance

“…In this context, predictive purchasing models can provide some orientation to the company as to which action should be adopted in each situation, but the particular nature of the insurance business has yet to be properly examined and discussed in the marketing literature. A number of recent papers have focused primarily on the reactions of those holding insurance to price changes, but none of them presents a comprehensive framework for addressing the problem of pricing, renewal and cross-selling (Donnelly et al, 2013;Kaishev et al, 2013;Guillén et al, 2011Guillén et al, , 2012Thuring et al, 2012).…”

“…In a similar context, Kaishev et al (2013) analyze the magnitude of that loss and report that it might be attributable to just a few policy holders in the portfolio. Finally, Guillén et al (2011) identify policy holders that were negatively impacted by a retention program, i.e., they present a higher probability of canceling their policy after being targeted by a retention program. In all these cases, the insurance company could have avoided the loss suffered if they had correctly selected the customers to be contacted for a marketing action.…”