A logistic regression approach to estimating customer profit loss due to lapses in insurance

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.Policy cancellation, customer loyalty, profit loss, customer behavior.

Semi-autonomous vehicles: Usage-based data evidences of what could be expected from eliminating speed limit violations

The use of advanced driver assistance systems and the transition towards semi-autonomous vehicles are expected to contribute to a lower frequency of motor accidents and to have a significant impact for the automobile insurance industry, as rating methods must be revised to ensure that risks are correctly measured. Telematics information and usage-based insurance research are analyzed to identify the effect of driving patterns on the risk of accident. This is used as a starting point for addressing risk quantification and safety for vehicles that can control speed. The effect of excess speed on the risk of accidents is estimated with a real telematics data set. Scenarios for a reduction of speed limit violations and the consequent decrease in the expected number of accident claims are shown. If excess speed could be eliminated, then the expected number of accident claims could be reduced to half of its initial value, applying the average conditions of the data used in this study. As a consequence, insurance premiums also diminish

Computing Practices in Statistics I and Statistics II Business and Management Degree Faculty of Economy and Business University of Barcelona

List of computing practices that we develop in different courses of Statistics in the Business and Management degree. It covers descriptive analysis using MicroSoftExcel, computing probabilities using MicroSoftExcel, computing confidence Intervals using MicroSoftExcel and carrying out statistics test hypothesis using Gretl

Teamwork, peer assessment and double revision of assignments as strategies for teaching quantitative research methods in Sociology

Sociology and statistics are part of the study programme in many bachelor's degrees and masters, some of them not focused primarily on quantitative research methods. This is the case of specific business management programmes, where quantitative research methods in sociology appear marginally in a single subject. In such subjects, we think it is necessary to introduce innovative teaching strategies which could motivate and help students in their learning process. METHOD. Here we describe our experience and results after implementing teamwork, peer assessment and double revision of assignments as strategies for teaching quantitative methods applied to sociological research in the Sport Business Management Master (SBMM) of the University of Barcelona. RESULTS. We observed that the grades for teamwork activities are positively correlated among themselves and that the individual grades on peer-assessment activities help to distinguish between higher and lower performing students, because they are positively correlated with final exam grades. DISCUSSION. These strategies are always well-received by students and help them in their learning process. We conclude that peer assessment can be easily combined with teamwork and used in our context as an individual activity instead of a final exam

Optimal personalized treatment rules for marketing interventions: A review of methods, a new proposal, and an insurance case study

In many important settings, subjects can show signi cant heterogeneity in response to a stimulus or treatment". For instance, a treatment that works for the overall population might be highly ine ective, or even harmful, for a subgroup of subjects with speci c characteristics. Similarly, a new treatment may not be better than an existing treatment in the overall population, but there is likely a subgroup of subjects who would bene t from it. The notion that "one size may not fit all" is becoming increasingly recognized in a wide variety of elds, ranging from economics to medicine. This has drawn signi cant attention to personalize the choice of treatment, so it is optimal for each individual. An optimal personalized treatment is the one that maximizes the probability of a desirable outcome. We call the task of learning the optimal personalized treatment "personalized treatment learning". From the statistical learning perspective, this problem imposes some challenges, primarily because the optimal treatment is unknown on a given training set. A number of statistical methods have been proposed recently to tackle this problem

Los hábitos de conducción al volante según el género en los seguros Pay-As-You-Drive o Usage based

La aplicación de la Directiva de Género ha supuesto para las entidades aseguradoras la prohibición del uso de la variable género como factor usado en la tarificación. La oferta de nuevos productos aseguradores basados en el uso del vehículo y la exposición al riesgo permite analizar de forma novedosa el efecto que determinados hábitos de conducción y pautas al volante pueden tener en la probabilidad de sufrir un siniestro. Los resultados obtenidos en esta investigación ponen de manifiesto como los factores de riesgo basados en el uso son diferentes en función del género del conductor asegurado

El seguro basado en el uso

El seguro del automóvil Pay-As-You-Drive (PAYD), comercializado en varios países, se ha convertido en la referencia básica de un nuevo producto asegurador: el seguro basado en el uso. Este trabajo constata, a través de una revisión bibliográfica y un estudio empírico, la relación entre el uso y la siniestralidad, y reflexiona sobre el diseño de productos basados en el uso más allá del seguro del automóvil, dentro del ramo de seguros generales

Do young insured drivers slow down after suffering an accident?

Being involved in an accident may modify the behavior of an insured at the wheel due to their having an aggravated perception of risk. Here, we analyze how the behavior of young drivers is modified after an accident by comparing percentage distances driven above posted speed limits before and after the event. The possibilities afforded by telematics, in terms of gathering information about such variables as speeding, undoubtedly constitute an important step forward in quantifying effects that hitherto have gone unanalyzed in the field of road safety and car insurance. Our results show a greater reduction in the speeding of young drivers that suffer severe bodily injuries, especially in the case of men and, particularly, among those that committed more frequent speed violations prior to the accident

Using GPS data to analyze the distance traveled to the first accident at fault in pay-as-you-drive insurance

In this paper we employ survival analysis methods to analyse the impact of driving patterns on distance travelled before a first claim is made by young drivers underwriting a pay-as-you-drive insurance scheme. An empirical application is presented in which we analyse real data collected by a GPS system from a leading Spanish insurer. We show that men have riskier driving patterns than women and, moreover, that there are gender differences in the impact driving patterns have on the risk of being involved in an accident. The implications of these results are discussed in terms of the 'no-gender' discrimination regulation

Testing extreme value copulas to estimate the quantile

We generalize the test proposed by Kojadinovic, Segers and Yan which is used for testing whether the data belongs to the family of extreme value copulas. We prove that the generalized test can be applied whatever the alternative hypothesis. We also study the effect of using different extreme value copulas in the context of risk estimation. To measure the risk we use a quantile. Our results have been motivated by a bivariate sample of losses from a real database of auto insurance claims