Multivariate credibility with application to cross-selling financial services products

In this thesis, methods that are capable of improving the revenue and profitability of a financial services company are presented. Of particular interest is the use of customer specific information for pricing insurance products and segmenting a customer population based on the expected profitability of the customers. A prerequisite is the possibility for customers to have many different financial services products from the same provider. The thesis presents multivariate credibility models for how customer specific information from one (or many) financial services products is related to customer specific information from another financial services product. The models are foremost applied to the context of cross-selling (selling additional products to existing customers) where customer specific information from the offered cross-sale product is not available before the sale. As products are related, it is reasonable to use an appropriate (credible) amount of customer specific information from another product (or products), for estimating the profitability expected to emerge from the offered cross-sale product. In four separate but related articles, it is shown that having appropriate models for pricing and customer segmentation is of great importance for a financial services company aiming at running a profitable and growing business

Optimal customer customer selection for cross-selling of financial services products

A new methodology, for optimal customer selection in cross-selling of financial services products, such as mortgage loans and non life insurance contracts, is presented. The optimal cross-sales selection of prospects is such that the expected profit is maximized, while at the same time the risk of suffering future losses is minimized. Expected profit maximization and mean–variance optimization are considered as alternative optimality criteria. In order to solve these optimality problems a stochastic model of the profit, expected to emerge from a single cross-sales prospect and from a selection of prospects, is developed. The related probability distributions of the profit are derived, both for small and large portfolio sizes and in the latter case, asymptotic normality is established. The proposed, profit optimization methodology is thoroughly tested, based on a real data set from a large Swedish insurance company and is shown to achieve considerable profit gains, compared to traditional cross-selling methods, which use only the estimated sales probabilities

Bayesian modeling of networks in complex business intelligence problems

Complex network data problems are increasingly common in many fields of application. Our motivation is drawn from strategic marketing studies monitoring customer choices of specific products, along with co-subscription networks encoding multiple purchasing behavior. Data are available for several agencies within the same insurance company, and our goal is to efficiently exploit co-subscription networks to inform targeted advertising of cross-sell strategies to currently mono-product customers. We address this goal by developing a Bayesian hierarchical model, which clusters agencies according to common mono-product customer choices and co-subscription networks. Within each cluster, we efficiently model customer behavior via a cluster-dependent mixture of latent eigenmodels. This formulation provides key information on mono-product customer choices and multiple purchasing behavior within each cluster, informing targeted cross-sell strategies. We develop simple algorithms for tractable inference, and assess performance in simulations and an application to business intelligence

The Cost of Legal Restrictions on Experience Rating

We investigate the cost of legal restrictions on experience rating in auto and home insurance. The cost is an opportunity cost as experience rating can mitigate the problems associated with unobserved heterogeneity in claim risk, including mispriced coverage and resulting demand distortions. We assess this cost through a counterfactual analysis in which we explore how risk predictions, premiums, and demand in home insurance and two lines of auto insurance would respond to unrestricted multiline experience rating. Using claims data from a large sample of households, we first estimate the variance-covariance matrix of unobserved heterogeneity in claim risk. We then show that conditioning on claims experience leads to material refinements of predicted claim rates. Lastly, we assess how the households’ demand for coverage would respond to multiline experience rating. We find that the demand response would be large

Asymmetric information, self-selection and pricing of insurance contracts: the simple no-claims case

This paper presents an optional bonus-malus contract based on a pri-ori risk classification of the underlying insurance contract. By inducing self-selection, the purchase of the bonus-malus contract can be used as a screening device. This gives an even better pricing performance than both an experience rating scheme and a classical no-claims bonus system. An application to the Danish automobile insurance market is considered

How to improve Banco Santander Brasil business model by analyzing opportunities of Cross Sell

CEM

Empirical Findings on Motor Insurance Pricing in Germany, Austria, and Switzerland

This paper focuses on recent developments in motor insurance pricing in Germany, Austria and Switzerland. Through the analysis of responses to a recent comprehensive survey of industry representatives, we examine the various premium components and the processes involved in premium adaptation. New findings on the use of different tariff criteria, on the tools used for market-based and customer-specific pricing, and on the information considered for customer valuation are reported. We also address the integration of the insurance sales staff in the pricing process. With regard to premium adjustments and the introduction of new tariffs, we examine the frequency, time required and costs incurred. With this paper, we contribute to a strand of literature where little academic research has been done so far. In addition, our results entail managerial implications for improving industry practices in insurance pricing

Cuantificación del riesgo para la tarificación en seguros de automóvil

El cálculo del precio del seguro del automóvil se centra en identificar los factores que determinan una mayor o menor siniestralidad, sin embargo, una concepción integral del riesgo tendrá en cuenta también la posibilidad de que el asegurado no renueve su póliza. Esta visión holística se basa en la concepción exigida en los requerimientos de capital de solvencia cuyo enfoque contempla riesgo de suscripción y de caída de cartera. En este trabajo mostramos una forma de cuantificar el riesgo que aporta cada asegurado con la finalidad de que se pueda identificar su contribución al riesgo total de la cartera. Los resultados obtenidos se ilustran con una muestra de asegurados del mercado español y muestran la importancia de la elección de los factores de tarificación, la medida de riesgo y el modelo de comportamiento aleatorio para las pérdidas

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

Recruiting undergraduate students in South Africa - towards a relationship orientation

Includes bibliographical references