Multidimensional Credibility With Time Effects - An Application to Commercial Business Lines

Englund, Martin; Gustafsson, Jim; Nielsen, Jens Perch; Thuring, Fredrik

doi:10.2139/ssrn.923316

Cited by 14 publications

(20 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a growing interest in the literature to extend the standard univariate analysis to multiple count processes (see, e.g., Frees, 2003;Pinquet, 1998;Englund et al 2008;Englund et al, 2009;Abdallah, Boucher, and Cossette, 2016;Bermúdez and Karlis, forthcoming). For instance, a car insurance policy involves in general two types of coverage: third-party liability and (body and vehicle) casualty.…”

Section: A Simple Common Factor Bivariate Extensionmentioning

confidence: 99%

Dynamic Frailty Count Process in Insurance: A Unified Framework for Estimation, Pricing, and Forecasting

Lu¹

2017

J of Risk & Insurance

View full text Add to dashboard Cite

We study count processes in insurance, in which the underlying risk factor is time varying and unobservable. The factor follows an autoregressive gamma process, and the resulting model generalizes the static Poisson‐Gamma model and allows for closed form expression for the posterior Bayes (linear or nonlinear) premium. Moreover, the estimation and forecasting can be conducted within the same framework in a rather efficient way. An example of automobile insurance pricing illustrates the ability of the model to capture the duration dependent, nonlinear impact of past claims on future ones and the improvement of the Bayes pricing method compared to the linear credibility approach.

show abstract

Section: A Simple Common Factor Bivariate Extensionmentioning

confidence: 99%

Dynamic Frailty Count Process in Insurance: A Unified Framework for Estimation, Pricing, and Forecasting

Lu¹

2017

J of Risk & Insurance

View full text Add to dashboard Cite

show abstract

“…Bühlmann & Gisler (2005) p. 185-186. For further details, see also Englund et al (2009) and Thuring et al (2012).…”

Section: Variable Specification and Parameter Estimationmentioning

confidence: 99%

Segmenting and Selecting Cross-sale Prospects using Dynamic Pricing

Thuring¹,

Nielsen²,

Guillén³

et al. 2013

Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems

View full text Add to dashboard Cite

In this paper we consider segmentation of a company's customer data base with respect to the future expected profit, emerging from not yet sold products. We consider a situation where the company is interested in contacting a subset of the customers in the data base to offer additional products. The price at which the products are offered may vary and by utilizing an estimate of the price elasticity of demand, we are able to find closed a form expression of the optimal price as well as the corresponding expected profit, for each customer prospect. We implement the methodology and test it using real data from a major Scandinavian insurance company. We underline that a closed form expression of the optimal price is seldom found in the dynamic pricing literature, suggesting that our pricing formula would be of interest to a broad audience of economists, econometricians, pricing managers and actuaries.

show abstract

“…We use the model from Englund et al (2008) and estimation following Englund et al (2009). We consider insurance customers i 5 1 ,y, I in time periods j 5 1 ,y, J i with insurance products k 0 and k, for convenience we will use the index l A k 0 , k and r A k 0 , k for insurance products in general.…”

Section: The Credibility Model and Estimatormentioning

confidence: 99%

“…Since we consider the two-dimensional case with the specific insurance products k 0 and k, under the stated model assumptions, the multivariate credibility estimator of y i 5 [y ik 0 , y ik ] 0 is (see Englund et al, 2009 andBü hlmann &Gisler 2005, p. 181)…”

Section: The Credibility Model and Estimatormentioning

confidence: 99%

“…Bailey (1950), Jewell (1974) and Gangopadhyay & Gau (2007). This paper uses developments of multivariate credibility found in Englund et al (2008) and Englund et al (2009), both papers model frequency of insurance claims from correlated business lines. Multivariate credibility models are also found in Venter (1985), describing multivariate credibility models in a hierarchical framework, and Jewel (1989), investigating multivariate predictions of first and second order moments in a credibility setting.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A credibility method for profitable cross-selling of insurance products

Thuring¹

2011

Ann. actuar. sci.

Self Cite

View full text Add to dashboard Cite

A method is presented for identifying an expected profitable set of customers, to offer them an additional insurance product, by estimating a customer specific latent risk profile, for the additional product, by using the customer specific available data for an existing insurance product of the specific customer. For the purpose, a multivariate credibility estimator is considered and we investigate the effect of assuming that one (of two) insurance products is inactive (without available claims information) when estimating the latent risk profile. Instead, available customer specific claims information from the active existing insurance product is used to estimate the risk profile and thereafter assess whether or not to include a specific customer in an expected profitable set of customers. The method is tested using a large real data set from a Danish insurance company and it is shown that sets of customers, with up to 36% less claims than a priori expected, are produced as a result of the method. It is therefore argued that the proposed method could be considered, by an insurance company, when cross-selling insurance products to existing customers.

show abstract

Multidimensional Credibility With Time Effects - An Application to Commercial Business Lines

Cited by 14 publications

References 11 publications

Dynamic Frailty Count Process in Insurance: A Unified Framework for Estimation, Pricing, and Forecasting

Dynamic Frailty Count Process in Insurance: A Unified Framework for Estimation, Pricing, and Forecasting

Segmenting and Selecting Cross-sale Prospects using Dynamic Pricing

A credibility method for profitable cross-selling of insurance products

Contact Info

Product

Resources

About