Not all claims are reported when a financial operational risk data base is created. The probability of reporting increase with the size of the operational risk loss and approaches one for very big losses. Operational risk losses comes from many different sources and can be expected to follow a...

This paper considers Danish insurance business lines, for which the pricing methodology recently has been dramatically upgraded. A costly affair, but nevertheless the benefits greatly exceed the costs; without a proper pricing mechanism, you are simply not competitive. We show that experience...

We investigate a concept of multivariate pricing, which includes claim history for more than one line of business and is a generalization of the Buuml;hlmann-Straub model. The multivariate credibility model is extended to allow for the age of claims to influence the estimation of future claims....

This paper considers the question of the most appropriate severity distribution estimator for Loss Distribution Analysis (LDA) on operational risk data. We compare the performance of four severity distribution estimators, three well known and one relatively new and assess their suitability for...

Prospective customers of financial and insurance products can be targeted based on the profit the provider expects to earn from them. We present a model for individual expected profit and two alternatives for calculating optimal personalized prices that maximize the expected profit. For one of...

We develop a tailor made semiparametric asymmetric kernel density estimator for the estimation of actuarial loss distributions. The estimator is obtained by transforming the data with the generalized Champernowne distribution initially fitted to the data. Then the density of the transformed data...

Prospective customers of financial and insurance products can be targeted based on the profit the provider expects to earn from them. We present a model for individual expected profit and two alternatives for calculating optimal personalized prices that maximize the expected profit. For one of...

When estimating loss distributions in insurance, large and small losses are usually split because it is difficult to find a simple parametric model that fits all claim sizes. This approach involves determining the threshold level between large and small losses. In this article a unified approach...

We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy tailed data. The multiplicative correction is based on prior knowledge and has a dimension reducing effect at the same time as the original dimension of the estimation problem is retained. Adding...

This paper introduces a multivariate density estimator for truncated and censored data with special emphasis on extreme values based on survival analysis. A local constant density estimator is considered. We extend this estimator by means of tail flattening transformation, dimension reducing...