Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss models specialise in capturing aspects of data. This is recognised practice the sense results from are often considered, and sometimes combined. For instance, actuaries may take weighted average prediction outcomes various models, based subjective assessments...

In this paper we analyse insurance data using Artificial Neural Networks (ANN)[1]. In particular, we use ANN for the problem of Loss Reserving. Loss reserving is the practice of estimating the future payments for the claims which have occurred on an insurance portfolio. A difficulty in forecasting future payments is that the time series of payments often depends on influences that are not obser...

Abstract The sensitivity of loss reserving techniques to outliers in the data or deviations from model assumptions is a well known challenge. It has been shown that popular chain-ladder approach at significant risk such aberrant observations reserve estimates can be significantly shifted presence even one outlier. As consequence technique non-robust. In this paper we investigate reserves and me...

This paper demonstrates the Vickrey auction has a Nash equilibrium in the core. Generally, the dominant-strategy-equilibrium outcome of the Vickrey auction is not located in the core. However, a particular profile of truncation strategies is a Nash equilibrium, and it implements an outcome in the bidderoptimal core. The specified strategy profile is identical to that in core-selecting auctions ...

Motivated by many industrial applications, we study a basic discrete-time model of reserving capacity (equivalently, quoting lead times) in a make-to-order setting for customers belonging to two diierent classes. Demands in each period are stochastic. Diierent customer classes penalize us with diierent margins for the lead times quoted to them. We derive the optimal policy for reserving capacit...