We present the one-year claims development result (CDR) in the paid-incurred chain (PIC) reserving model. The PIC reserving model presented in Merz-Wuthrich is a Bayesian stochastic claims reserving model that considers simultaneously claims payments and incurred losses information and allows...

The paid-incurred chain (PIC) reserving method is a claims reserving method in general insurance that allows to combine claims payments and incurred losses information in a mathematical consistent way. The main criticism on the original Bayesian log-normal PIC model presented in Merz-Wuthrich...

The aim of this project is to develop a stochastic simulation machine that generates individual claims histories of non-life insurance claims. This simulation machine is based on neural networks to incorporate individual claims feature information. We provide a fully calibrated stochastic...

With the emergence of telematics car driving data, insurance companies start to boost classical actuarial regression models for claim frequency prediction. In this paper, we propose two data-driven neural network approaches that process telematics car driving data to construct driving behavior...

The time-series nature of mortality rates lends itself to processing through neural networks that are specialized to deal with sequential data, such as recurrent and convolutional networks. Although appealing intuitively, a naive implementation of these networks does not lead to enhanced...

The aim of this paper is to operationalize claims reserving based on general insurance individual claims data. We design a modeling architecture that is based on six different neural networks. Each network is a separate module that serves a certain modeling purpose. We apply our architecture to...

A simple formula for non-discriminatory insurance pricing is introduced. This formula is based on the assumption that certain individual (discriminatory) policyholder information is not allowed to be used for insurance pricing. The suggested procedure can be summarized as follows: First, we...

Various stochastic models have been proposed to estimate mortality rates. In this paper we illustrate how machine learning techniques allow us to analyze the quality of such mortality models. In addition, we present how these techniques can be used for differentiating the different causes of...

The main idea of this paper is to embed a classical actuarial regression model into a neural network architecture. This nesting allows us to learn model structure beyond the classical actuarial regression model if we use as starting point of the neural network calibration exactly the classical...

The Lee-Carter model is a basic approach to forecasting mortality rates of a single population. Although extensions of the Lee-Carter model to forecasting rates for multiple populations have recently been proposed, the structure of these extended models is hard to justify and the models are...