22 citations · 82 across the 10 of their papers we have counts for
10 papers
Machine Learning with High-Cardinality Categorical Features in Actuarial Applications
Benjamin Avanzi, Greg Taylor, Melantha Wang +1
High-cardinality categorical features are pervasive in actuarial data (e.g. occupation in commercial property insurance). Standard categorical encoding methods like one-hot encodin…
Log normal claim models with common shocks
Greg Taylor
This paper is concerned with modelling multiple claim arrays that are subject to one or more common shocks. It uses a structure that involves very general forms both idiosyncratic…
Detection and treatment of outliers for multivariate robust loss reserving
Benjamin Avanzi, Mark Lavender, Greg Taylor +1
Traditional techniques for calculating outstanding claim liabilities such as the chain ladder are notoriously at risk of being distorted by outliers in past claims data. Unfortunat…
On the impact of outliers in loss reserving
Benjamin Avanzi, Mark Lavender, Greg Taylor +1
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 the popular chain-ladder…
Auto-balanced common shock claim models
Greg Taylor, Phuong Anh Vu
The paper is concerned with common shock models of claim triangles. These are usually constructed as a linear combinations of shock components and idiosyncratic components. Previou…
SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator
Benjamin Avanzi, Gregory Clive Taylor, Melantha Wang
In this paper, we first introduce a simulator of cases estimates of incurred losses, called `SPLICE` (Synthetic Paid Loss and Incurred Cost Experience). In three modules, case esti…
Stochastic loss reserving with mixture density neural networks
Muhammed Taher Al-Mudafer, Benjamin Avanzi, Greg Taylor +1
Neural networks offer a versatile, flexible and accurate approach to loss reserving. However, such applications have focused primarily on the (important) problem of fitting accurat…
SynthETIC: an individual insurance claim simulator with feature control
Benjamin Avanzi, Gregory Clive Taylor, Melantha Wang +1
Recent years have seen rapid increase in the application of machine learning to insurance loss reserving. They yield most value when applied to large data sets, such as individual…
On the modelling of multivariate counts with Cox processes and dependent shot noise intensities
Benjamin Avanzi, Gregory Clive Taylor, Bernard Wong +1
In this paper, we develop a method to model and estimate several, _dependent_ count processes, using granular data. Specifically, we develop a multivariate Cox process with shot no…
Modelling and understanding count processes through a Markov-modulated non-homogeneous Poisson process framework
Benjamin Avanzi, Greg Taylor, Bernard Wong +1
The Markov-modulated Poisson process is utilised for count modelling in a variety of areas such as queueing, reliability, network and insurance claims analysis. In this paper, we e…