19 citations · 23 across the 2 of their papers we have counts for
3 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…
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…
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…