• Corpus ID: 237453286

SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator

  title={SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator},
  author={Benjamin Avanzi and Greg Taylor and 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 estimates are simulated in continuous time, and a record is output for each individual claim. Revisions for the case estimates are also simulated as a sequence over the lifetime of the claim, in a number of different situations. Furthermore, some dependencies in relation to case estimates of incurred losses are incorporated… 


Claim Models: Granular and Machine Learning Forms
The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their
2021b. SynthETIC: Synthetic experience tracking insurance claims. https://CRAN
  • 2021
Recent Challenges in Actuarial Science
For centuries, mathematicians and, later, statisticians, have found natural research and employment opportunities in the realm of insurance. By definition, insurance offers financial cover against
2021c. SynthETIC: Synthetic experience tracking insurance claims. https://CRAN
  • 2021
Recent challenges in actuarial science. Annual Review of Statistics and Its Application in press
  • 2021
SPLICE: Synthetic paid loss and incurred cost experience (splice) simulator
  • 2021
SynthETIC: Synthetic experience tracking insurance claims
  • 2021
An Individual Claims History Simulation Machine
A fully calibrated stochastic scenario generator that is based on real non-life insurance data and allows everyone to simulate their own synthetic insurance portfolio of individual claims histories and back-test thier preferred claims reserving method.
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