The Actuary and IBNR Techniques: A Machine Learning Approach

53 Pages Posted: 11 Nov 2020 Last revised: 13 Nov 2020

See all articles by Caesar Balona

Caesar Balona

QED Actuaries and Consultants

Ronald Richman

Old Mutual Insure; University of the Witwatersrand

Date Written: August 14, 2020

Abstract

Actuarial reserving techniques have evolved from the application of algorithms, like the chain-ladder method, to stochastic models of claims development, and, more recently, have been enhanced by the application of machine learning techniques. Despite this proliferation of theory and techniques, there is relatively little guidance on which reserving techniques should be applied and when. In this paper, we revisit traditional reserving techniques within the framework of supervised learning to select optimal reserving models. We show that the use of optimal techniques can lead to more accurate reserves and investigate the circumstances under which different scoring metrics should be used.

Keywords: IBNR, machine learning, reserving, short-term insurance, non-life insurance

JEL Classification: G22

Suggested Citation

Balona, Caesar and Richman, Ronald, The Actuary and IBNR Techniques: A Machine Learning Approach (August 14, 2020). Available at SSRN: https://ssrn.com/abstract=3697256 or http://dx.doi.org/10.2139/ssrn.3697256

Caesar Balona

QED Actuaries and Consultants

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South Africa

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Ronald Richman (Contact Author)

Old Mutual Insure ( email )

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South Africa

University of the Witwatersrand ( email )

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South Africa

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