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Bias, guess and expert judgement in actuarial work

Published online by Cambridge University Press:  12 October 2016

E. R. W. Tredger ,
J. T. H. Lo ,
S. Haria ,
H. H. K. Lau ,
N. Bonello ,
B. Hlavka  and
C. Scullion
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Abstract

Expert judgement is frequently used within general insurance. It tends to be a method of last resort and used where data is sparse, non-existent or non-applicable to the problem under consideration. Whilst such judgements can significantly influence the end results, their quality is highly variable. The use of the term “expert judgement” itself can lend a generous impression of credibility to what may be a little more than a guess. Despite the increased emphasis placed on the importance of robust expert judgements in regulation, actuarial research to date has focussed on the more technical or data-driven methods, with less emphasis on how to use and incorporate softer information or how best to elicit judgements from others in a way that reduces cognitive biases. This paper highlights the research that the Getting Better Judgement Working Party has conducted in this area. Specifically, it covers the variable quality of expert judgement, both within and outside the regulatory context, and presents methods that may be applied to improve its formation. The aim of this paper is to arm the insurance practitioner with tools to distinguish between low-quality and high-quality judgements and improve the robustness of judgements accordingly, particularly for highly material circumstances.

Keywords

Expert JudgementElicitationCognitive BiasesHeuristicsBayesian Statistics
Type
Sessional meetings: papers and abstracts of discussions
Information
British Actuarial Journal , Volume 21 , Issue 3 , September 2016 , pp. 545 - 578
Copyright
© Institute and Faculty of Actuaries 2016 

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References

Antal, P.A. (2014). Internal capital models, 30th International Congress of Actuaries, 2014, available at https://cas.confex.com/cas/ica14/webprogram/Handout/Paper1720/Internal%20Models%20ICA.pdf (accessed 13 December 2015).Google Scholar
Arberz, P. & Canestraro, D. (2012). Estimating copulas for insurance from scarce observations, expert opinion and prior information. ASTIN Bulletin, 42, 271–290.Google Scholar
Ashcroft, M., Austin, R., Scolley, P. & Makin, S. (2015). Expert judgement. IFoA’s Solvency and Capital Management Working Party. British Actuarial Journal.Google Scholar
Bell, W. (1997). Foundations of Future Studies: Human Science for a New Era. vol. 1. New Jersey, USA: Transaction Publishers.Google Scholar
Brosius, E. (1993). Loss development using credibility, Casualty Actuarial Society Study Note.Google Scholar
De Bono, E. (2015). Lateral Thinking: Creativity Step by Step. London: Penguin Books.Google Scholar
Goodwin, P. & Wright, G. (2009). Decision Analysis for Management Judgment. London: Wiley.Google Scholar
Irving, J. L. (1972). Victims of Groupthink. New York: Houghton Mifflin.Google Scholar
Kahneman, D. (2012). Thinking, Fast and Slow. London: Penguin Books.Google Scholar
Kerley, C. & Margetts, S. (2006). Top down/bottom up correlation, 33rd Annual GIRO Convention, 2006, IFoA, available at https://www.actuaries.org.uk/sites/default/files/documents/pdf/kerley.pdf (accessed 13 December 2015).Google Scholar
O’Hagan, A., Jenkinson, D. J., Oakley, J. E. & Rakow, T. (2006). Uncertain Judgements: Eliciting Experts’ Probabilities. London: Wiley.Google Scholar
Rowe, G. & Wright, G. (2001). Expert opinions in forecasting: the role of the Delphi technique, in Principles of Forecasting: A Handbook for Researchers and Practitioners (ed. J.S. Armstrong). International Series in Operational Research and Management Science, Springer, USA, 30, 125–144.Google Scholar
Weick, M., Hopthrow, T., Abrams, D. & Taylor-Gooby, P. (2013). Cognition: minding risks – why the study of behaviour is important for the insurance industry, Lloyd’s of London, available at https://www.lloyds.com/~/media/lloyds/reports/emerging%20risk%20reports/cognitionfinal%20web.pdf (accessed 20 July 2015).Google Scholar