Machine learning approaches to outlier detection
By Abdal Chaudhry, Miruna Dudceac, and Michael Leitschkis
15 December 2021
A brute-force nested simulations approach to capture risk interdependencies is infeasible with current resources available to insurers. It would require 1,000 or more risk-neutral simulations to produce a single stochastic scenario of the full risk distribution. This paper delves into finding an automated way to identify and remove outliers. We discuss the following:
- A simple outlier deletion technique and its limitations
- A powerful method known as Cook’s distance
- A few alternative machine learning approaches
- Conclusions and areas for further research
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About the Author(s)
Abdal Chaudhry
Miruna Dudceac
Michael Leitschkis
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