SynthETIC: An individual insurance claim simulator with feature control

Benjamin Avanzi, Greg Taylor, Melantha Wang and Bernard Wong

Insurance: Mathematics and Economics, 2021, vol. 100, issue C, 296-308

Abstract: 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 claims, or large claim triangles. In short, they are likely to be useful in the analysis of any data set whose volume is sufficient to obscure a naked-eye view of its features. Unfortunately, such large data sets are in short supply in the actuarial literature. Accordingly, one needs to turn to synthetic data. Although the ultimate objective of these methods is application to real data, the use of synthetic data containing features commonly observed in real data is also to be encouraged.

Keywords: Granular models; Individual claims; Individual claim simulator; Loss reserving; Partial payments; Simulated losses; Superimposed inflation; SynthETIC (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 C55 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:100:y:2021:i:c:p:296-308

DOI: 10.1016/j.insmatheco.2021.06.004

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