Stochastic loss reserving with mixture density neural networks

Muhammed Taher Al-Mudafer, Benjamin Avanzi, Greg Taylor and Bernard Wong

Insurance: Mathematics and Economics, 2022, vol. 105, issue C, 144-174

Abstract: In recent years, new techniques based on artificial intelligence and machine learning in particular have been making a revolution in the work of actuaries, including in loss reserving. A particularly promising technique is that of neural networks, which have been shown to offer a versatile, flexible and accurate approach to loss reserving. However, applications of neural networks in loss reserving to date have been primarily focused on the (important) problem of fitting accurate central estimates of the outstanding claims. In practice, properties regarding the variability of outstanding claims are equally important (e.g., quantiles for regulatory purposes).

Keywords: Loss reserving; Neural network; Mixture density network; Distributional forecasting; Machine learning (search for similar items in EconPapers)
JEL-codes: C45 C53 G22 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:105:y:2022:i:c:p:144-174

DOI: 10.1016/j.insmatheco.2022.03.010

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