Smoothness and monotonicity constraints for neural networks using ICEnet
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Date
2024-11
Publication Type
Journal Article
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yes
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Abstract
Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods, but also due to the close connection of these models to the generalized linear models (GLMs) currently used in industry. Although constraining GLM parameters relating to insurance risk factors to be smooth or exhibit monotonicity is trivial, methods to incorporate such constraints into deep neural networks have not yet been developed. This is a barrier for the adoption of neural networks in insurance practice since actuaries often impose these constraints for commercial or statistical reasons. In this work, we present a novel method for enforcing constraints within deep neural network models, and we show how these models can be trained. Moreover, we provide example applications using real-world datasets. We call our proposed method ICEnet to emphasize the close link of our proposal to the individual conditional expectation model interpretability technique.
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published
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Journal / series
Volume
18 (3)
Pages / Article No.
712 - 739
Publisher
Cambridge University Press
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Edition / version
Methods
Software
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Subject
Smoothing; Whittaker-Henderson smoothing; graduation; monotonicity; deep neural networks; constrained likelihood; individual conditional expectation
Organisational unit
08813 - Wüthrich, Mario Valentin (Tit.-Prof.)
02204 - RiskLab / RiskLab