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Keywords = payments per claim incurred

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11 pages, 706 KiB  
Article
Penalising Unexplainability in Neural Networks for Predicting Payments per Claim Incurred
by Jacky H. L. Poon
Risks 2019, 7(3), 95; https://doi.org/10.3390/risks7030095 - 1 Sep 2019
Cited by 2 | Viewed by 3951
Abstract
In actuarial modelling of risk pricing and loss reserving in general insurance, also known as P&C or non-life insurance, there is business value in the predictive power and automation through machine learning. However, interpretability can be critical, especially in explaining to key stakeholders [...] Read more.
In actuarial modelling of risk pricing and loss reserving in general insurance, also known as P&C or non-life insurance, there is business value in the predictive power and automation through machine learning. However, interpretability can be critical, especially in explaining to key stakeholders and regulators. We present a granular machine learning model framework to jointly predict loss development and segment risk pricing. Generalising the Payments per Claim Incurred (PPCI) loss reserving method with risk variables and residual neural networks, this combines interpretable linear and sophisticated neural network components so that the ‘unexplainable’ component can be identified and regularised with a separate penalty. The model is tested for a real-life insurance dataset, and generally outperformed PPCI on predicting ultimate loss for sufficient sample size. Full article
(This article belongs to the Special Issue Claim Models: Granular Forms and Machine Learning Forms)
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