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Loss Reserving Models: Granular and Machine Learning Forms
Open AccessArticle

Penalising Unexplainability in Neural Networks for Predicting Payments per Claim Incurred

Independent Researcher, Level 18, 1 Farrer Place, Sydney, NSW 2000, Australia
Risks 2019, 7(3), 95; https://doi.org/10.3390/risks7030095
Received: 13 June 2019 / Revised: 13 July 2019 / Accepted: 23 August 2019 / Published: 1 September 2019
(This article belongs to the Special Issue Claim Models: Granular Forms and Machine Learning Forms)
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. View Full-Text
Keywords: actuarial; risk pricing; loss reserving; granular models; neural networks; payments per claim incurred actuarial; risk pricing; loss reserving; granular models; neural networks; payments per claim incurred
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H. L. Poon, J. Penalising Unexplainability in Neural Networks for Predicting Payments per Claim Incurred. Risks 2019, 7, 95.

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