Advancing the Use of Deep Learning in Loss Reserving: A Generalized DeepTriangle Approach

Yining Feng and Shuanming Li ()
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Yining Feng: AXA, 20 Gracechurch Street, London EC3V 0BG, UK
Shuanming Li: Centre for Actuarial Studies, Department of Economics, The University of Melbourne, Melbourne, VIC 3010, Australia

Risks, 2023, vol. 12, issue 1, 1-14

Abstract: This paper proposes a generalized deep learning approach for predicting claims developments for non-life insurance reserving. The generalized approach offers more flexibility and accuracy in solving actuarial reserving problems. It predicts claims outstanding weighted by exposure instead of loss ratio to remove subjectivity associated with premium weighting. Chain-ladder predicted outstanding claims are used as part of the multi-task learning to remove the dependence on case estimates. Grid-search is introduced for hyperparameter tuning to improve model performance. Performance-wise, the Generalized DeepTriangle outperforms both traditional chain-ladder methodology, the automated machine learning approaches (AutoML), and the original DeepTriangle model.

Keywords: loss reserving; actuarial reserving techniques; machine learning; deep learning; DeepTriangle; artificial neural networks (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
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