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Automatic analysis of insurance reports through deep neural networks to identify severe claims

Published online by Cambridge University Press:  09 March 2021

Isaac Cohen Sabban ,
Olivier Lopez  and
Yann Mercuzot
Isaac Cohen Sabban*
Affiliation:
Sorbonne Université, CNRS, Laboratoire de Probabilités, Statistique et Modélisation, LPSM, 4 place Jussieu, F-75005 Paris, France Pacifica, Crédit Agricole Assurances, F-75015 Paris, France
Olivier Lopez*
Affiliation:
Sorbonne Université, CNRS, Laboratoire de Probabilités, Statistique et Modélisation, LPSM, 4 place Jussieu, F-75005 Paris, France
Yann Mercuzot
Affiliation:
Pacifica, Crédit Agricole Assurances, F-75015 Paris, France
*
*Corresponding author. E-mails: isaac.cohen-sabban@etu.upmc.fr, olivier.lopez@sorbonne-universite.fr
*Corresponding author. E-mails: isaac.cohen-sabban@etu.upmc.fr, olivier.lopez@sorbonne-universite.fr
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Abstract

In this paper, we develop a methodology to automatically classify claims using the information contained in text reports (redacted at their opening). From this automatic analysis, the aim is to predict if a claim is expected to be particularly severe or not. The difficulty is the rarity of such extreme claims in the database, and hence the difficulty, for classical prediction techniques like logistic regression to accurately predict the outcome. Since data is unbalanced (too few observations are associated with a positive label), we propose different rebalance algorithm to deal with this issue. We discuss the use of different embedding methodologies used to process text data, and the role of the architectures of the networks.

Keywords

InsuranceDeep neural networksLong Short-Term MemoryConvolutional Neural NetworksText analysis
Type
Original Research Paper
Information
Annals of Actuarial Science , Volume 16 , Issue 1 , March 2022 , pp. 42 - 67
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Institute and Faculty of Actuaries

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