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

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.

Link to the paper

You can find a preprint here

Communications and Events

We presented this paper to several conferences or working groups :

• Working Group "Actuariat et Risques Contemporains" (GT ARC), Paris, France (5 April 2019)
• 23rd "International Congress on Insurance: Mathematics and Economics", Munich, Germany (10-12 July 2019)
• Online "International Conference in Actuarial science, data science and finance" - online (28-29 April 2020).
• "Actuarial Colloquium Paris 2020" - Sections Virtual Colloquium - online (11-15 May 2020).

Dependencies

• Tensorflow
• NumPy
• scikit-learn
• Matplotlib
• seaborn
• pandas
• gensim
• itertools
• functools

Help and Support

Communication

• Mail: isaaccohensabban_at_gmail_dot_com

Citation

If you use our algoritms in a scientific publication, we would appreciate citations: bibtex