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

IF 1.5 Q3 BUSINESS, FINANCE
Isaac Cohen Sabban, O. Lopez, Yann Mercuzot
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引用次数: 1

Abstract

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.
通过深度神经网络自动分析保险报告,识别严重索赔
摘要在本文中,我们开发了一种方法,使用文本报告中包含的信息自动对索赔进行分类(在报告开头进行了编辑)。通过这种自动分析,目的是预测索赔是否会特别严重。困难在于数据库中很少有这种极端的说法,因此逻辑回归等经典预测技术很难准确预测结果。由于数据是不平衡的(与阳性标签相关的观测值太少),我们提出了不同的再平衡算法来处理这个问题。我们讨论了用于处理文本数据的不同嵌入方法的使用,以及网络架构的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.10
自引率
5.90%
发文量
22
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