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ACTUARIAL APPLICATIONS OF WORD EMBEDDING MODELS

  • Gee Y Lee (a1), Scott Manski (a2) and Tapabrata Maiti (a3)

Abstract

In insurance analytics, textual descriptions of claims are often discarded, because traditional empirical analyses require numeric descriptor variables. This paper demonstrates how textual data can be easily used in insurance analytics. Using the concept of word similarities, we illustrate how to extract variables from text and incorporate them into claims analyses using standard generalized linear model or generalized additive regression model. This procedure is applied to the Wisconsin Local Government Property Insurance Fund (LGPIF) data, in order to demonstrate how insurance claims management and risk mitigation procedures can be improved. We illustrate two applications. First, we show how the claims classification problem can be solved using textual information. Second, we analyze the relationship between risk metrics and the probability of large losses. We obtain good results for both applications, where short textual descriptions of insurance claims are used for the extraction of features.

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ACTUARIAL APPLICATIONS OF WORD EMBEDDING MODELS

  • Gee Y Lee (a1), Scott Manski (a2) and Tapabrata Maiti (a3)

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