ECLIPSE: Holistic AI System for Preparing Insurer Policy Data

Varun Sriram (), Zijie Fan () and Ni Liu ()
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Varun Sriram: Guy Carpenter, 1166 6th Ave, New York, NY 10036, USA
Zijie Fan: Guy Carpenter, 1166 6th Ave, New York, NY 10036, USA
Ni Liu: Guy Carpenter, 1166 6th Ave, New York, NY 10036, USA

Risks, 2022, vol. 11, issue 1, 1-19

Abstract: Reinsurers possess high volumes of policy listings data from insurers, which they use to provide insurers with analytical insights and modeling that guide reinsurance treaties. These insurers often act on the same data for their own internal modeling and analytics needs. The problem is this data is messy and needs significant preparation in order to extract meaningful insights. Traditionally, this has required intensive manual labor from actuaries. However, a host of modern AI techniques and ML system architectures introduced in the past decade can be applied to the problem of insurance data preparation. In this paper, we explore a novel application of AI/ML on policy listings data that poses its own unique challenges, by outlining the holistic AI-based platform we developed, ECLIPSE (Elegant Cleaning and Labeling of Insurance Policies while Standardizing Entities). With ECLIPSE, actuaries not only save time on data preparation but can build more effective loss models and provide crisper insights.

Keywords: entity resolution; data matching; NLP; labeling; MLOps; machine learning; data preparation; AI; Apache Spark (Spark SQL) (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
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