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Text Mining Methods Applied to Insurance Company Customer Calls: A Case Study

Author

Listed:
  • Xiyue Liao
  • Guoqiang Chen
  • Ben Ku
  • Rahul Narula
  • Janet Duncan

Abstract

The purpose of this case study is to develop a process for a U.S. personal lines insurance company to improve its customer service, make call center operations more efficient, and reduce costs by analyzing customer calls. Text mining methods such as topic modeling and sentiment analysis are used to study approximately 10,000 nonclaim customer calls from 2016. Results show the most frequent topics of calls and how customer sentiment differs between topics, which will allow the company to adjust its customer service accordingly.

Suggested Citation

  • Xiyue Liao & Guoqiang Chen & Ben Ku & Rahul Narula & Janet Duncan, 2020. "Text Mining Methods Applied to Insurance Company Customer Calls: A Case Study," North American Actuarial Journal, Taylor & Francis Journals, vol. 24(1), pages 153-163, January.
    • Handle: RePEc:taf:uaajxx:v:24:y:2020:i:1:p:153-163
    DOI: 10.1080/10920277.2019.1649155
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    Cited by:

    1. Peiheng Gao & Ning Sun & Xuefeng Wang & Chen Yang & Riv{c}ardas Zitikis, 2023. "NLP-based detection of systematic anomalies among the narratives of consumer complaints," Papers 2308.11138, arXiv.org, revised Mar 2024.

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