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NLP-based detection of systematic anomalies among the narratives of consumer complaints

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Listed:
  • Peiheng Gao
  • Ning Sun
  • Xuefeng Wang
  • Chen Yang
  • Riv{c}ardas Zitikis

Abstract

We develop an NLP-based procedure for detecting systematic nonmeritorious consumer complaints, simply called systematic anomalies, among complaint narratives. While classification algorithms are used to detect pronounced anomalies, in the case of smaller and frequent systematic anomalies, the algorithms may falter due to a variety of reasons, including technical ones as well as natural limitations of human analysts. Therefore, as the next step after classification, we convert the complaint narratives into quantitative data, which are then analyzed using an algorithm for detecting systematic anomalies. We illustrate the entire procedure using complaint narratives from the Consumer Complaint Database of the Consumer Financial Protection Bureau.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2308.11138
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    References listed on IDEAS

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    1. 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.
    2. Rachel M. Hayes & Feng Jiang & Yihui Pan, 2021. "Voice of the Customers: Local Trust Culture and Consumer Complaints to the CFPB," Journal of Accounting Research, Wiley Blackwell, vol. 59(3), pages 1077-1121, June.
    3. Nadezhda Gribkova & Ričardas Zitikis, 2018. "A User-Friendly Algorithm for Detecting the Influence of Background Risks on a Model," Risks, MDPI, vol. 6(3), pages 1-11, September.
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