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- W4386114004 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." @default.
- W4386114004 created "2023-08-24" @default.
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- W4386114004 date "2023-08-21" @default.
- W4386114004 modified "2023-09-27" @default.
- W4386114004 title "NLP-based detection of systematic anomalies among the narratives of consumer complaints" @default.
- W4386114004 doi "https://doi.org/10.48550/arxiv.2308.11138" @default.
- W4386114004 hasPublicationYear "2023" @default.
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