NLP-Based Detection of Systematic Anomalies Among the Narratives of Consumer Complaints
32 Pages Posted: 28 Aug 2023 Last revised: 3 Dec 2023
Date Written: August 21, 2023
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.
Keywords: consumer complaints, anomaly detection, classification, sentiment scores, Cobb-Douglas function, natural language processing
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