  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.
