NLP-based detection of systematic anomalies among the narratives of consumer complaints
This addresses the challenge of identifying small, frequent anomalies in consumer complaints for regulatory or financial institutions, but it appears incremental as it builds on existing classification and anomaly detection methods.
The paper tackles the problem of detecting systematic nonmeritorious consumer complaints in narratives by developing an NLP-based procedure that converts narratives into quantitative data for anomaly detection, applied to the Consumer Financial Protection Bureau database.
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.