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

TL;DR

This paper presents an NLP-based method to identify systematic nonmeritorious consumer complaints by converting complaint narratives into quantitative data and analyzing them with specialized algorithms.

Contribution

It introduces a novel two-step approach combining NLP and anomaly detection algorithms to identify systematic anomalies in consumer complaint narratives.

Findings

Effective detection of systematic anomalies demonstrated on CFPB data

Improved identification of frequent nonmeritorious complaints

Combines classification and quantitative analysis for better accuracy

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.