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.

翻译：我们开发了一种基于NLP的方法，用于检测消费者投诉文本中的系统性非真实投诉（简称系统性异常）。虽然分类算法能够检测明显的异常案例，但对于规模较小且频繁出现的系统性异常，由于技术因素及人工分析师固有的局限性等原因，传统算法可能表现不佳。因此，作为分类过程的后续步骤，我们将投诉文本转化为量化数据，并通过系统性异常检测算法对其进行分析。我们以消费者金融保护局投诉数据库中的投诉文本为例，完整展示了该方法的实施流程。