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自然语言处理的流程，用于从消费者投诉文本中检测系统性非正当投诉（简称系统性异常）。虽然分类算法可识别显著异常，但对于规模较小且频发的系统性异常，由于技术原因及人工分析师固有局限性等多重因素，分类算法可能失效。为此，在分类步骤完成后，我们进一步将投诉文本转化为量化数据，并通过系统性异常检测算法进行分析。本研究以美国消费者金融保护局投诉数据库中的文本数据为例，完整演示了该流程的实施过程。