We develop a natural language processing-based procedure for detecting systematic nonmeritorious consumer complaints – called simply systematic anomalies – among complaint narratives. While classification algorithms are used to identify meritorious complaints, these algorithms may falter in the case of smaller and more frequent systematic patterns of nonmeritorious complaints. This could be for a variety of reasons, such as technical issues or the natural limitations of human analysts. Therefore, following the classification stage, the complaint narratives are converted into quantitative data, which are then analyzed using indexes for detecting systematic anomalies. An illustration of the entire procedure is provided using complaint narratives from the Consumer Complaint Database of the US Consumer Financial Protection Bureau. The results suggest that the support vector machine outperforms other selected classifiers. Although the classification results with the Valence Aware Dictionary for sEntiment Reasoning (VADER) intensity pertinent to the featurization step have lower accuracy, they contain fewer nonmeritorious complaints than those without the VADER intensity.