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

arXiv: https://arxiv.org/abs/2308.11138 · HTML: https://mlsanity.com/paper/2308.11138/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 60 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 3 of 3)

- [Computational Analysis of Insurance Complaints: GEICO Case Study](https://mlsanity.com/paper/1806.09736/) (2018)
- [Detecting intrusions in control systems: a rule of thumb, its justification and illustrations](https://mlsanity.com/paper/1806.06295/) (2018)
- [Detecting systematic anomalies affecting systems when inputs are stationary time series](https://mlsanity.com/paper/2011.10195/) (2020)

## Built on by (most cited first, 3 of 3)

- [Performance of diverse evaluation metrics in NLP-based assessment and text generation of consumer complaints](https://mlsanity.com/paper/2506.21623/) (2025)
- [SKG-VLA: Scene Knowledge Graph Priors for Structured Scene Semantics and Multimodal Reasoning for Decision Making](https://mlsanity.com/paper/2605.09343/) (2026)
- [Distributional sentiment modeling and anomaly detection for consumer complaint assessment](https://mlsanity.com/paper/2609.31653/) (2026)

## Abstract (arXiv)

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.