NLP-based detection of systematic anomalies among the narratives of consumer complaints
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929290225909760 |
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| author | Gao, Peiheng Sun, Ning Wang, Xuefeng Yang, Chen Zitikis, Ričardas |
| author_facet | Gao, Peiheng Sun, Ning Wang, Xuefeng Yang, Chen Zitikis, Ričardas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_11138 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | NLP-based detection of systematic anomalies among the narratives of consumer complaints Gao, Peiheng Sun, Ning Wang, Xuefeng Yang, Chen Zitikis, Ričardas Methodology Computation and Language Risk Management Machine Learning 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. |
| title | NLP-based detection of systematic anomalies among the narratives of consumer complaints |
| topic | Methodology Computation and Language Risk Management Machine Learning |
| url | https://arxiv.org/abs/2308.11138 |