Khái niệm cốt lõi
Developing a Natural Language Processing (NLP) procedure to detect systematic anomalies in consumer complaints.
Tóm tắt
The content introduces a method using NLP to identify systematic anomalies in consumer complaints. It discusses the importance of analyzing consumer complaints for regulatory purposes and service quality. The study uses the Consumer Complaint Database of the Consumer Financial Protection Bureau for illustration. Classifiers like Support Vector Machine (SVM) are employed to classify meritorious and non-meritorious complaints. The study also introduces the concept of systematic-anomaly detection and evaluates the performance of different classifiers. Anomaly detection indices are used to assess the presence of anomalies in the dataset. The study concludes with implications for financial institutions and the potential of the proposed methodology.
Thống kê
"In 2021, state insurance departments received 259,345 official complaints."
"Among the selected 2,849 complaints, 1,051 are meritorious."
"Companies have granted consumers tangible reliefs 21.9% of the time."
Trích dẫn
"The results suggest that the Support Vector Machine (SVM) outperforms other selected classifiers."