Application of Natural Language Processing in Financial Risk Detection

Liyang Wang, Yu Cheng, Ao Xiang, Jingyu Zhang and Haowei Yang

Papers from arXiv.org

Abstract: This paper explores the application of Natural Language Processing (NLP) in financial risk detection. By constructing an NLP-based financial risk detection model, this study aims to identify and predict potential risks in financial documents and communications. First, the fundamental concepts of NLP and its theoretical foundation, including text mining methods, NLP model design principles, and machine learning algorithms, are introduced. Second, the process of text data preprocessing and feature extraction is described. Finally, the effectiveness and predictive performance of the model are validated through empirical research. The results show that the NLP-based financial risk detection model performs excellently in risk identification and prediction, providing effective risk management tools for financial institutions. This study offers valuable references for the field of financial risk management, utilizing advanced NLP techniques to improve the accuracy and efficiency of financial risk detection.

Date: 2024-06, Revised 2024-06
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-rmg
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2406.09765 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2406.09765

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().