Author(s)

Liyang Wang ¹, Yu Cheng ², Ao Xiang ³, Jingyu Zhang ⁴, Haowei Yang ⁵

Affiliation(s)

¹ Olin Business School, Washington University in St. Louis, St. Louis, MO, Finance
² The Fu Foundation School of Engineering and Applied Science, Operations Research, Columbia University, New York, NY, USA
³ School of Computer Science & Engineering (School of Cybersecurity), Digital Media Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
⁴ The Division of the Physical Sciences, the University of Chicago, Analytics, Chicago, IL, USA
⁵ Cullen College of Engineering, Industrial Enginnering, University of Houston, Houston, TX, USA

Corresponding Author

Liyang Wang

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.

KEYWORDS

Natural Language Processing (NLP), financial risk detection, text mining, machine learning

CITE THIS PAPER

Liyang Wang, Yu Cheng, Ao Xiang, Jingyu Zhang, Haowei Yang, Application of Natural Language Processing in Financial Risk Detection. Financial Engineering and Risk Management (2024) Vol. 7: 1-10. DOI: http://dx.doi.org/10.23977/ferm.2024.070401.

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