Application of Natural Language Processing in Financial Risk Detection

2024/06/14 by Liyang Wang, Yu Cheng, Wang, Liyang +7 · 3 citations
Business, Management and Accounting · Decision Sciences · Economics, Econometrics and Finance · #Computation and Language (cs.CL) #FOS: Computer and information sciences #FOS: Economics and business #Insurance and Financial Risk Management #Risk Management (q-fin.RM) #Risk Management in Financial Firms #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2406.09765

openalex publication_date 2024/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Feature Augmentations for High-Dimensional Learning
• Evaluating Large Language Models (LLMs) in Financial NLP: A Comparative Study on Financial Report Analysis
• To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions

Related

• Textual Data Mining for Financial Fraud Detection: A Deep Learning Approach
• Financial Risk Assessment using Machine Learning : A Bibliometric Analysis
• RiskLabs: Predicting Financial Risk Using Large Language Model based on Multimodal and Multi-Sources Data
• Analyzing Credit Risk Model Problems through NLP-Based Clustering and Machine Learning: Insights from Validation Reports
• A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection