Semantic Network Analysis of Financial Regulatory Documents: Extracting Early Risk Warning Signals

Yibang Liu; Wenyu Bi; Jiayan Fan

doi:10.70393/616a736d.323731

OPEN ACCESS |Research Article ||18 March 2025

Semantic Network Analysis of Financial Regulatory Documents: Extracting Early Risk Warning Signals

Yibang Liu

Baruch College

attoemavlelk@outlook.com

Financial Engineering, Baruch College, NY, USA.

Wenyu Bi

University of Southern California

Science in Applied Economics and Econometrics, University of Southern California, CA, USA.

Jiayan Fan

University of Michigan

Information Scienc, University of Michigan, MI, USA.

* Corresponding Author¹: Yibang Liu, E-Mail: attoemavlelk@outlook.com

Publication

Accepted 2025 March 10 ; Published 2025 March 18

Academic Journal of Sociology and Management, 2025, 3(2), 3005-5040.

Abstract

This paper presents a semantic network analysis framework for extracting early risk warning signals from financial regulatory documents. Financial regulations contain critical information about emerging risks, but their increasing volume and complexity challenge traditional analysis methods. We propose a novel approach that constructs semantic networks from regulatory texts, representing concepts as nodes and their relationships as edges. Our methodology integrates techniques from natural language processing and network science to identify structural patterns indicative of emerging risks. The framework was implemented and tested on a corpus of 2,874 financial regulatory documents published between 2010-2023. Results demonstrate that the semantic network approach outperforms traditional keyword-based monitoring in both risk coverage (79.4% vs 68.7%) and false alarm reduction (11.6% vs 22.5%). The multi-metric ensemble method achieved an F1-score of 0.81 with an average lead time of 82.6 days before explicit regulatory announcements. Validation with 24 regulatory compliance professionals confirmed the practical utility of the approach, showing comparable quality to expert analysis while reducing analysis time from 24.7 to 4.8 hours. This research contributes to both theoretical understanding of regulatory text structures and practical applications for financial compliance and risk management.

Keywords

Semantic Network Analysis , Financial Regulation , Risk Detection , Natural Language Processing .

Metadata

DOI:

10.70393/616a736d.323731

ARK:

ark:/40704/AJSM.v3n2a03

Pages: 22-32

References: 32

Disciplines: Economics

Subjects: Behavioral Economics

Cite This Article

APA Style

Liu, Y., Bi, W. & Fan, J. (2025). Semantic network analysis of financial regulatory documents: extracting early risk warning signals. Academic Journal of Sociology and Management, 3(2), 22-32. https://doi.org/10.70393/616a736d.323731

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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