AI-Powered Financial Insights: Using Large Language Models to Improve Government Decision-Making and Policy Execution

Luqing Ren

doi:10.70393/6a69656173.333139

OPEN ACCESS |Research Article ||2 October 2025

AI-Powered Financial Insights: Using Large Language Models to Improve Government Decision-Making and Policy Execution

Luqing Ren

Columbia University

lr3130@columbia.edu

Columbia University, New York, USA.

* Corresponding Author¹: Luqing Ren, E-Mail: lr3130@columbia.edu

Publication

Accepted 2025 September 19 ; Published 2025 October 2

Journal of Industrial Engineering and Applied Science, 2025, 3(5), 3005-6071.

Abstract

Given the complexity of fiscal data types and the lengthy policy execution chain, this study constructs an application framework for language models supporting government decision-making. It systematically investigates task modules including decision-making question-answering identification, expenditure forecasting modeling, executive summary extraction, semantic matching, and conflict reasoning. The framework elucidates model architecture design methodologies and semantic fusion mechanisms, while introducing response capability simulation testing and performance evaluation systems. Using heterogeneous fiscal corpora and multi-task experimental data, demonstrates that the model exhibits strong performance in accuracy, generative consistency, and generalization capabilities, supporting intelligent applications across diverse fiscal scenarios.

Keywords

Language Model , Semantic Matching , Execution Analysis .

Metadata

DOI:

10.70393/6a69656173.333139

ARK:

ark:/40704/JIEAS.v3n5a03

Pages: 21-26

References: 5

Disciplines: Artificial Intelligence Technology

Subjects: Natural Language Processing

Cite This Article

APA Style

Ren, L. (2025). Ai-powered financial insights: using large language models to improve government decision-making and policy execution. Journal of Industrial Engineering and Applied Science, 3(5), 21-26. https://doi.org/10.70393/6a69656173.333139

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.

References

1.

Barbosa, E. D., & Paramo, S. J. (2024). Enhancing board of director decision-making: The impact of government support on risk management and nonfinancial performance. Business Strategy & Development, 7(3), e413-e413.

2.

Mienye, D. I., Jere, N., Obaido, G., & Others. (2025). Large language models: An overview of foundational architectures, recent trends, and a new taxonomy. Discover Applied Sciences, 7(9), 1027-1027.

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Aman, S. S., Kone, T., N’guessan, G. B., & Others. (2025). Learning to represent causality in recommender systems driven by large language models (LLMs). Discover Applied Sciences, 7(9), 960.

4.

Qiu, J., Fang, Q., & Kang, W. (2025). Towards controllable and explainable text generation via causal intervention in LLMs. Electronics, 14(16), 3279.

5.

A, M. A., M, E., M, S., & Others. (2021). Factors influencing financial performance of the government. Academy of Accounting and Financial Studies Journal, 25(3), 1-15.

PUBLISHER'S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
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