Against the backdrop of rapid global economic development and intensified contradictions in ecological protection, green and sustainable development has become a global consensus. As a resource allocation hub, the ESG performance of the financial industry has a leverage effect on green transformation. Artificial intelligence technology, with its data-driven and intelligent decision-making advantages, has opened up new paths for financial enterprises to improve their ESG performance. However, economic policy fluctuations may interfere with the effectiveness of technology empowerment, forming a dynamic interaction mechanism of "technology empowerment policy fluctuations ESG performance". This study adopts a logical framework of "theory empirical conclusion", constructs a model based on technological innovation theory and stakeholder theory, selects panel data of major global financial enterprises from 2012 to 2022, and uses a fixed effects model for benchmark regression, mechanism testing, and heterogeneity analysis to verify the impact of artificial intelligence on ESG performance and the moderating effect of economic policy uncertainty. Research has found that artificial intelligence has a significant positive impact on the overall ESG performance of financial enterprises, especially in the environmental and governance dimensions, but there is a negative effect in the social dimension; The impact effect is heterogeneous due to the concentration of equity and the nature of property rights, with dispersed equity and non-state-owned enterprises more likely to improve ESG performance through AI; The level of technological innovation is the core transmission path, and AI indirectly improves ESG performance by driving technological innovation; Economic policy uncertainty will weaken the role of AI in promoting ESG. This study reveals the impact mechanism of economic policy fluctuations on the ESG performance of financial enterprises under the empowerment of artificial intelligence, providing theoretical support for financial enterprises to implement precise policies and optimize technology investment decisions. At the same time, it emphasizes the importance of maintaining economic policy stability to maximize the social and environmental benefits of technology empowerment, and promotes the transformation of the financial industry towards intelligence and sustainability.

Dexi Chen. The Impact Mechanism of Economic Policy Fluctuations on ESG Performance of Financial Enterprises Empowered by Artificial Intelligence. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 122-130. https://doi.org/10.38007/IJBDIT.2026.070115.

[1] Yixian Jiang. Performance Optimization and Improvement of Advertising Machine Learning Platform Based on Distributed Systems. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 9-17

[2] Jin Li. Performance Analysis of Efficient Microservice Architecture in the Financial Industry. Machine Learning Theory and Practice (2026), Vol. 6, Issue 1: 1-9.

[3] Bukun Ren. Multimodal Learning Method for Cross-Modal Data Alignment and Retrieval. International Journal of Multimedia Computing (2026), Vol. 7, Issue 1: 1-8.

[4] Zhengle Wei. Research on Innovative Design of Financial Derivatives and Market Risk Management Strategies. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 19-27

[5] Yuhan Zhou. Green Bonds and Sustainable Financing Models in Energy Finance. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 28-35

[6] Yilin Fu. Research on the Application of Innovative Financial Technologies in Capital Market Risk Management. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 1-9

[7] Linwei Wu. Data Visualization and Decision Support Analysis Based on Tableau. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 10-18

[8] Wang, C. (2026). Research on the Control of Uncertainty Risks in Investment Decision-making by Financial Modeling.

[9] Liu, Y. (2026). The Application of Data-Driven Financial Risk Management in Multinational Enterprises. Economics and Management Innovation, 3(1), 20-26.

[10] Huang, J. (2026). Practice of Public Space Optimization and Functional Enhancement in Cultural Architecture. European Journal of Engineering and Technologies, 2(1), 9-21.

[11] Xu, D. (2026). AI-Driven Video Content Optimization Strategies for Immersive Media. European Journal of Engineering and Technologies, 2(1), 1-8.

[12] Sun, J. (2025). Research on Financial Systemic Risk Measurement Based on Investor Sentiment and Network Text Mining. Socio-Economic Statistics Research (2025), 6(2), 185-193.

[13] Zhang, X. (2025). Optimization and Implementation of Time Series Dimensionality Reduction Anti-fraud Model Integrating PCA and LSTM under the Federated Learning Framework. Procedia Computer Science, 262, 992-1001.

[14] Yiting Gu. Application and Optimization Strategies of Cloud Services in Front end Engineering. International Journal of Big Data Intelligent Technology (2026), Vol. 7, Issue 1: 1-8

[15] Shuang Yuan. Research on Abnormal Detection and Transaction Risk Management Based on Machine Learning. International Journal of Social Sciences and Economic Management (2026), Vol. 7, Issue 1: 10-18