AI-Enhanced Administrative Prosecutorial Supervision in Financial Big Data: New Concepts and Functions for the Digital Era

Authors

  • Yida Zhu Financial Analysis, Rutgers Business School, NJ, USA
  • Keke Yu University of California, Santa Barbara, CA, US
  • Ming Wei Finance, Washington University in St. Louis, MO, USA
  • Yanli Pu Finance, University of Illinois at Urbana Champaign, IL, USA
  • Zeyu Wang Computer Science, University of Toronto, Toronto, Canada

DOI:

https://doi.org/10.5281/zenodo.13766965

Keywords:

artificial intelligence, financial supervision, machine learning, regulatory technology

Abstract

This research explores the integration of artificial intelligence (AI) in administrative prosecutorial supervision within the context of extensive financial data analysis. The study investigates the application of advanced machine learning algorithms, natural language processing techniques, and network analysis methods in enhancing the efficiency and effectiveness of financial crime detection and prevention. We propose a novel framework for "penetrating" administrative prosecutorial supervision, which leverages AI to analyse multi-layered financial data and uncover hidden risks. The research examines the implementation of real-time monitoring systems and the development of adaptive machine-learning models for fraud detection. Furthermore, we address data privacy challenges, model explainability, and regulatory adaptation in AI-enhanced supervision. The study introduces new concepts such as integrating supervision and case handling, extensive data legal supervision, and substantive resolution of administrative disputes. Our findings demonstrate significant improvements in violation detection rates, reduction in false positives, and increased efficiency in case handling and dispute resolution. The research also highlights the importance of international cooperation in combating cross-border financial crimes and the need for continuous innovation in supervisory technologies. This study contributes to the ongoing discourse on the responsible implementation of AI in economic regulation. It provides insights for policymakers and regulatory bodies seeking to enhance their supervisory capabilities in the digital era.

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References

Belov, V. A., Asadulina, R. R., & Semenov, V. P. (2017). Legal regulation of economic activity is the sphere of interaction of private-law and public mechanism of law regulation. IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 1311-1314.

Du, W. (2022). Research on the application of computer artificial intelligence technology in bank financial risk monitoring. IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA), pp. 1218-1221.

Manoharan, G., Dharmaraj, A., Sheela, S. C., Naidu, K., Chavva, M., & Chaudhary, J. K. (2024). Machine learning-based real-time fraud detection in financial transactions. International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), pp. 1-6.

Suresh, N., Neelam, H., Chakrapani, E., Kumar, K. A., & Ali, S. S. (2023). Artificial intelligence advances and their repercussions on the financial system. International Conference on Computer Communication and Informatics (ICCCI), pp. 1-6.

Thommandru, A., Mone, V., Mitharwal, S., & Tilwani, R. (2023). Exploring the intersection of machine learning, money laundering, data privacy, and law. International Conference on Innovative Data Communication Technologies and Application (ICIDCA), pp. 149-155.

Li, S., Xu, H., Lu, T., Cao, G., & Zhang, X. (2024). Emerging technologies in finance: revolutionizing investment strategies and tax management in the digital era. Management Journal for Advanced Research, 4(4), 35-49.

Shi J, Shang F, Zhou S, et al. (2024). Applications of quantum machine learning in large-scale e-commerce recommendation systems: enhancing efficiency and accuracy. Journal of Industrial Engineering and Applied Science, 2(4), 90-103.

Wang, S., Zheng, H., Wen, X., & Fu, S. (2024). Distributed high-performance computing methods for accelerating deep learning training. Journal of Knowledge Learning and Science Technology, 3(3), 108-126.

Zhang, M., Yuan, B., Li, H., & Xu, K. (2024). LLM-cloud complete: Leveraging cloud computing for efficient large language model-based code completion. Journal of Artificial Intelligence General Science (JAIGS), 5(1), 295-326.

Lei, H., Wang, B., Shui, Z., Yang, P., & Liang, P. (2024). Automated lane change behavior prediction and environmental perception based on slam technology. arXiv preprint arXiv:2404.04492.

Wang, B., He, Y., Shui, Z., Xin, Q., & Lei, H. (2024). Predictive optimization of DDoS attack Mitigation in distributed systems using machine learning. Applied and Computational Engineering, 64, 95-100.

Wang, B., Zheng, H., Qian, K., Zhan, X., & Wang, J. (2024). Edge computing and AI-driven intelligent traffic monitoring and optimization. Applied and Computational Engineering, 77, 225-230.

Zhao, F., Zhang, M., Zhou, S., & Lou, Q. (2024). Detection of network security traffic anomalies based on machine learning KNN method. Journal of Artificial Intelligence General Science (JAIGS), 1(1), 209-218.

Yang, M., Huang, D., Zhang, H., & Zheng, W. (2024). AI-Enabled precision medicine: Optimizing treatment strategies through genomic data analysis. Journal of Computer Technology and Applied Mathematics, 1(3), 73-84.

Wen, X., Shen, Q., Zheng, W., & Zhang, H. (2024). AI-driven solar energy generation and smart grid integration a holistic approach to enhancing renewable energy efficiency. International Journal of Innovative Research in Engineering and Management, 11(4), 55-55.

Lou, Q. (2024). New development of administrative prosecutorial supervision with chinese characteristics in the new era. Journal of Economic Theory and Business Management, 1(4), 79-88.

Zhou, S., Yuan, B., Xu, K., Zhang, M., & Zheng, W. (2024). The impact of pricing schemes on cloud computing and distributed systems. Journal of Knowledge Learning and Science Technology, 3(3), 193-205.

Sun, J., Wen, X., Ping, G., & Zhang, M. (2024). Application of news analysis based on large language models in supply chain risk prediction. Journal of Computer Technology and Applied Mathematics, 1(3), 55-65.

Zhao, F., Zhang, M., Zhou, S., & Lou, Q. (2024). Detection of network security traffic anomalies based on machine learning KNN method. Journal of Artificial Intelligence General Science (JAIGS), 1(1), 209-218.

Yang, M., Huang, D., Zhang, H., & Zheng, W. (2024). AI-enabled precision medicine: Optimizing treatment strategies through genomic data analysis. Journal of Computer Technology and Applied Mathematics, 1(3), 73-84.

Wen, X., Shen, Q., Zheng, W., & Zhang, H. (2024). AI-driven solar energy generation and smart grid integration a holistic approach to enhancing renewable energy efficiency. International Journal of Innovative Research in Engineering and Management, 11(4), 55-55.

Lou, Q. (2024). New development of administrative prosecutorial supervision with chinese characteristics in the new era. Journal of Economic Theory and Business Management, 1(4), 79-88.

Zhou, S., Yuan, B., Xu, K., Zhang, M., & Zheng, W. (2024). The impact of pricing schemes on cloud computing and distributed systems. Journal of Knowledge Learning and Science Technology, 3(3), 193-205.

Sun, J., Wen, X., Ping, G., & Zhang, M. (2024). Application of news analysis based on large language models in supply chain risk prediction. Journal of Computer Technology and Applied Mathematics, 1(3), 55-65.

Huang, D., Yang, M., Wen, X., Xia, S., & Yuan, B. (2024). AI-driven drug discovery: Accelerating the development of novel therapeutics in biopharmaceuticals. Journal of Knowledge Learning and Science Technology, 3(3), 206-224.

Liu, Y., Tan, H., Cao, G., & Xu, Y. (2024). Enhancing user engagement through adaptive UI/UX design: A study on personalized mobile app interfaces.

Xu, H., Li, S., Niu, K., & Ping, G. (2024). Utilizing deep learning to detect fraud in financial transactions and tax reporting. Journal of Economic Theory and Business Management, 1(4), 61-71.

Li, P., Hua, Y., Cao, Q., & Zhang, M. (2020, December). Improving the restore performance via physical-locality middleware for backup systems. in Proceedings of the 21st International Middleware Conference, pp. 341-355.

Zhou, S., Yuan, B., Xu, K., Zhang, M., & Zheng, W. (2024). The impact of pricing schemes on cloud computing and distributed systems. Journal of Knowledge Learning and Science Technology, 3(3), 193-205.

Shang, F., Zhao, F., Zhang, M., Sun, J., & Shi, J. (2024). Personalized recommendation systems powered by large language models: integrating semantic understanding and user preferences. International Journal of Innovative Research in Engineering and Management, 11(4), 39-49.

Li, S., Xu, H., Lu, T., Cao, G., & Zhang, X. (2024). Emerging technologies in finance: revolutionizing investment strategies and tax management in the digital era. Management Journal for Advanced Research, 4(4), 35-49.

Shi J, Shang F, Zhou S, et al. (2024). Applications of quantum machine learning in large-scale e-commerce recommendation systems: enhancing efficiency and accuracy. Journal of Industrial Engineering and Applied Science, 2(4), 90-103.

Wang, S., Zheng, H., Wen, X., & Fu, S. (2024). Distributed high-performance computing methods for accelerating deep learning training. Journal of Knowledge Learning and Science Technology, 3(3), 108-126.

Zhang, M., Yuan, B., Li, H., & Xu, K. (2024). LLM-cloud complete: Leveraging cloud computing for efficient large language model-based code completion. Journal of Artificial Intelligence General Science (JAIGS), 5(1), 295-326.

Huang, D., Yang, M., Wen, X., Xia, S., & Yuan, B. (2024). AI-driven drug discovery: Accelerating the development of novel therapeutics in biopharmaceuticals. Journal of Knowledge Learning and Science Technology, 3(3), 206-224.

Liu, Y., Tan, H., Cao, G., & Xu, Y. (2024). Enhancing user engagement through adaptive UI/UX design: A study on personalized mobile app interfaces.

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Published

16-09-2024

How to Cite

Yida Zhu, Keke Yu, Ming Wei, Yanli Pu, & Zeyu Wang. (2024). AI-Enhanced Administrative Prosecutorial Supervision in Financial Big Data: New Concepts and Functions for the Digital Era. Social Science Journal for Advanced Research, 4(5), 40–54. https://doi.org/10.5281/zenodo.13766965