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

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.