Research on the Application of Deep Learning Model in Financial Innovation Regulation from the Perspective of Building a Financial Power

Abstract

Driven by the dual demands of advancing the strategy of building a strong financial nation, accelerating the iteration of financial innovation, and improving the quality and efficiency of regulation, traditional financial regulatory models are struggling to adapt to the risk control needs of complex innovative scenarios, making the transformation to intelligent regulation imperative. This paper, from the perspective of building a strong financial nation, analyzes the practical achievements and existing shortcomings of financial innovation regulation in my country, assesses the regulatory adaptability of deep learning at the technology, scenario, and objective levels, and clarifies the real challenges of technology implementation. It constructs a deep learning model adapted to financial innovation regulation, completes parameter optimization and empirical testing, and verifies the model's practical effectiveness in risk identification and early warning. Finally, from five dimensions—technology empowerment, data governance, collaborative regulation, talent development, and system improvement—it proposes an optimized path for deep learning to empower financial innovation regulation, helping to strengthen the financial security defense line and promote the high-quality advancement of building a strong financial nation.