Modern Economics & Management Forum

Against the backdrop of accelerated financial digital transformation, Python has become a core tool to address the pain points of traditional financial data processing due to its efficient data processing capability and abundant open-source libraries. Current applications of Python in automated financial data processing suffer from insufficient compatibility in data integration, low script reusability, weak exception handling mechanisms, and inadequate security control, which restrict processing efficiency and in-depth application. Based on Python’s technical features and business requirements for financial data processing, this paper systematically analyzes the application status and existing difficulties. It proposes optimization strategies from four dimensions: data integration, script development, process control, and security guarantee, providing theoretical references and practical paths for enterprises to improve financial data processing efficiency, reduce labor costs, and strengthen data-driven decision-making.