Optimization-Driven Artificial Intelligence Finance: A Paradigm Shift and Technological Reconfiguration

The widespread application of artificial intelligence technologies and large language models in financial markets has provided new methodological foundations and technical carriers for overcoming the limitations of traditional financial optimization.These advances significantly enhance the modeling mechanisms and decision optimization of financial problems.Grounded in the core logic of optimization in financial behavior,this paper focuses on several key domains.These include financial product pricing and investment return forecasting,portfolio selection and asset trading decision optimization,precision improvement in financial risk identification and management,and the advancement of regulatory tools and supervisory effectiveness.By systematically comparing traditional financial methods with artificialintelligence-based approaches,this study reveals the paradigm shift and technological reconfiguration brought about by artificial intelligence in addressing financial optimization problems.The analysis summarizes the mechanisms through which artificial intelligence reshapes financial modeling and decision-making,and clarifies the specific pathways and methodological innovations of its application.The findings demonstrate that artificial-intelligence-driven finance delivers superior performance in solving optimization problems.In particular,it enhances forecasting accuracy,optimizes asset pricing models and trading decision mechanisms,and enables more intelligent and granular approaches to financial risk identification and regulation.