A Survey of Financial AI: Architectures, Advances and Open Challenges
This is an incremental survey paper that synthesizes existing knowledge about financial AI for researchers and practitioners in finance and AI.
This survey systematically analyzes financial AI developments across predictive models, decision-making frameworks, and knowledge augmentation systems, examining innovations like foundation models for financial time series and graph-based architectures while identifying critical gaps between theoretical advances and industrial implementation.
Financial AI empowers sophisticated approaches to financial market forecasting, portfolio optimization, and automated trading. This survey provides a systematic analysis of these developments across three primary dimensions: predictive models that capture complex market dynamics, decision-making frameworks that optimize trading and investment strategies, and knowledge augmentation systems that leverage unstructured financial information. We examine significant innovations including foundation models for financial time series, graph-based architectures for market relationship modeling, and hierarchical frameworks for portfolio optimization. Analysis reveals crucial trade-offs between model sophistication and practical constraints, particularly in high-frequency trading applications. We identify critical gaps and open challenges between theoretical advances and industrial implementation, outlining open challenges and opportunities for improving both model performance and practical applicability.