A Survey of Financial AI: Architectures, Advances and Open Challenges

TL;DR

This survey comprehensively reviews recent advances in Financial AI, covering architectures, models, and challenges in market forecasting, portfolio optimization, and automated trading, highlighting innovations and open issues.

Contribution

It systematically analyzes key developments in Financial AI, including foundation models, graph architectures, and hierarchical frameworks, and discusses practical constraints and open challenges.

Findings

Introduction of foundation models for financial time series

Use of graph-based architectures for market relationships

Identification of gaps between theory and industrial practice

Abstract

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.