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

ArXiv ID: 2411.12747 “View on arXiv”

Authors: Unknown

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.

Keywords: Financial AI, Foundation Models, Portfolio Optimization, Financial Time Series, Automated Trading

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats
• Why: The paper is rich in mathematical formalisms, presenting detailed notations and problem formulations for predictive and decision-making tasks. However, as a survey of existing works, it focuses on theoretical analysis and architectural reviews rather than providing new empirical results, backtests, or implementation code.

  flowchart TD
    A["Research Goal:<br>Analyze Financial AI Architectures &<br>Challenges"] --> B{"Key Methodology"}

    B --> C["1. Systematic Analysis of Three Dimensions"]
    C --> D["Predictive Models<br>Market Dynamics"]
    C --> E["Decision Frameworks<br>Portfolio Optimization"]
    C --> F["Knowledge Augmentation<br>Unstructured Data"]

    B --> G["Data/Inputs<br>Financial Time Series, Market Graphs,<br>Unstructured Financial Info"]

    G --> H["Computational Processes<br>Foundation Models, Graph-Based<br>Architectures, Hierarchical Frameworks"]

    H --> I{"Key Findings & Outcomes"}
    
    I --> J["Innovations<br>Foundation Models, Graph Architecture,<br>Hierarchical Optimization"]
    I --> K["Trade-offs<br>Sophistication vs. Practical Constraints<br>(e.g., High-Frequency Trading)"]
    I --> L["Open Challenges<br>Theoretical Advances vs.<br>Industrial Implementation"]