Explaining AI in Finance: Past, Present, Prospects

ArXiv ID: 2306.02773 “View on arXiv”

Authors: Unknown

Abstract

This paper explores the journey of AI in finance, with a particular focus on the crucial role and potential of Explainable AI (XAI). We trace AI’s evolution from early statistical methods to sophisticated machine learning, highlighting XAI’s role in popular financial applications. The paper underscores the superior interpretability of methods like Shapley values compared to traditional linear regression in complex financial scenarios. It emphasizes the necessity of further XAI research, given forthcoming EU regulations. The paper demonstrates, through simulations, that XAI enhances trust in AI systems, fostering more responsible decision-making within finance.

Keywords: Explainable AI (XAI), Machine Learning, Financial Applications, Regulatory Compliance

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 1.0/10
• Quadrant: Philosophers
• Why: The paper is a survey and literature review focusing on conceptual discussions of XAI methods like SHAP and LIME, with no heavy mathematical derivations. It relies on conceptual arguments and simulations rather than real-world backtests or implementation details.

  flowchart TD
    RQ["Research Goal<br>Explainable AI in Finance"] -->|Input| Inp["Simulated Financial Data"]
    Inp --> M["Methodology:<br>XAI vs. Linear Regression"]
    M --> C["Computational Analysis<br>Shapley Values & ML Models"]
    C --> F1["Outcome: Superior<br>Interpretability"]
    C --> F2["Outcome: Enhanced<br>Trust & Responsibility"]
    C --> F3["Outcome: Regulatory<br>Compliance Potential"]
    F1 & F2 & F3 --> R["Final Conclusion:<br>XAI Drives Responsible Finance"]