A Comprehensive Review on Financial Explainable AI

ArXiv ID: 2309.11960 “View on arXiv”

Authors: Unknown

Abstract

The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex patterns. However, due to their lack of explainability, there are significant concerns regarding their use in critical sectors, such as finance and healthcare, where decision-making transparency is of paramount importance. In this paper, we provide a comparative survey of methods that aim to improve the explainability of deep learning models within the context of finance. We categorize the collection of explainable AI methods according to their corresponding characteristics, and we review the concerns and challenges of adopting explainable AI methods, together with future directions we deemed appropriate and important.

Keywords: Explainable Artificial Intelligence (XAI), Deep Learning, Financial Transparency, Survey, General (Finance)

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers
• Why: The paper is a review/survey discussing conceptual frameworks and categorizations of explainable AI methods, with no original mathematical derivations or empirical testing presented. Its focus is on literature synthesis rather than algorithmic innovation or backtesting.

  flowchart TD
    A["Research Goal: Survey Explainable AI<br>in Financial Deep Learning"] --> B["Methodology: Comparative Literature Review"]
    B --> C["Data/Inputs: Financial XAI Methods<br>from Academic Literature"]
    C --> D["Computational Process: Categorization &<br>Analysis by Method Characteristics"]
    D --> E["Key Outcomes: Comparative Survey,<br>Challenges, and Future Directions"]