A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods

TL;DR

This paper discusses best practices for implementing explainable AI in financial time series forecasting, emphasizing data quality, audience-specific explanations, and explanation stability to improve transparency and trust.

Contribution

It proposes domain-driven guidelines for deploying XAI in finance, addressing limitations of classical methods like LIME and SHAP.

Findings

Highlighting importance of data quality in XAI effectiveness

Emphasizing audience-specific explanation methods

Stressing the need for stable and reliable explanations

Abstract

Machine learning and deep learning have become increasingly prevalent in financial prediction and forecasting tasks, offering advantages such as enhanced customer experience, democratising financial services, improving consumer protection, and enhancing risk management. However, these complex models often lack transparency and interpretability, making them challenging to use in sensitive domains like finance. This has led to the rise of eXplainable Artificial Intelligence (XAI) methods aimed at creating models that are easily understood by humans. Classical XAI methods, such as LIME and SHAP, have been developed to provide explanations for complex models. While these methods have made significant contributions, they also have limitations, including computational complexity, inherent model bias, sensitivity to data sampling, and challenges in dealing with feature dependence. In this context, this paper explores good practices for deploying explainability in AI-based systems for finance, emphasising the importance of data quality, audience-specific methods, consideration of data properties, and the stability of explanations. These practices aim to address the unique challenges and requirements of the financial industry and guide the development of effective XAI tools.