A Hypothesis on Good Practices for AI-based Systems for Financial Time
  Series Forecasting: Towards Domain-Driven XAI Methods
Branka Hadji Misheva Joerg Osterrieder
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.