  Artificial Intelligence (AI) models have reached a very significant level of
accuracy. While their superior performance offers considerable benefits, their
inherent complexity often decreases human trust, which slows their application
in high-risk decision-making domains, such as finance. The field of eXplainable
AI (XAI) seeks to bridge this gap, aiming to make AI models more
understandable. This survey, focusing on published work from the past five
years, categorizes XAI approaches that predict financial time series. In this
paper, explainability and interpretability are distinguished, emphasizing the
need to treat these concepts separately as they are not applied the same way in
practice. Through clear definitions, a rigorous taxonomy of XAI approaches, a
complementary characterization, and examples of XAI's application in the
finance industry, this paper provides a comprehensive view of XAI's current
role in finance. It can also serve as a guide for selecting the most
appropriate XAI approach for future applications.
