This course provides an introduction to Explainable Artificial Intelligence (XAI), focusing on various state-of-the-art models such as LIME, SHAP, DiCE, LRP, and counterfactual explanations, exploring their methodologies and applications. It delves into the challenges faced by classical XAI methods, including issues of scalability, interpretability, and accuracy. The course also covers advanced topics on the limitations and reliability of these models when applied to complex data sets. A special emphasis is placed on methods tailored for financial applications, addressing the specific needs and regulatory requirements unique to this sector. By the end of the course, participants will be equipped with the knowledge to implement and critically evaluate XAI approaches within financial systems.