Financial applications of AI, such as credit card scoring, have strict regulatory constraints and arise many ethical concerns. The chapter demonstrates how XAI techniques can be applied in credit predictions to improve both individual and group-level machine learning fairness and comply with regulations. The chapter also reviews recent literature in the field of machine learning fairness. We show that demographic factors affect the outcomes of these models, and we highlight this effect using XAI techniques in this context. A case study is used as an example, featuring visualization methods such as SHAP and LIME, as well as a detailed analysis on a publicly available lending dataset.