This systematic literature review (SLR) examines the application of Explainable Artificial Intelligence (XAI) in finance, identifying 138 relevant articles from 2005 to 2022. The review highlights the importance of XAI in improving risk assessment, minimizing trust loss, and promoting a more resilient financial ecosystem. The most common financial tasks addressed by XAI include credit management, stock price predictions, and fraud detection. Key AI black-box techniques evaluated for explainability are Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), and Random Forest. Common XAI methods used include feature importance, Shapley Additive Explanations (SHAP), and rule-based methods, often integrated into explainability frameworks. The review also discusses challenges, requirements, and unresolved issues in applying XAI in finance, emphasizing the need for further research and development. The study aims to provide valuable insights into the current advances and trends in XAI for various financial tasks, help practitioners select appropriate XAI techniques, and identify areas for future investigation.This systematic literature review (SLR) examines the application of Explainable Artificial Intelligence (XAI) in finance, identifying 138 relevant articles from 2005 to 2022. The review highlights the importance of XAI in improving risk assessment, minimizing trust loss, and promoting a more resilient financial ecosystem. The most common financial tasks addressed by XAI include credit management, stock price predictions, and fraud detection. Key AI black-box techniques evaluated for explainability are Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), and Random Forest. Common XAI methods used include feature importance, Shapley Additive Explanations (SHAP), and rule-based methods, often integrated into explainability frameworks. The review also discusses challenges, requirements, and unresolved issues in applying XAI in finance, emphasizing the need for further research and development. The study aims to provide valuable insights into the current advances and trends in XAI for various financial tasks, help practitioners select appropriate XAI techniques, and identify areas for future investigation.