This systematic literature review (SLR) examines the application of Explainable Artificial Intelligence (XAI) in the financial sector, analyzing 138 relevant articles published between 2005 and 2022. The study identifies key financial tasks where XAI is applied, including credit management, stock price prediction, and fraud detection. It highlights the most commonly used black-box AI techniques in finance, such as Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), and Random Forest. The review emphasizes the use of feature importance, Shapley Additive Explanations (SHAP), and rule-based methods to enhance model interpretability. It also discusses the development and application of new XAI methods, as well as the challenges and unresolved issues in implementing XAI in finance. The study categorizes XAI methods based on their financial applications, explaining the differences in XAI methods across various tasks and applications. It also explores the importance of XAI in ensuring transparency, trust, and accountability in financial decision-making. The review concludes that XAI is crucial for improving the understanding and trust in AI systems used in finance, and that further research is needed to address the challenges and opportunities in this field. The study provides a comprehensive overview of the current state of XAI research in finance, highlighting the key areas of application, the methods used, and the challenges faced. The findings indicate that XAI is increasingly being adopted in finance to improve the transparency and interpretability of AI models, and that there is a growing need for further research to explore the potential of XAI in this domain.This systematic literature review (SLR) examines the application of Explainable Artificial Intelligence (XAI) in the financial sector, analyzing 138 relevant articles published between 2005 and 2022. The study identifies key financial tasks where XAI is applied, including credit management, stock price prediction, and fraud detection. It highlights the most commonly used black-box AI techniques in finance, such as Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), and Random Forest. The review emphasizes the use of feature importance, Shapley Additive Explanations (SHAP), and rule-based methods to enhance model interpretability. It also discusses the development and application of new XAI methods, as well as the challenges and unresolved issues in implementing XAI in finance. The study categorizes XAI methods based on their financial applications, explaining the differences in XAI methods across various tasks and applications. It also explores the importance of XAI in ensuring transparency, trust, and accountability in financial decision-making. The review concludes that XAI is crucial for improving the understanding and trust in AI systems used in finance, and that further research is needed to address the challenges and opportunities in this field. The study provides a comprehensive overview of the current state of XAI research in finance, highlighting the key areas of application, the methods used, and the challenges faced. The findings indicate that XAI is increasingly being adopted in finance to improve the transparency and interpretability of AI models, and that there is a growing need for further research to explore the potential of XAI in this domain.