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Recently, there has been a growing trend in incorporating Artificial Intelligence (AI) into financial decision-making, prompting concerns about the transparency and accountability of these intricate systems. This study investigates the impact of Explainable Artificial Intelligence (XAI) approaches in alleviating these concerns and improving transparency in financial decision-making processes. The paper commences by outlining the current landscape of AI applications in finance, underscoring the complex and opaque nature of advanced machine learning models. The lack of interpretability in these models presents a significant challenge, as stakeholders, regulators, and end-users often struggle to comprehend the reasoning behind AI-driven financial decisions. This opacity raises questions regarding accountability and trust, particularly in critical financial scenarios. The primary focus of the research centers on the analysis and implementation of XAI techniques to introduce transparency into financial AI systems. Various XAI methods, including rule-based systems, model-agnostic approaches, and interpretable machine learning models, are scrutinized for their effectiveness in producing understandable explanations for AI-driven financial decisions. The paper explores how these approaches can be tailored to meet the distinct requirements of the financial domain, where interpretability is essential for regulatory compliance and stakeholder confidence. Moreover, the research delves into the potential impact of XAI on accountability mechanisms within financial institutions. By offering interpretable explanations for model outputs, XAI not only enhances …