Digital transformation and the growing adoption of Artificial Intelligence (AI) in the financial industry have significantly transformed decision-making processes, risk management, credit assessment, and fraud detection. However, the complexity of AI models and the opacity of their decision-making mechanisms have raised concerns regarding transparency, accountability, privacy protection, and regulatory compliance. In this context, Explainable Artificial Intelligence (XAI) has emerged as an effective approach to enhance the transparency and interpretability of algorithmic decisions. The effective implementation of this technology in financial systems requires a coherent data governance framework.The purpose of this study is to design a data governance model for Explainable AI-based financial systems. This research is applied in terms of purpose and qualitative-exploratory in terms of methodology. To achieve this objective, the literature on data governance, explainable artificial intelligence, and financial technologies was systematically reviewed, and the dimensions and components of the proposed model were identified through content analysis and expert opinions. The findings indicate that the proposed model consists of data quality, security and privacy, algorithmic transparency, accountability, risk management, regulatory compliance, and continuous monitoring.
The results suggest that implementing this model can enhance stakeholder trust, reduce algorithmic bias, improve the quality of financial decision-making, and facilitate compliance with legal and regulatory requirements. Therefore, the proposed model can serve as a strategic framework for financial institutions, banks, fintech companies, and regulatory authorities.