Money laundering is one of the most fundamental issues in Iran's economic system and banks. Money laundering can have very destructive effects on a country’s economy and society; Therefore, legal and executive solutions with high effectiveness are important to deal with it. International organizations and institutions active in the field of combating money laundering, such as the Financial Action Task Force (FATF), continuously examine and evaluate countries and organizations and classify them based on the calculated risk level. In countries that are in the high-risk category banking transactions face major challenges and economic and financial activities tend to be difficult. Based on this, the importance of combating money laundering and compliance with international standards increases. With regard to the very high volume of data generated from various face-to-face and non-face-to-face channels in the banking industry, it is necessary to apply proper governance to them in order to fight money laundering. Technological solutions, artificial intelligence, machine learning and a risk-based approach to deal with money laundering go through the path of access to high-quality data. According to the research questions, Bank Mellat, one of the largest commercial banks in Iran, was selected for the statistical population and in order to identify the data governance framework in the fight against money laundering through targeted and snowball sampling, 14 managers and expert experts with the highest scores were selected for interviews. 750 people were included. By conducting an in-depth interview and using the foundation's data theorizing method, a paradigm model of the data governance framework was designed from 43 extracted codes in the form of 18 concepts and 6 categories. Subsequently, the designed model was given to academic and experimental experts and evaluated. The study findings are explained and the results and future suggestions are presented.