Big data and technology-based supervision

Leading central banks and the Bank for International Settlements (BIS) are increasingly leveraging big data, artificial intelligence, and advanced analytics in central bank operations. The key challenge is the absence of organization-wide data governance framework and data standards while volume, variety, and demand for data are growing rapidly. This has created a need for more robust mechanisms to govern data ownership, sharing, usage, quality, accountability, security and information flows across the institution. It is therefore essential to establish an integrated data management framework encompassing all these activities. To support greater use of data-driven decision-making, advanced analytics, and artificial intelligence, the central bank must address challenges related to data inconsistencies, duplication, quality, and standardization, which can undermine the timely and reliable use of data. In addition, there is a growing need for institution-wide policies governing data ownership, user access rights, accountability for data quality, and appropriate levels of data protection. Consequently, beyond developing technological infrastructure, establishing an integrated data governance framework that aligns data quality, standards, ownership, accountability, accessibility, and security has emerged as a critical priority for the Bank.

Under the Bank of Mongolia’s Medium-Term Strategy (2026–2030), the Bank plans to progressively establish an integrated data management framework by strengthening data governance, developing data sharing infrastructure through APIs, and the embedding data-driven decision-making throughout its operation.

• Establish the foundations of data governance.
• Automate data sharing and processing.
• Embed data-driven decision-making.

• Establish a Data Board responsible for overseeing the implementation of the Bank of Mongolia’s data governance framework and approving the Board’s regulations and operating procedures.
• Enhance the integrated data reporting and sharing infrastructure by introducing API-based SupTech and RegTech solutions that facilitate seamless collection, processing, and sharing of banking and financial sector data through a unified platform.
• Develop a framework to detect emerging financial sector risks and vulnerabilities at an early stage by leveraging data-driven artificial intelligence and advanced analytics, enabling the timely delivery of insights and alerts to relevant stakeholders.

By establishing a unified framework for data quality, ownership, accountability, and usage, the Bank of Mongolia will enhance information consistency, strengthen oversight and accountability for data and information flows, and accelerate decision-making. This, in turn, will create the conditions necessary to strengthen the institutional capacity of the central bank. In addition, by adopting the best international practices employed by leading central banks in the area of SupTech, develop data collection standards and enable real-time data sharing through API-based services. Furthermore, the Bank of Mongolia will gradually shift away from the conventional practice of collecting data with significant time lag and conducting analyses primarily based on historical developments. Instead, it will transition toward a data-driven management framework in which policy objectives, regulatory requirements, and risk indicators are embedded in cross-sector information systems and technological solutions.

This shift will enable to move beyond assessing policy implementation through reports and ex-post analysis. By incorporating risk thresholds, and monitoring mechanisms directly into systems and processes, the Bank of Mongolia will be better positioned to detect emerging risks at an early stage and implement timely preventive and corrective policy measures.

• The establishment of an integrated data management and governance framework at the Bank of Mongolia will enable banking and financial sector stakeholders to transition to standardized data sharing practices supported by modern technological solutions. This will enhance data quality, accessibility, coherence, and efficiency of data utilization across the financial sector.
• The automation of data processing, compilation, and monitoring processes will reduce manual work, human intervention, processing time, and operational burden. As a result, the Bank of Mongolia’s responsiveness, operational efficiency, productivity, and institutional capacity will be further strengthened.
• The use of data-driven advanced analytics and artificial intelligence will enhance the capacity to detect emerging risks and vulnerabilities in the banking system and financial sector using high-frequency and real-time data, enabling timely early warning, informed decision-making, and proactive policy and supervisory responses.