The International Monetary Fund (IMF) is a global organisation focused on building a framework for international economic cooperation and eliminating competitive currency devaluations. It helps to ensure the stability of the international monetary system so that countries and citizens can transact with each other efficiently at a greater speed. It provides surveillance, financial assistance, capacity development, Special Drawing Rights (SDRs), resources, and governance. It offers to facilitate the balanced growth of international trade, promote exchange stability, assist in the establishment of a multilateral system of payments, and many more.

On the other hand, the International Monetary Fund has started leveraging artificial intelligence to support social goods efficiently without any potential error. It has realised the need for a global approach to data in the digital age with the global principles on data policy. According to the IMF blogs, artificial intelligence can have a major impact on the developing economies— it can widen the gap between the rich and poor nations in the upcoming times. It has presented one departmental paper known as “Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance”. Artificial intelligence has a huge potential for enormous societal and economic impact with new benefits. Multiple financial institutions and banks have started leveraging AI systems to manage high volumes of loan applications to enhance underwriting process as well as fraud detection. AI-enabled data analytics are helping to enhance the analysis of complex balance sheets and stress testing models.

There are four areas of artificial intelligence and machine learning that have importance in the functionalities of the IMF— governance, labour markets, taxes and social equity. The IMF needs to develop expertise in the use of micro-level data from e-commerce, IoT, satellite data and many more. The International Monetary Fund has examined all kinds of artificial intelligence use cases in fintech as well as banking. The use cases include predictions with multiple data sources, fraud detection and loan portfolio analysis. The global organisation has predicted an impending shift towards artificial intelligence to pursue monetary and macroprudential mandates.