Artificial Intelligence in Central Banking

Abstract

Abstract The paper uses qualitative research to investigate the potential uses of artificial intelligence in the field of central banking. The analysis shows that monetary policy, prudential supervision and the oversight of payments are the areas where the use artificial intelligence is most likely to bring benefits. Monetary policy calibration involves working with long time series of data for various parameters and making the necessary analysis and forecasts, an activity in which artificial neural networks may prove useful. Bank supervision can benefit from the use natural language processing algorithms that can read documents and extract the relevant information. Such algorithms can read all of the required documents (not just those that the supervisor selected) and return all of the sentences that contain a certain predefined expression. In the field of the oversight of payments, the capabilities of machine learning to identify new patterns or anomalies in the data that could indicate fraud or money laundering will boost the efforts to combat them. In terms of challenges associated with the use of artificial intelligence in central banking, perhaps the two biggest challenges are that some of the models do not allow for a reasonable level of explainability of the algorithm(s) through which they arrive at the result (especially relevant for bank supervision) and data availability. With respect to the latter, although the issue of quantity of data can be dismissed as a shortcoming given the huge amounts of data available, the issue of data quality seems to be more pronounced, as deficiencies such as data measured incompletely or incorrectly, scarcity and regulatory barriers that impede data sharing may be difficult to surpass.