Artificial intelligence (AI) and machine learning (ML) offer numerous opportunities for financial sector participants. The FSB (2017) highlights that the various factors that spurred the use of fintech in general, have also led to further adoption of AI/ML in financial services. This includes the availability of more computing power, cheaper storage, parsing, and analysis of data, as well as the “rapid growth of datasets for learning and prediction owing to increased digitization and the adoption of web-based services.” Accordingly, central banks, supervisors/regulators, and market participants can deploy AI/ML tools1 to improve their products, services, risk management, compliance, and—specifically for legislators/regulators—their development of relevant legislation and regulations.The IMF’s Central Bank Legislation Database (CBLD) is large text-based dataset that offers an interesting testing ground to analyze developments in central bank legislation worldwide. The CBLD is the most comprehensive central bank legislation database in the world.2 The CBLD currently contains laws of 175 central banks and monetary unions and has 273 specific search categories that allow users to run granular queries on nearly any topic that could involve a central bank. The CBLD includes datasets from four specific update moments: 2010, 2015, 2020/2021, and 2023. Going forward, annual updates of the CBLD will take place in a prioritized manner (i.e., a prioritized selection of laws will be included in the annual update, based on, e.g., laws that have recently been fully updated, laws that include novel concepts, laws from countries that have limited data in the CBLD, etc.). CBLD data can be accessed by searching by country, or by pre-set groups of countries (notably, by region, income level, exchange rate arrangements, and membership of a monetary union). The database was opened to the public in February 2019; access requires a one-time (free) registration via the CBLD’s website (https://data.imf.org/cbld).