KORIŠĆENJE VELIKIH SKUPOVA PODATAKA I MAŠINSKOG UČENJA U CENTRALNIM BANKAMA
USE OF BIG DATA AND MACHINE LEARNING IN CENTRAL BANKS

Summary/Abstract: The digitalization of the economy is not a new phenomenon. However, although it has been going on for several decades, today there is a consensus that it has reached a turning point. Big data is a very important driver of digital transformation in the banking sector, but, more broadly, in the entire financial sector. Machine learning (ML) has its origins in computational statistics. Its primary role is to use algorithms to identify patterns or relationships that exist in the data and use these patterns in prediction. Central banks are interested in Data Science for several reasons. The first reason is innovation and potential that can provide more accurate results. Second, such models are good for obtaining detailed data at the micro level. Moreover, Data Science gives impetus to innovative sources of information that have not been used much (such as texts) to better approximate people’s feelings and preference and their expectations in the central bank’s economy and activities.

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