Machine learning is still not sufficiently recognized as a quality decision-making tool in the financial sector. The application of machine learning can be much more widespread in the context of finance, considering that machine learning could potentially contribute to a more productive and efficient use of financial data to obtain relevant calculations and calculations. Unsupervised machine learning can recognize patterns and relationships among large data sets, which are otherwise very challenging statistical tasks that require resources and time. On the other hand, supervised learning can help with statistics-based forecasting. Risk analyses, fraud detection mechanisms and forecasts can be much more accurate and sophisticated, and ultimately calculations can be performed more efficiently with the help of machine learning algorithms. Machine learning means that the user gets accurate calculations in an extremely short time using advanced statistical methods that simultaneously cover extremely large ranges of data. A large range of data, on the other hand, contributes to the accuracy and relevance of the calculation model of the necessary calculation, because it can take into account data from very different sources, and the result is efficiently fast. Decision-making information usually comes from extensive analyses where data from a variety of sources are used, and decision-making information is more robust when using different sources and types of data. The application of machine learning could provide a revolution when it comes to optimizing business processes in financial industries. However, like any other technology, it is necessary to know the settings of the functioning of such technologies in order not to use the technology in an insufficiently good way. It is necessary to know the limitations of technology to successfully manage and use it in business.