The rise of blockchain technology in recent years has brought a different perspective to the traditional circulation of money in the physical environment and digital banking. Money began to exist in a decentralized form in the virtual environment. The use of technology in finance is an emerging trend. Although this seems like an advantage, it also imposes a great responsibility because financial institutions lagging behind the technology will create risks for the institution and the customers. Therefore, the integration of new methods and techniques in accordance with the current technological innovations and developments of our age in financial processes and the smooth functioning of the processes afterward should be guaranteed. At this point, it is believed that artificial intelligence technologies make essential contributions. Artificial intelligence provides many tasks, such as decision making, automatic inference, estimation/foresight and speech/object recognition in corporate processes that can be performed almost similar to human performance. Moreover, in critical decision-making processes, it is thought that many humanspecific errors can be prevented, and faster and more accurate decisions can be taken. This research aims to examine the applications of machine learning, a subfield of artificial intelligence, in finance, as well as recent related studies in the literature. In this context, process automation, financial predictions in investment, algorithmic trading & highfrequency trading, financial advisory, financial security and financial risk management are examined. In this way, the study’s primary motivation is to provide a different perspective to researchers particularly interested in finance and machine learning, and contribute to progress in the field.