Human Centered AI for Financial Decisions

We survey the state of the art of AI applications to financial expectations and the role quantum logic can play in further advancements of AI technologies. We discuss financial applications of such machine learning techniques as reinforcement learning and deep neural networks to the analysis of financial statements, algorithmic trading, portfolio management, and robo-advising. Next, we elaborate on the emergence and advancement of QML (quantum machine learning) and advocate for the wider exploration of the advantages of quantum inspired neural networks, steaming from the use of quantum logic that is able to capture agents’ non- classical expectations and non expected utility decisions, also coined “bounded rationality”. We would like to motivate to use human—like AI techniques that are centered on quantum, rather than classical logic to (i) represent the human brain type information processing; (ii) speed up the work of the AI algorithms; (iii) better operate in complex and uncertain environments.