With the rapid advancement of artificial intelligence, machine learning, and multi-agent systems, banking systems are entering a new stage of digital transformation in which the role of human decision-makers is gradually diminishing, moving toward full automation. This paper investigates the development of humanless banking based on autonomous financial agents. The study adopts a descriptive-analytical approach and a library-based research method, relying on academic literature, international journals, and fintech reports. In addition, a conceptual modeling approach based on multi-agent systems is used to structure the proposed framework.Within this paradigm, intelligent agents perform key banking functions such as customer credit scoring, credit allocation, liquidity management, financial risk analysis, and fraud detection in real time without human intervention. The proposed architecture integrates deep learning, big data processing, and interactions among autonomous agents, leading to faster, more accurate, and scalable decision-making.Despite significant benefits such as reduced human error and improved operational efficiency, this approach faces challenges including algorithmic transparency, legal accountability, systemic risks, and ethical concerns. The findings suggest that while fully eliminating human decision-makers in the short term is difficult, a gradual shift toward autonomous banking can fundamentally transform the financial industry.