Artificial Intelligence in Finance - Financial Technology

Abstract

A paradigm for understanding and addressing the increasing significance of artificial intelligence (or "AI") in finance was put forth in this study. It highlights the importance of human accountability in resolving the "black box" issue of AI, or its lack of transparency. Possibility of unexpected or undetected harmful impacts from people's inability to understand how an AI works internally or from the AI functioning autonomously without human supervision or involvement. Following an explanation and illumination of the various applications of AI in banking, we draw attention to a number of potential challenges and legal requirements due to the technology's quick development. After describing and demonstrating the numerous uses of AI in banking, we draw attention to a number of potential challenges and legal requirements due to the technology's quick development. challenges with AI in financial services and the resources available to address them. Nonlinear and unexpected behaviour that changes over time is a common feature of many modern real-world financial applications. Consequently, there is a growing need to tackle temporal variant problems that are very nonlinear. Interest in artificial intelligence techniques grew as a result of these problems and others with conventional models.

This paper compares and reviews three widely used artificial intelligence techniques in the financial market: expert systems, artificial neural networks, and hybrid intelligence systems.

Additionally, a financial market is separated into three areas: financial forecasting and planning, portfolio management, and credit evaluation. The results demonstrate that these artificial intelligence methods outperform conventional statistical methods in resolving financial issues, particularly when nonlinear patterns are present. This outperformance is not guaranteed, though.