Authors :
Kun Chi; Stephanie Ness; Tayyab Muhammad; Mohan Raja Pulicharla
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
http://tinyurl.com/m3ux989h
Scribd :
http://tinyurl.com/2kkuhhyk
DOI :
https://doi.org/10.5281/zenodo.10707503
Abstract :
The integration of Artificial Intelligence (AI) in
finance has received an increasing amount of attention in
the industry over the past few years, extending from back-
office trade automation to the more innovative robo-
advisors in the front office. This paper presents a thorough
review of the diverse landscape of AI in finance, covering
both traditional financial operations and the new and
exciting domain of FinTech. Unlike prior reviews that have
been confined to very specific paradigms within AI
methodologies, this review attempts to present a much
more holistic approach to AI in Data Science (AiDS) in
finance, encompassing the last several decades of AiDS
research. The research categorizes, classifies, and carefully
weighs the complete evolution of AiDS in finance. The
research also points out the directions of the research,
encompassing old and new challenges in finance. The
research also critically compares the classical and the
current AI in finance paradigms. In addition to its
capabilities, the article details AI applications across wide-
ranging financial sectors, including market prediction,
fraud detection, algorithmic trading, and consumer
behavior analysis.
The integration of Artificial Intelligence (AI) in
finance has received an increasing amount of attention in
the industry over the past few years, extending from back-
office trade automation to the more innovative robo-
advisors in the front office. This paper presents a thorough
review of the diverse landscape of AI in finance, covering
both traditional financial operations and the new and
exciting domain of FinTech. Unlike prior reviews that have
been confined to very specific paradigms within AI
methodologies, this review attempts to present a much
more holistic approach to AI in Data Science (AiDS) in
finance, encompassing the last several decades of AiDS
research. The research categorizes, classifies, and carefully
weighs the complete evolution of AiDS in finance. The
research also points out the directions of the research,
encompassing old and new challenges in finance. The
research also critically compares the classical and the
current AI in finance paradigms. In addition to its
capabilities, the article details AI applications across wide-
ranging financial sectors, including market prediction,
fraud detection, algorithmic trading, and consumer
behavior analysis.