A Literature Review of Gen AI Agents in Financial Applications: Models and Implementations

This paper presents a structured literature review of AI agents in financial applications, focusing on their implementation frameworks, model architectures, and future directions. The review categorizes AI agents into five key domains: financial risk management, investment strategies, fraud detection, stock market analysis, and customer support. By analyzing measurable outcomes, the paper highlights significant contributions (from the literature) of AI agents, including a 25% improvement in risk model accuracy, a 20% reduction in loan defaults, and a 40% decrease in false-positive fraud detections. Additionally, it identifies gaps in scalability, interpretability, and adaptability, proposing future research into hybrid models and ethical integration. This review provides actionable insights into the transformative potential of AI agents to reshape financial ecosystems and enhance decision-making capabilities. Quantitative outcomes are highlighted to showcase the impact of these agents across each domain. Also, this paper discusses and compare the modeling implementation and models with the financial domain using van diagrams, heat maps and radars. And finally proposes how to address the gaps in the current literature. This work uses research and white-papers only from the last six months making it one of the most current works in the subject of Gen AI.