A Literature Review of Gen AI Agents in Financial Applications: Models and Implementations
Review of generative AI agents in finance, detailing frameworks and benefits in risk management, investment, fraud detection, and customer support.
What it examines
This study reviews recent literature on generative AI agents in finance, covering risk management, investment strategies, fraud detection, stock analysis, and customer support. It categorizes research outcomes, quantifies benefits like improved accuracy and reduced inefficiencies, and identifies gaps in scalability and explainability, guiding future research and practical applications.
What it concludes
This study shows generative AI agents enhance financial decision-making, benefiting risk management, investment optimization, fraud detection, and customer service. While achieving higher accuracy and efficiency, further research should address model interpretability, scalability, and real-time adaptability. Applications include credit scoring, trading, and customer support, underscoring AI's potential to reshape financial services.
Evidence objects
A sixmonth review finds generative AI transforms finance by enhancing risk models, investment decisions, fraud detection, with $25%$ improved accuracy, $20%$ reduction in defaults, and $40%$ fewer fraud alerts overall.
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Researchers categorize AI frameworks from GANs to multi-agent reinforcement learning, clarifying model strengths and limitations. They propose hybrid architectures and adaptive, interpretable models for real-time risk assessment in dynamic markets.
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Using heat maps, radar charts, and van diagrams from cloud platforms, the study quantifies AI performance, revealing challenges in scalability, interpretability, and adaptability while outlining directions for future financial solutions.
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The paper compiles recent research on generative AI agents in finance, emphasizing risk management improvements via quantitative illustrations. Its structured synthesis provides practical value and fresh insights by aggregating diverse studies. As a literature review lacking groundbreaking methods, its timeliness and presentation make it a compelling resource for financial AI.
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Raw abstract and provenance
- … AI agents are transforming the financial landscape by … risk management, fraud detection, investment strategies, … [7] “Artificial intelligence and machine learning in financial …
Source row: 44 · abstract type: snippet