A Comprehensive Review of Gen AI Agents: Applications and Frameworks in Finance, Investments and Risk Domains

In one sentence

Summary

This paper provides a comprehensive review of generative AI agent frameworks and their specific applications within the financial services sector, including trading, investment analysis, and risk management. It positions agentic AI as the next evolutionary step beyond standard LLMs, emphasizing the shift toward autonomous, self-reasoning systems capable of complex, multi-step financial tasks. The authors synthesize insights from academic research, industry reports, and technical documentation to map the current state of the art, focusing on the transition from single-agent tools to collaborative multi-agent systems that can handle sophisticated financial workflows and decision support scenarios. The methodology involves a comparative analysis of prominent open-source and enterprise frameworks, including LangGraph, CrewAI, AutoGen, and LlamaIndex, alongside cloud platforms like IBM watsonx and NVIDIA NIM. The paper outlines a proposed three-layer multi-agent architecture comprising data, agent, and orchestration layers, supported by theoretical foundations such as verbal reinforcement learning and market microfoundations. It evaluates these systems through proposed scenarios like portfolio optimization, fraud detection, and algorithmic trading, using metrics such as accuracy, efficiency, and risk-adjusted returns, while benchmarking against traditional rule-based and single-agent baselines. Key findings indicate that specialized agent frameworks can achieve 50-80% productivity gains in financial data tasks, with multi-agent systems showing particular promise in complex domains like fraud detection and algorithmic trading. The paper highlights real-world examples, such as an 80% reduction in data task time and significant workforce efficiency improvements. However, it cautions that successful deployment requires addressing critical challenges in regulatory compliance, risk alignment, and workforce upskilling. The study concludes that while agentic AI offers transformative potential, financial institutions must adopt standardized architectures, robust testing protocols, and hybrid human-AI workflows to mitigate risks and ensure reliable performance in production environments.

Research problem

The paper addresses the lack of a unified understanding of how generative AI agent frameworks can be effectively deployed in financial services. It seeks to categorize existing frameworks, evaluate their suitability for specific financial tasks like trading and risk management, and identify the theoretical and practical challenges associated with implementing multi-agent systems in regulated environments.

Core contributions

• Synthesizes the landscape of Gen AI agent frameworks (LangGraph, CrewAI, AutoGen) and their financial applications.
• Proposes a three-layer multi-agent architecture (Data, Agent, Orchestration) for financial analysis.
• Introduces theoretical foundations for agentic AI in finance, including verbal reinforcement learning and market microfoundations.
• Provides a comparative analysis of open-source and enterprise-grade agent platforms.
• Highlights quantitative productivity gains (50-80%) and specific use cases in trading, risk, and compliance.
• Offers recommendations for deployment, focusing on regulatory compliance, risk alignment, and workforce transformation.

Method

• Literature synthesis of academic, industry, and technical sources.
• Comparative analysis of agent frameworks based on features and financial applicability.
• Proposal of a three-layer architecture: Data, Agent, and Orchestration.
• Definition of theoretical foundations: verbal reinforcement learning, market microfoundations.
• Evaluation strategy using KPIs: accuracy, efficiency, robustness, explainability, risk-adjusted return.
• Benchmarking against rule-based, single-agent, and SOTA baselines.
• Analysis of cloud-native libraries and deployment considerations.
• Review of regulatory and ethical challenges in agentic AI deployment.

Results & findings

• Specialized agent frameworks achieve 50-80% productivity gains in financial data tasks.
• Multi-agent systems show promise in complex domains like algorithmic trading and fraud detection.
• Successful deployment requires addressing workforce upskilling, risk alignment, and regulatory compliance.
• West Monroe’s agent reduces data task time by 80%.
• Capitec Bank employees save over 1 hour per week with AI assistants.
• 45% of firms now use GenAI for risk management.
• Cloud-based agents can reduce infrastructure costs by 30-40% compared to on-premise solutions.
• Theoretical models include verbal reinforcement learning and market microfoundations for price formation.

Data & experiments

• Synthesizes insights from academic research, industry reports, and technical blog posts.
• Analyzes prominent frameworks: LangGraph, CrewAI, AutoGen, LlamaIndex, IBM watsonx, NVIDIA NIM.
• Proposes evaluation scenarios: portfolio optimization, fraud detection, algorithmic trading, financial news analysis.
• Defines metrics: accuracy, efficiency, robustness, explainability, risk-adjusted return.
• Benchmarks against rule-based systems, single-agent LLMs, and state-of-the-art financial models.
• Cites industry examples: Capitec Bank, West Monroe, JPMorgan, Interface.ai, Zetaris.

Limitations & caveats

• Lacks empirical validation of the proposed architecture with real trading data.
• Relies heavily on industry reports and anecdotal evidence for productivity claims.
• Does not provide detailed code or open-source implementations of the proposed system.
• Theoretical models are presented without extensive quantitative testing.
• Limited discussion on specific financial datasets used for training or evaluation.
• Deployment recommendations are high-level and lack technical depth on integration.

Curator notes

Discovery status: round3_promoted_seed_for_round4. Source links: Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research; FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Making