Agentic AI Finance Benchmark: FinRobot vs FinRL vs FinGPT
79% of executives report that their companies have started adopting AI agents, yet only 34% are currently using them in accounting and finance.1
We conduct a benchmark on 3 agentic AI finance tools tailored for financial workflows. Results suggest that
- FinGPT appears better suited for financial statement analysis,
- FinRobot shows relative strength in valuation tasks, and
- FinRL underperformed in both applied areas despite matching others in conceptual finance.
Along with benchmark details, see use cases of AI agents in finance and challenges of using agentic AI in financial operations:
Agentic finance benchmark
Results
The findings highlight several important patterns:
- Conceptual knowledge: All three tools performed equally well (88%), suggesting strong foundational finance knowledge across platforms.
- Financial statement analysis: Significant variation was observed. FinGPT achieved the highest score (86%), while FinRL lagged considerably (29%). This suggests FinGPT has stronger capabilities in handling tabular financial data and accounting-based calculations.
- Valuation: FinRobot outperformed others (75%), indicating better performance in applying quantitative finance models such as dividend discount and bond pricing. In contrast, FinRL showed limited ability in this area (25%).
- Average performance: FinGPT scored highest overall (79%), followed by FinRobot (74%). FinRL was less consistent, with a total score of 53%.
From a workflow and organizational perspective:
- FinGPT:2 With its strong performance in financial statement analysis (86%), FinGPT is well-suited for workflows involving reporting, accounting, and controllership functions. It can assist finance teams with budget variance analysis, quarterly reporting, and financial health diagnostics, making it a good fit for corporate finance and accounting teams.
- FinRobot:3 By demonstrating superior accuracy in valuation tasks (75%), FinRobot is more applicable to investment analysis, equity research, and corporate development workflows. It can support teams in M&A evaluations, capital budgeting, and investment decision-making, making it valuable for investment banking, strategy, and financial planning & analysis (FP&A) teams.
- FinRL:4 Although FinRL lagged in applied finance tasks, its equal performance in conceptual knowledge (88%) indicates potential as a training, education, or decision-support tool. It may be better suited for academic use, finance training programs, or teams exploring reinforcement learning approaches in experimental settings, rather than direct financial analysis.
Methodology
To evaluate the effectiveness of agentic AI tools in finance, three platforms, FinRobot, FinGPT, and FinRL, were tested. The benchmark consisted of 9 structured finance questions covering a range of domains:
- Conceptual/theoretical finance knowledge – Questions that assess understanding of fundamental finance concepts, such as capital budgeting, financing decisions, and cash flow implications.
- Financial statement analysis – Exercises requiring calculations and interpretations based on income statements and balance sheets, including net working capital, book vs. market value, and performance metrics such as EVA, ROC, and ROE.
- Valuation – Equity and bond valuation questions, including dividend discount models, time value of money, and bond pricing under different credit ratings.
- Average performance – Based on the tasks
To ensure a standardized testing environment across all tools:
- The connections were made to GPT-3.5 turbo through the OpenRouter API.
- Each tool was asked the same set of questions.
- Responses were then evaluated for accuracy, completeness, and correctness of financial reasoning. A 1% error margin was applied when assessing numerical responses to account for minor rounding differences.
Scores were aggregated to provide both category-level and overall performance results.
How agentic AI systems are used in financial operations
Agentic AI is increasingly shaping financial operations by automating decision-making and improving efficiency. These intelligent systems are used across banking, corporate finance, and capital markets to address operational challenges, manage risk, and enhance customer service.
Source PwC5
1. Fraud detection and risk management
Use Case: Monitoring transactions and behavioral patterns in real time.
Benefit: Detects suspicious activity instantly, reducing potential financial losses.
Transformation: Agents continuously scan transactions, flag anomalies, and take immediate action such as pausing accounts or notifying compliance teams. This closes the gap between detection and response, moving from reactive to proactive risk management.
2. Credit assessment and lending
Use Case: Evaluating borrower creditworthiness and adjusting lending criteria.
Benefit: Improves accuracy and inclusivity of lending decisions.
Transformation: AI agents analyze real-time financial indicators and adjust assessments dynamically. This allows teams to extend credit more efficiently while maintaining risk control.
3. Compliance automation and regulatory monitoring
Use Case: Ensuring adherence to policies, regulations, and internal controls.
Benefit: Reduces compliance errors and supports audit readiness.
Transformation: Agents automatically validate documents, cross-check regulatory lists, and maintain continuous audit trails. Updates to rules propagate instantly, making compliance proactive rather than reactive.
4. Financial analysis and accounting
Use Case: Revenue recognition, expense management, and reporting.
Benefit: Increases reporting accuracy and reduces manual effort.
Transformation: Agentic AI consolidates data, identifies discrepancies, and generates audit-ready reports. It monitors transactions and contracts continuously, enabling faster and more reliable accounting workflows.
5. Liquidity and portfolio management
Use Case: Managing cash flow, reallocating capital, and rebalancing portfolios.
Benefit: Enhances operational efficiency and decision speed.
Transformation: AI agents monitor cash positions, market conditions, and exposure thresholds in real time. They can initiate internal transfers, rebalance assets, or escalate exceptions immediately, replacing slow batch processes with continuous, data-driven action.
6. Intelligent document processing (IDP)
Use Case: Extracting insights from unstructured text like contracts, filings, and news.
Benefit: Accelerates decision-making and reduces the risk of missing critical information.
Transformation: AI agents classify, summarize, and retrieve relevant data automatically, enabling faster investment recommendations and operational decisions without manual document review.
7. Customer service automation
Use Case: Handling inquiries, processing forms, and supporting account management.
Benefit: Reduces operational costs, ensures 24/7 availability, and minimizes human error.
Transformation: Agentic AI triages requests automatically, escalates complex cases with full context, and provides personalized guidance. Employees can focus on high-value, judgment-based complex tasks instead of routine processing.
6 challenges of agentic AI in financial services
Agentic AI brings powerful capabilities to finance, but it also introduces new risks and complexities. Autonomous systems do not remove responsibility, they shift how and where it must be managed.
Privacy and security
AI agents handle sensitive financial and personal data. They must follow strict privacy rules, such as GDPR and CCPA. Clear permissions, encryption, and secure data handling are essential. Autonomy does not mean ungoverned. Weak security can put both the bank and customers at risk.
Human oversight
Delegating routine tasks to AI changes responsibility rather than removing it. Human teams must supervise exceptions, review ethical concerns, and refine policies. AI works best when escalation paths are clear and accountability is defined.
Bias and explainability
AI learns from historical data. If that data is biased or incomplete, AI decisions can be unfair. Banks should:
- Use diverse, representative datasets.
- Implement explainable AI models that can justify decisions.
- Conduct regular audits and fairness testing.
Transparent decision logs and predictable behavior are critical for trust.
Regulatory compliance
Agentic AI raises new compliance questions:
- Who is responsible for autonomous decisions?
- Can the system explain why it acted a certain way?
- How can the bank demonstrate compliance when AI takes the lead?
In the European Union, the EU AI Act (Regulation (EU) 2021/0106) sets strict requirements for high-risk AI systems, including those used in finance.6 Banks must ensure AI systems are traceable, auditable, and accountable. This includes maintaining detailed logs of decisions, performing risk assessments, and demonstrating that human intervention exists for critical processes.
In the United States, there is no single comprehensive AI law yet. However, banks must follow guidance from multiple authorities:
- The Office of the Comptroller of the Currency (OCC) and Federal Reserve provide model validation and governance expectations for AI in banking.7
- The NIST AI Risk Management Framework offers voluntary guidance on AI trustworthiness, accountability, and risk assessment.8
Maintaining a “human-in-the-loop” model for critical decisions and keeping detailed records helps meet these regulatory requirements. Staying updated with evolving laws is essential for safe and compliant use of agentic AI.
Organizational and cultural change
AI changes team roles and may cause a cultural shift. Employees who handled repetitive tasks or data entry may need to guide, monitor, or train AI agents. Organizations should provide reskilling, clear role definitions, and ongoing support.
Governance and risk management
Strong governance is essential for safe, scalable AI. This includes:
- Data protection and privacy controls
- Ethical oversight and human supervision
- Continuous monitoring for bias and errors
- Clear accountability structures
When approached deliberately, these measures allow financial institutions to harness agentic AI effectively while minimizing risks.
FAQs
Further readings
- Top AI Financial Research Platforms for Investors
- AI Excel Tools to Boost Productivity
- AI-Based Stock Trading: Which Gen AI Tool Is Better
Reference Links
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