Finance Agent Benchmark: Evaluation & Safety

• Finance Agent Benchmark is a standardized evaluation suite that assesses LLM-powered agents on financial tasks using real-world data and multi-step reasoning.
• It integrates multi-modal artifacts and tool orchestration to simulate dynamic financial workflows, including retrieval, reporting, and compliance checks.
• Empirical findings reveal that even advanced agents struggle with robust accuracy and safety, especially under adversarial and high-stakes scenarios.

A finance agent benchmark is a standardized evaluation suite designed to rigorously and reproducibly assess the end-to-end capabilities, safety, and reliability of LLM-powered agents in complex financial tasks. These benchmarks, which have emerged with the maturation of agentic LLM frameworks, systematically test competencies such as retrieval, numerical reasoning, workflow composition, compliance, and decision-making in realistic, high-stakes financial scenarios. Modern finance agent benchmarks feature multi-modal artifacts, agentic or workflow-oriented protocols, and target both capability (can the agent solve the problem as a human analyst would?) and robustness (does the agent avoid catastrophic or risky outputs under operational and adversarial stress). The field has matured from simple static question-answering to full-stack benchmarks that canvas trading, research, retrieval, reporting, forecasting, regulation, risk, and execution safety.

1. Scope and Defining Attributes

Finance agent benchmarks are characterized by:

• Agentic Evaluation: The agent is assessed as an active planner and actor, not just a generator of answers. Tasks require tool use (e.g., search, parsing, code execution), dynamic data integration, and multi-step reasoning.
• Multi-Domain and Multi-Task Coverage: Tasks span equities, fixed income, derivatives, banking, insurance, ESG, wealth management, and emerging asset classes (e.g., crypto).
• Multi-Modal and Multi-Tool Integration: Benchmarks include messy, enterprise-grade artifacts (spreadsheets, PDFs, emails, charts, databases) to mirror real-world workflows (Dong et al., 15 Dec 2025, Vidgen et al., 20 Jan 2026).
• End-to-End, Process-Oriented Assessment: Evaluation encompasses whole workflows—retrieval, analysis, reporting, and sometimes execution—with fine-grained criteria or checkpoints at each step (Milsom, 1 Dec 2025, Zeng et al., 23 Jul 2025, Yang et al., 9 Jan 2026).
• Dynamic, Live Data and Real-Time Tools: Several benchmarks require agents to fetch live or time-sensitive market data, handle API access, or integrate real-time web search (Wang et al., 31 May 2025, Guo et al., 29 Nov 2025, Hu et al., 16 Sep 2025, Li et al., 2024).
• Robustness and Safety Under Stress: Advanced benchmarks stress test systems under adversarial prompts, data drift, workflow errors, and execution-grounded attacks (Chen et al., 21 Feb 2025, Yang et al., 9 Jan 2026).
• Agent-Level, Multi-Dimensional Metrics: Evaluation protocols extend beyond accuracy—capturing compliance, explainability, risk, comprehensiveness, faithfulness, precision, relevance, completeness, and end-to-end process reliability.

2. Representative Benchmarks and Their Targeted Domains

Comprehensive & General Finance

• Finance Agent Benchmark (FAB): 537 expert-authored real-world research questions, spanning nine categories from simple retrieval to financial modeling and market analysis. Tasks leverage dedicated agentic harness with Google Search and SEC EDGAR access. Rigorous rubric-based scoring and contradiction detection. Even state-of-the-art models are capped below 50% class-balanced accuracy (Bigeard et al., 20 May 2025).
• FinGAIA: 407 tasks across seven sub-domains (securities, funds, banking, insurance, futures, trusts, asset management) in a three-level scenario structure (basic analysis, decision support, strategic risk management). Evaluates agents with zero-shot, multi-modal, multi-tool workflows, showing a 35+ point gap between best agent and human expert (Zeng et al., 23 Jul 2025).
• Finch: 172 composite, enterprise-grade workflows, primarily spreadsheet-centric, sourced from Enron and other financial institutions—testing cross-file reasoning and formulaic logic under real-world messiness (Dong et al., 15 Dec 2025).
• APEX-Agents: 480 “worlds” simulating investment banking, consulting, and legal projects, with multi-application orchestration but no internet access. Primary metric: Pass@1 (full criteria met in one run); top agent score is just 24% (Vidgen et al., 20 Jan 2026).

Specialized Task and Sector Benchmarks

Benchmark	Primary Coverage	Core Task Types
FinReflectKG–EvalBench	Financial knowledge graph extraction (10-K)	(s,r,o) triple extraction; reflection/single/multi-pass; faithfulness/precision/relevance/comprehensiveness (Dimino et al., 7 Oct 2025)
FinAgentBench	Agentic retrieval in QA	Filing-type retrieval + chunk pinpointing in SEC filings; nDCG/MAP/MRR metrics (Choi et al., 7 Aug 2025)
INVESTORBENCH	Multi-asset trading agent	Stock/crypto/ETF environments, full POMDP agent design; risk-adjusted metrics (CR, Sharpe, MDD) (Li et al., 2024)
CryptoBench	Crypto/DeFi agent analysis	Retrieval/prediction, adversarial workflows, dynamic, monthly updating (Guo et al., 29 Nov 2025)
ESGAgent	ESG reporting	3-level hierarchy: atomic QA, multi-step workflow, integrated report synthesis (charts/citations) (Zhao et al., 13 Jan 2026)
FinDeepForecast	Forecasting (macro + corporate)	Live, strictly time-isolated, dual-track recurrent/non-recurrent tasks; >1,300 companies, ~1,400 tasks/10w (Li et al., 8 Jan 2026)
BizBench	Quantitative reasoning	MCQ, span extraction, formulaeval, code generation, program synthesis pipelines (Koncel-Kedziorski et al., 2023)
FinBen	Holistic, multi-task	36 datasets, 24 tasks (IE, QA, forecasting, risk, trading); RAG/agent/trading simulation (Xie et al., 2024)
Finova (Agentar-Fin-R1)	Compliance + agent capabilities	Intent/slot/tool planning, reasoning, safety & compliance, regulatory checks (Zheng et al., 22 Jul 2025)
Wealth-Management Bench	Operational workflow	Workflow reliability and cost metrics in synthetic assistant scenarios (Milsom, 1 Dec 2025)

3. Task Taxonomies and Scenario Structures

Benchmarks employ taxonomy frameworks to ensure coverage and allow interpretable error analysis. Common structures include:

• Financial Process Taxonomies: e.g., FAB’s nine analyst categories (retrieval, modeling, trend analysis, etc.), FinGAIA’s seven sub-domains × 3 difficulty levels.
• Workflow Decomposition: Agentar-Fin-R1 and FAB require agents to plan, invoke tools, and generate outputs for multi-step workflows; acceptance is measured by passing all checkpoint criteria.
• Capability Axes: ESGAgent and FinBen organize tasks along axes of increasing complexity, from foundation skills (extraction, QA) through advanced synthesis and decision-making (forecasting, trading).
• Attack and Safety Dimensions: FinVault structures 31 execution-grounded scenarios with 107 vulnerabilities (privilege, compliance, leakage, etc.), imposing compliance constraints and reporting attack success rates (ASR) (Yang et al., 9 Jan 2026).

4. Evaluation Protocols and Metrics

Modern benchmarks feature multi-dimensional, granular measurement:

• Rubric/LLM-as-Judge: Experts or LLMs score agent outputs with explicit pass criteria, rubric checks, and contradiction detection (FAB, FinResearchBench, FinAgentBench, Finch).
• Multi-Level or Workflow Pass Criteria: Partial credit awarded per subtask (point-accuracy), plus strict pass@1/PassRate for all-or-nothing per-task completion (APEX-Agents, Finch, FinAgentBench, Wealth-Management Bench).
• Compositional Skill Metrics: Logic tree extraction (FinResearchBench), breadth/depth/density scoring, and rule-based compositions.
• Risk-Aware and Robustness Metrics: Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), Maximum Drawdown (MDD), error propagation, prompt sensitivity, and adversarial robustness (Chen et al., 21 Feb 2025).
• Compliance and Safety: Attack Success Rate (ASR), compliance-violation rate, false positive/negative rates. Finesse through “commit-then-justify” protocols and explicit bias controls (FinReflectKG–EvalBench).
• Domain-Adjusted Tolerances: Financial accuracy within set tolerance bands (e.g., ±5% for quote retrieval, tight bounds for regulatory compliance).
• Temporal/Freshness Isolation: Prevents data contamination in live or rolling benchmarks (FinDeepForecast, CryptoBench, FinSearchComp).

5. Experimental Findings and Agent Performance

Key empirical patterns:

• Frontier LLM agents are not yet robust for autonomous financial operations. Even on simplified real-world tasks, pass rates and accuracy remain below 50% for best-in-class closed-source models, and near or below 25% for open-source (Bigeard et al., 20 May 2025, Dong et al., 15 Dec 2025, Vidgen et al., 20 Jan 2026).
• Complex, multi-step, multi-modal workflows (Finch, APEX-Agents, FAB) create severe error accumulation. Performance drops precipitously as tasks exceed two steps (Finch: 44%→23.5% GPT on >2-task workflows).
• Domain-specificity is critical. Generalist LLMs underperform on sector-specific knowledge, regulatory compliance, and safety (Zeng et al., 23 Jul 2025, Zheng et al., 22 Jul 2025, Yang et al., 9 Jan 2026).
• Agentic reasoning and multi-tool orchestration amplify both capability and risk. Reflection/iterative agents improve coverage but can increase susceptibility to subtle attacks or workflow errors unless paired with robust verification (Dimino et al., 7 Oct 2025, Yang et al., 9 Jan 2026).
• Precise compliance and adversarial robustness remain unsolved. Execution-grounded evaluations (FinVault) show ASR up to 50% for leading models in financial sandbox attacks, with domain-tuned safety protocols underdeveloped (Yang et al., 9 Jan 2026).
• Retrieval/prediction dichotomy in analytical tasks. Agents excel in shallow fact lookup but often fail to synthesize or forecast, especially under real-time data constraints (CryptoBench: ΔRP ≈ 35% for top models) (Guo et al., 29 Nov 2025).

6. Limitations, Best Practices, and Future Directions

Common limitations include:

• Synthetic or Small-Scale Data: Many benchmarks seed synthetic data or limit workflow scale (Wealth-Management, FAB, Finch).
• Tool and Platform Heterogeneity: Varying toolchains and data APIs can create reproducibility and comparability issues.
• Incomplete Realism: Simulated sandbox environments do not fully capture live system integration, workflow scale, or social dynamics (Milsom, 1 Dec 2025, Yang et al., 9 Jan 2026).
• Evaluation Gaps: Inadequate or overstrict rubric/pipeline matching can obscure true performance; binary pass/fail masks partial credit.

Best practices and research recommendations:

• Granular, deterministic checkpointing for localized error attribution across workflow steps (Milsom, 1 Dec 2025).
• Cost–accuracy Pareto analysis to surface model efficiency vs performance trade-off (Bigeard et al., 20 May 2025).
• Enriched multimodal/contextual pretraining and advanced tool adapters for robust cross-file and chart/table reasoning (Dong et al., 15 Dec 2025, Zhao et al., 13 Jan 2026).
• Risk-first evaluation approaches prioritizing failure modes, stress scenarios, and safety budgets (Chen et al., 21 Feb 2025).
• Open, extensible, and richly-documented benchmarks to foster reproducibility, cross-model evaluation, and domain expansion (APEX-Agents, FAB, FinBen) (Vidgen et al., 20 Jan 2026, Bigeard et al., 20 May 2025, Xie et al., 2024).

Anticipated directions:

• Enhanced agent self-monitoring, meta-reasoning, and continuous error calibration;
• Expanding coverage to regulatory, ESG, and cross-border compliance;
• Inclusion of dynamic, real-time data feeds and stochastic market perturbations for stress testing;
• Incorporation of multi-agent, collaborative, and human-in-the-loop workflows.

7. Significance Within the Financial AI Ecosystem

Finance agent benchmarks have catalyzed a paradigm shift from evaluating isolated NLP tasks to orchestrating and stress-testing end-to-end AI agents capable of realistic, high-stakes financial decision-making. These benchmarks increasingly set the gold standard for demonstrating progress in safe, reliable, and operationally aligned financial AI, and are now central tools for both academic progress and industrial validation of LLM-based finance applications (Bigeard et al., 20 May 2025, Vidgen et al., 20 Jan 2026, Zeng et al., 23 Jul 2025, Yang et al., 9 Jan 2026).