FinGAIA: AI Benchmark in Finance

• FinGAIA is an end-to-end benchmark that evaluates AI performance across 407 tasks in multi-step financial workflows.
• It systematically assesses AI capabilities through tiered scenarios, revealing significant gaps compared to human experts.
• The benchmark highlights AI limitations in cross-modal integration, financial terminology, and process orchestration.

FinGAIA is an end-to-end benchmark designed to systematically evaluate the practical abilities and limitations of AI agents in high-fidelity financial workflows. Comprised of 407 carefully constructed tasks across seven major financial sub-domains—securities, funds, banking, insurance, futures, trusts, and asset management—FinGAIA probes not only domain knowledge but also the multi-step, multi-tool collaboration capabilities required for effective deployment in real-world financial operations. The benchmark adopts a hierarchical scenario structure reflecting escalating complexity, and results indicate a considerable lag between even the best-performing AI agents and human experts on core industry-relevant competencies, revealing substantial gaps and guiding future research in financial AI (Zeng et al., 23 Jul 2025).

1. Benchmark Structure and Task Taxonomy

FinGAIA organizes its 407 tasks across three scenario depths, each aiming to emulate increasingly complex financial workflows:

• Level 1: Basic Business Analysis (89 tasks)
  • Customer Data Analytics (47 tasks)
  • Transaction Risk Assessment (42 tasks)
• Level 2: Asset Decision Support (185 tasks)
  • Financial Data Statistics (101 tasks)
  • Loan Credit Analysis (43 tasks)
  • Fraud Detection Analysis (41 tasks)
• Level 3: Strategic Risk Management (133 tasks)
  • Risk Management Analysis (42 tasks)
  • Portfolio Fund Allocation (40 tasks)
  • Market Trend Forecasting (51 tasks)

This architecture ensures both fine-grained evaluation of atomic analytical abilities and realistic assessment of multi-hop reasoning, domain adaptation, and complex workflow orchestration. Each task includes authentic business attachments (e.g., Excel, PDF, images, audio) to enforce multimodal understanding and tool-use.

2. Evaluation Protocols and Metrics

FinGAIA employs a strict zero-shot evaluation regime in which agents interact only with the task prompt and its supporting attachments—without access to demonstrations or task-level fine-tuning. The benchmark includes ten mainstream AI agents (nine proprietary web-based and one open-source local agent). Responses are manually validated and checked via an "LLM-as-Judge" for consistency.

• Primary Metric:

$$\text{Accuracy} = \frac{\#\,\text{correct answers}}{\#\,\text{total tasks}} \times 100\%$$

Correct answers exactly matching reference values, format, and semantics receive one point; incorrect responses receive zero. Unassessable answers (due to file-type issues or blank submissions) are excluded from the denominator.

• Secondary Metric:

Weighted Average (WA) across scenario depths, adjusting for variable sub-scenario sizes.

Manual review ensures both quantitative rigor and the ability to capture subtle semantic or process-based misalignments that automated techniques may overlook (Zeng et al., 23 Jul 2025).

3. Agent Performance and Comparative Analysis

Results for each agent are reported across eight sub-scenarios and summarized relative to human expert and non-professional baselines.

Agent	WA Accuracy (%)	Comment
ChatGPT (DeepResearch)	48.9	Highest agent score, ≈35 points below experts
Perplexity (DeepResearch)	37.0	Second tier, marked performance drop
Cashcat (DeepResearch)	33.2	Similar to Perplexity, lagging substantially
OWL (Open Source)	21.8	Open-source baseline, lowest performance among agents
Human Experts (Finance PhDs)	84.7	Reference standard
Non-Experts (Novices)	46.9	Matches/underperforms best agents in select scenarios

ChatGPT leads with 48.9% overall accuracy, outperforming non-professional users on a 50-question subset but trailing PhD experts by approximately 35 percentage points. Task complexity strongly modulates performance: mid-level asset decision support tasks show higher agent proficiency (e.g., 58.1% for Loan Credit Analysis), while high-complexity scenarios such as Market Trend Forecasting see a pronounced decline (e.g., 37.3% for ChatGPT).

4. Characteristic Failure Patterns

Error analysis via stratified sampling of incorrect responses exposes five recurring agent failure modes:

1. Cross-modal Alignment Deficiency: Inability to correctly integrate data from images, PDFs, audio, or tabular attachments. A common manifestation is the failure to extract crucial tabular data embedded within non-text modalities.
2. Financial Terminological Bias: Misinterpretation or inappropriate substitution of specialized finance terms and regulatory language; typical errors include inaccurate mapping of model factor definitions.
3. Operational Process Awareness Barrier: Errors arising from misunderstanding or misapplying standard business workflows, including inversion of regulatory steps or incorrect calculation order.
4. Hallucinatory Financial Reasoning: Unfounded claims, fictitious regulations, or unsound data generation, particularly in regulatory questions or compliance tasks.
5. Entity-Causation Misidentification: Faulty causal reasoning linking business entities, notably confusing correlation with causation or failing to map real business logic chains.

The distribution of error types varies across agent architectures, but cross-modal alignment and domain terminology errors are the most systematic and widespread in high-fidelity scenarios (Zeng et al., 23 Jul 2025).

5. Task Complexity and Multi-Tool Usage

FinGAIA quantifies procedural complexity by measuring the average number of tool invocations per scenario:

Scenario Level	Avg. Tool Invocations
Level 1	~2.1
Level 2	~2.4
Level 3	~2.6

This progression reflects the benchmark's deliberate emulation of real-world financial analysis pipelines, from atomic data analytics to multi-modal, multi-step strategic planning. It highlights that agent weaknesses become more pronounced as tasks demand orchestrated use of heterogeneous information channels and external tools.

6. Significance, Limitations, and Future Trajectories

FinGAIA demonstrates that, as of its publication, state-of-the-art AI agents are not yet competitive with domain experts on realistic financial workflows—especially those requiring multimodal integration, rigorous terminological distinction, and precise process awareness. There is a pronounced performance gradient from open-domain LLMs to specialized agents, and none reach expert-level proficiency in nuanced, regulation-driven tasks (Zeng et al., 23 Jul 2025).

Identified research priorities include:

• Enhancement of cross-modal processing capabilities
• Improved calibration to financial domain ontology and workflows
• Strategies mitigating hallucinatory responses in high-stakes decision contexts
• Few-shot and adaptive benchmarks to assess dynamic reasoning under market volatility

Future benchmarks are encouraged to expand into adaptive, time-sensitive scenarios reflecting real market dynamics and to weight scenario complexity according to business impact.

7. Availability and Community Impact

FinGAIA offers a public partial dataset and supporting validation scripts aimed at fostering transparent benchmarking and reproducibility (see https://github.com/SUFE-AIFLM-Lab/FinGAIA). Its rigorous structure and empirical grounding position it as a foundational resource for driving the next generation of finance-focused AI research, targeted capability diagnosis, and benchmarking of agentic reasoning, orchestration, and compliance in the financial domain (Zeng et al., 23 Jul 2025).