Evaluating Financial Intelligence in Large Language Models
Benchmarking SuperInvesting AI with LLM Engines
This paper introduces the AI Financial Intelligence Benchmark (AFIB) to assess financial analysis capabilities of AI systems, evaluating GPT, Gemini, Perplexity, Claude, and SuperInvesting across multiple dimensions. SuperInvesting achieves the highest aggregate performance.
Key Findings for Enterprise AI Adoption
Our research reveals a multi-dimensional view of financial intelligence in AI systems, emphasizing that no single architectural paradigm currently dominates all financial analysis tasks. Hybrid systems that combine structured data access with analytical reasoning show the most reliable performance for complex investment research.
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Avg Factual Accuracy
0
Avg Completeness Score
Low
Hallucination Rate
Deep Analysis & Enterprise Applications
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Factual Accuracy
Analytical Depth
Data Recency
Financial analysis demands high numerical precision. Our benchmark measured factual correctness against verified ground truth, identifying instances of hallucination where models generated unverified numerical values.
SuperInvesting achieved the highest factual accuracy (8.96/10) and lowest hallucination rate. Retrieval-oriented systems like Perplexity performed well on data recency, but reasoning-oriented models struggled with numerical precision.
This dimension assessed models' structured reasoning, integration of financial variables, application of valuation frameworks, and awareness of sector-specific dynamics.
SuperInvesting consistently demonstrated strong analytical depth, linking financial metrics to business drivers and macroeconomic context, while other models often relied on isolated figure retrieval.
Financial markets are sensitive to recent developments. This dimension evaluated whether models incorporated current financial information, such as quarterly earnings and policy decisions.
Perplexity excelled in data recency due to its live information access. Gemini showed weaker results in recency-dependent questions, indicating a trade-off between real-time data and analytical synthesis.
84.1%
SuperInvesting Overall Score
Investment Research Workflow
Data Collection & Verification
→
Financial Statement Analysis
→
Valuation Modeling
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Macroeconomic Integration
→
Investment Thesis Generation
→
Risk Assessment
| Dimension | 1st Place | 2nd Place |
|---|---|---|
| Accuracy | SuperInvesting | Gemini |
| Completeness | SuperInvesting | Gemini |
| Data Recency | Perplexity | SuperInvesting |
Case Study: Enhancing Equity Research with AFIB
A leading institutional investor leveraged the AFIB framework to evaluate their existing AI tools. The benchmark revealed a critical gap in real-time data integration for their reasoning-focused AI, leading to outdated insights. By identifying this, they were able to prioritize a hybrid architecture that combined their strong analytical engine with live market data feeds, resulting in a 25% improvement in analyst productivity and a 15% reduction in time-to-decision for investment recommendations.
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Implementation Roadmap
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01 Discovery & Strategy
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02 Pilot & Customization
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03 Integration & Scaling
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