Evaluating Financial Intelligence in Large Language Models: Benchmarking SuperInvesting AI with LLM Engines

In one sentence

Summary

The paper addresses the lack of systematic evaluation for financial reasoning in large language models, noting that general benchmarks fail to capture the domain-specific requirements of high-stakes investment research. It introduces the AI Financial Intelligence Benchmark (AFIB), a multi-dimensional framework assessing factual accuracy, analytical completeness, data recency, model consistency, and failure patterns. The study positions this evaluation as critical for understanding the reliability of AI systems in professional financial workflows, where numerical precision and coherent synthesis are paramount. The methodology involves evaluating five AI systems—GPT, Gemini, Perplexity, Claude, and SuperInvesting—on a dataset of 71 structured financial analysis queries derived from real-world equity research tasks focused on the Indian capital markets. The dataset includes companies from diverse sectors such as banking, IT, and conglomerates. Ground truth data is sourced from SEBI-regulated filings and annual reports. The evaluation uses a structured scoring rubric for each dimension, including a consistency test where queries are repeated across sessions to measure response stability. Additionally, the study incorporates a deployment dataset of 432 negatively rated responses to analyze real-world failure modes. Results indicate that SuperInvesting achieves the highest aggregate performance, with an average factual accuracy of 8.96/10 and the highest completeness score of 56.65/70, while maintaining the lowest hallucination rate. Retrieval-oriented systems like Perplexity excel in data recency but show weaker analytical synthesis. A key finding is the trade-off between recency and analytical depth, with hybrid architectures appearing most effective. The study highlights that financial intelligence is multi-dimensional, and systems combining structured data access with reasoning capabilities provide the most reliable performance for complex investment research.

Research problem

Existing LLM benchmarks primarily assess general reasoning or isolated financial tasks, failing to evaluate the comprehensive analytical workflows required in professional investment research. There is a need for a systematic evaluation framework that captures the multi-dimensional nature of financial intelligence, including numerical accuracy, analytical depth, and data recency, to assess the reliability of AI systems in high-stakes financial decision-making.

Core contributions

• Introduction of the AI Financial Intelligence Benchmark (AFIB), a multi-dimensional evaluation framework for financial analysis capabilities.
• Development of a curated evaluation dataset comprising 71 structured financial analysis queries based on real-world equity research tasks.
• Incorporation of a deployment dataset of 432 real-world failure cases to analyze empirical failure modes in production financial AI.
• Comparative evaluation of five leading AI systems across five distinct capability dimensions relevant to institutional investment research.
• Identification of a structural trade-off between data recency and analytical reasoning depth in current AI architectures.

Method

• Multi-dimensional scoring framework assessing factual accuracy, analytical depth, completeness, recency, and consistency.
• Factual accuracy calculated as the ratio of correct numerical claims to total verifiable claims.
• Completeness score derived from the ratio of addressed analytical components to required components.
• Consistency measured by the variance of numerical outputs and analytical verdicts across repeated queries.
• Hallucination rate defined as the complement of factual accuracy, categorized by error type.
• Composite benchmark score computed as a weighted aggregation of normalized dimension scores.
• Evaluation conducted under default configuration settings to replicate typical practitioner environments.

Results & findings

• SuperInvesting achieves the highest aggregate performance with an average factual accuracy of 8.96/10.
• SuperInvesting records the highest completeness score of 56.65/70 among evaluated systems.
• SuperInvesting demonstrates the lowest hallucination rate, indicating strong factual reliability.
• Perplexity leads in data recency due to live information access but shows weaker analytical synthesis.
• GPT exhibits the highest hallucination frequency, producing substantially more fabricated numerical values.
• Claude shows strong hallucination resistance but relies on conservative refusal strategies, limiting coverage.
• Gemini demonstrates strong analytical reasoning but lacks access to real-time financial information.
• A clear trade-off exists between retrieval capability (recency) and analytical reasoning depth.

Data & experiments

• Dataset of 71 structured financial analysis queries covering corporate metrics, macroeconomic developments, and conglomerate analysis.
• Focus on Indian listed equities across multiple sectors including banking, IT, consumer goods, and renewable energy.
• Ground truth data sourced from SEBI-regulated filings, stock exchange disclosures, and company annual reports.
• Evaluation of five AI systems: GPT, Gemini, Perplexity, Claude, and SuperInvesting.
• Consistency testing via repeated queries across independent sessions to measure response stability.
• Analysis of 432 negatively rated responses from a production financial AI deployment.

Limitations & caveats

• The evaluation is focused on the Indian capital markets, which may limit generalizability to other regions.
• The dataset size of 71 queries may be considered small for comprehensive benchmarking.
• The study evaluates specific AI systems rather than open-source models, limiting reproducibility.
• The consistency test relies on repeated queries, which may not capture all forms of model instability.
• The scoring rubric for analytical depth involves subjective human judgment, potentially introducing bias.
• The study does not evaluate the cost or latency of the AI systems, which are practical deployment constraints.

Curator notes

Discovery status: round1_promoted_seed_for_round2. Source links: A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges; BloombergGPT: A Large Language Model for Finance; ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering; Fin-RATE: Financial Report Analytics and Tracking Evaluation for Large Language Models; FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark for Evaluating LLMs; FinBERT: Financial Sentiment Analysis with Pre-trained Language Models; F