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

2026-03-09

AI Summary

This study addresses the lack of systematic evaluation of large language models’ reasoning capabilities in financial analysis and investment research. It introduces AFIB, the first multidimensional benchmark tailored for investment research, which assesses mainstream AI systems on five dimensions—factual accuracy, analytical completeness, data timeliness, model consistency, and error patterns—using a structured financial question-answering dataset, human evaluations, and automated metrics. Empirical results demonstrate that SuperInvesting achieves the best overall performance, with a factual accuracy score of 8.96/10 and analytical completeness of 56.65/70, while exhibiting the lowest hallucination rate. Although retrieval-augmented systems show advantages in data timeliness, they lag significantly in analytical synthesis capability.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Large language models are increasingly used for financial analysis and investment research, yet systematic evaluation of their financial reasoning capabilities remains limited. In this work, we introduce the AI Financial Intelligence Benchmark (AFIB), a multi-dimensional evaluation framework designed to assess financial analysis capabilities across five dimensions: factual accuracy, analytical completeness, data recency, model consistency, and failure patterns. We evaluate five AI systems: GPT, Gemini, Perplexity, Claude, and SuperInvesting, using a dataset of 95+ structured financial analysis questions derived from real-world equity research tasks. The results reveal substantial differences in performance across models. Within this benchmark setting, SuperInvesting achieves the highest aggregate performance, with an average factual accuracy score of 8.96/10 and the highest completeness score of 56.65/70, while also demonstrating the lowest hallucination rate among evaluated systems. Retrieval-oriented systems such as Perplexity perform strongly on data recency tasks due to live information access but exhibit weaker analytical synthesis and consistency. Overall, the results highlight that financial intelligence in large language models is inherently multi-dimensional, and systems that combine structured financial data access with analytical reasoning capabilities provide the most reliable performance for complex investment research workflows.

Problem

• Financial Intelligence
• Large Language Models
• Benchmarking
• Investment Research
• Financial Reasoning

Innovation

• financial intelligence
• large language models
• evaluation benchmark
• investment research
• hallucination rate