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About this paper
Akshay Gulati, Kanha Singhania, Tushar Banga, Parth Arora, Anshul Verma, Vaibhav Kumar Singh, Agyapal Digra, Jayant Singh Bisht and 3 more
The study introduces the AI Financial Intelligence Benchmark (AFIB), a multi-dimensional framework evaluating large language models across accuracy, completeness, recency, consistency, and analytical depth for financial analysis. Benchmarking various AI systems, including SuperInvesting, GPT, Gemini, Perplexity, and Claude, revealed SuperInvesting achieved the highest aggregate performance while highlighting a trade-off between data recency and analytical depth in other models.
Key insights
- A structural trade-off appears to exist between the ability to incorporate recent data (recency) and the depth of analytical reasoning in many current AI systems.
- Confident numerical hallucination by LLMs poses a significant, quantifiable risk in financial analysis, potentially more dangerous than a model's refusal to answer.
- Effective financial intelligence in AI is a multi-dimensional capability, arising from the interaction of retrieval, reasoning depth, and numerical reliability.
Summary by alphaXiv.
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