FinRetrieval: A Benchmark for Financial Data Retrieval by AI Agents
#FinRetrieval #benchmark #financial data #AI agents #retrieval #evaluation #finance #artificial intelligence
📌 Key Takeaways
- FinRetrieval is a new benchmark designed to evaluate AI agents' ability to retrieve financial data.
- The benchmark focuses on assessing performance in the specialized domain of finance.
- It aims to address the need for standardized testing of AI in financial information retrieval.
- The development highlights the growing application of AI agents in financial analysis and decision-making.
📖 Full Retelling
arXiv:2603.04403v1 Announce Type: cross
Abstract: AI agents increasingly assist with financial research, yet no benchmark evaluates their ability to retrieve specific numeric values from structured databases. We introduce FinRetrieval, a benchmark of 500 financial retrieval questions with ground truth answers, agent responses from 14 configurations across three frontier providers (Anthropic, OpenAI, Google), and complete tool call execution traces. Our evaluation reveals that tool availability
🏷️ Themes
AI Benchmarking, Financial Technology
📚 Related People & Topics
AI agent
Systems that perform tasks without human intervention
In the context of generative artificial intelligence, AI agents (also referred to as compound AI systems or agentic AI) are a class of intelligent agents distinguished by their ability to operate autonomously in complex environments. Agentic AI tools prioritize decision-making over content creation ...
Entity Intersection Graph
Connections for AI agent:
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OpenAI
6 shared
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Large language model
4 shared
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Reinforcement learning
3 shared
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OpenClaw
3 shared
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Artificial intelligence
2 shared
Mentioned Entities
Original Source
--> Computer Science > Information Retrieval arXiv:2603.04403 [Submitted on 2 Jan 2026] Title: FinRetrieval: A Benchmark for Financial Data Retrieval by AI Agents Authors: Eric Y. Kim , Jie Huang View a PDF of the paper titled FinRetrieval: A Benchmark for Financial Data Retrieval by AI Agents, by Eric Y. Kim and Jie Huang View PDF Abstract: AI agents increasingly assist with financial research, yet no benchmark evaluates their ability to retrieve specific numeric values from structured databases. We introduce FinRetrieval, a benchmark of 500 financial retrieval questions with ground truth answers, agent responses from 14 configurations across three frontier providers (Anthropic, OpenAI, Google), and complete tool call execution traces. Our evaluation reveals that tool availability dominates performance: Claude Opus achieves 90.8% accuracy with structured data APIs but only 19.8% with web search alone--a 71 percentage point gap that exceeds other providers by 3-4x. We find that reasoning mode benefits vary inversely with base capability (+9.0pp for OpenAI vs +2.8pp for Claude), explained by differences in base-mode tool utilization rather than reasoning ability. Geographic performance gaps (5.6pp US advantage) stem from fiscal year naming conventions, not model limitations. We release the dataset, evaluation code, and tool traces to enable research on financial AI systems. Comments: 26 pages, 2 figures, 16 tables Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2603.04403 [cs.IR] (or arXiv:2603.04403v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2603.04403 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Eric Y. Kim [ view email ] [v1] Fri, 2 Jan 2026 17:51:37 UTC (5,034 KB) Full-text links: Access Paper: View a PDF of the paper titled FinRetrieval: A Benchmark for Financial Data Retrieval by AI Agents, by Eric Y. Kim and Jie Huang View PDF view license C...
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