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Accuracy Gains in Financial Research: Daloopa’s Groundbreaking Findings

Research Overview

Date and Location
NEW YORK, Feb. 10, 2026 /PRNewswire/ — Daloopa, recognized as a leader in financial data for the agentic era, has unveiled its recent research report titled Benchmarking AI Agents on Financial Retrieval. This extensive study provides valuable insights into the performance of leading AI-powered systems when tasked with answering real-world financial questions.

Key Findings on AI Accuracy

Daloopa’s research involved testing 500 real-world finance questions, demonstrating significant accuracy gains among agents that utilized a structured, auditable financial database. Remarkably, the accuracy of AI agents improved by up to 71 percentage points, soaring to an impressive 90% accuracy. This advancement underscores the critical importance of employing reliable data sources over traditional public web-sourced information, which often lacks the necessary reliability.

Understanding AI Agent Performance

AI agents are fundamentally limited by the quality of data they can access. Enhanced by Daloopa’s research, it is clear that the use of structured databases can drastically improve the results of AI systems. The study prominently featured three leading systems: OpenAI’s Agents SDK with GPT-5.2, Anthropic’s Agent SDK with Claude Opus 4.5, and Google’s ADK with Gemini 3 Pro. Each of these models demonstrated increased capabilities in financial information retrieval, closely highlighting the difference in accuracy when comparing structured sources to the less reliable public web.

The Path to Higher Accuracy

While the overall accuracy of financial data retrieval reached around 90%, Daloopa’s findings indicate that achieving even higher accuracy—exceeding 99%—requires robust infrastructural improvements. These enhancements aim to resolve common issues found in data handling, such as inconsistencies in fiscal calendars and varying naming conventions for financial terms. For instance, the study found that U.S.-based companies generally achieved higher accuracy due to their common December fiscal year-end, contrasting with non-U.S. companies that adopt diverse fiscal year-end dates.

Daloopa’s Solution to Infrastructure Challenges

To address these infrastructural challenges, Daloopa offers a solution rich in structured, audit-ready financial data crafted specifically for AI and agentic workflows. Covering more than 5,000 public companies worldwide, Daloopa supplies an impressive volume of data—up to ten times more data points per company than its competitors. Each of these data points is hyperlinked directly to its original sources, increasing the overall auditability—a crucial factor for users in financial sectors.

CEO Insights

"Our benchmark research highlights the urgent need for AI agents to have access to high-quality data tailored for financial retrieval tasks," commented Thomas Li, CEO of Daloopa. "The pursuit of accuracy in AI-driven finance transcends mere modeling; it is fundamentally a matter of data access and infrastructure. At Daloopa, we build solutions that empower our clients by overcoming their most substantial challenges and providing a reliable data foundation for AI and agentic workflows in finance."

Collaborations and Future Prospects

Daloopa’s reputation as a trusted partner from leading global AI companies is further bolstered by its recent integrations with pioneering AI platforms. The introduction of a Model Context Protocol (MCP) connector with OpenAI enriches the workflows of ChatGPT users with enhanced data accessibility. In addition, Daloopa has established a similar partnership with Anthropic’s Claude, focusing on financial services. The capabilities of Daloopa’s MCP extend to a myriad of analytical AI workflows, from hedge funds identifying quarter-over-quarter changes to equity researchers generating reports with complete source traceability.

The Role of AI legalese decoder

In this complex landscape of financial data retrieval, the AI legalese decoder serves as a crucial resource. As the intricacies of financial documentation can often lead to misinterpretations, the AI legalese decoder simplifies the language, ensuring that users can effortlessly comprehend essential legal and financial information. Thus, when combined with Daloopa’s structured data, it enables financial professionals to navigate challenges with clarity and precision. With both these powerful tools at their disposal, users can expect enhanced reliability and accuracy in their financial research and decision-making processes.

Conclusion

The findings from Daloopa’s research not only illuminate the importance of accuracy in financial AI retrieval but also signify a monumental leap toward creating a more robust financial data ecosystem. With strategic partnerships and advanced data solutions, Daloopa is uniquely poised to transform how financial research is conducted, empowering agents and financial professionals alike.

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