Structured Data Lifts AI Agent Accuracy in Finance: Inside Daloopa's 500-Question Benchmark
Daloopa's new benchmark puts numbers behind what many teams feel every day: AI agents only perform as well as the data they can pull. In 500 real-world finance questions, accuracy jumped to roughly 90%-an improvement of up to 71 percentage points-when agents retrieved from a structured, auditable database instead of the public web.
For finance, that delta shows up as fewer model misses, cleaner comps, and faster turnarounds. It also explains why agents still stumble in high-stakes workflows despite better reasoning: the inputs are noisy.
What Daloopa Tested
The study evaluated three LLM-powered agent systems-OpenAI's Agents SDK with GPT-5.2, Anthropic's Agent SDK with Claude Opus 4.5 (see Claude AI Training), and Google's ADK with Gemini 3 Pro-on "FinRetrieval" tasks. Daloopa reports that all three performed far better when pulling from a structured financial database than from public web sources.
Key finding: accuracy rose to about 90% with structured data. The jump reached as high as 71 percentage points depending on the question set.
Where Accuracy Still Breaks
Moving from ~90% to 99%+ isn't just about bigger models. The report points to infrastructure-especially fiscal calendars and naming conventions-as the friction.
- Fiscal year alignment: U.S. companies were easier for agents, likely because many use December year-ends. Non-December year-ends added confusion.
- Naming conventions: Inconsistent entity names, tickers, and identifiers tripped up retrieval and mapping.
Why This Matters for Your Desk
- Fewer restatements: Reliable retrieval reduces back-and-forth to verify numbers.
- Speed with control: Structured, linked sources keep analysts moving without sacrificing audit trails.
- Repeatability: Standardized calendars and naming make pipelines consistent across sectors and geographies.
Practical Moves to Get to 99%+
- Route agents to auditable, structured data first. Use a connector or protocol that enforces source-granular retrieval. Consider Model Context Protocol (MCP)-style integrations.
- Normalize fiscal calendars. Map non-December year-ends and standardize quarter boundaries before the agent sees the data.
- Standardize entity naming. Maintain a dictionary for tickers, legal names, and common aliases.
- Require source links in every answer. If a figure can't be traced to an original filing, it doesn't ship.
- Measure with a house test set. Keep a rolling FinRetrieval pack of questions tied to your coverage universe.
How Daloopa Fits
Daloopa provides structured, audit-ready financial data built for AI and agent workflows. The platform covers 5,000+ public companies globally and delivers up to 10x more data points per company than other providers, with each datapoint hyperlinked to its original source.
Integrations include a Model Context Protocol connector with OpenAI and a partnership with Anthropic's Claude for Financial Services. Teams use Daloopa's MCP integration for tasks like spotting quarter-over-quarter inflections, running scenario analyses, and generating equity research with full source traceability.
"Accuracy in AI-driven finance isn't just a model problem, it's a data access problem," said Thomas Li, CEO of Daloopa. "Our latest benchmark research underscores the necessity of equipping AI agents with high-quality data for FinRetrieval."
Details and demo: daloopa.com
If You're Building AI Capability In-House
Pair data infrastructure with targeted upskilling so your team writes better retrieval prompts, sets stronger guardrails, and evaluates outputs with discipline. A curated starting point: AI tools for finance.
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