How to Build Trust in AI for Investment Analysis With Authoritative Data

The biggest risk of AI for financial analysis is not just hallucination. It is a wrong number delivered with high confidence.

For investment managers, that is the trust problem in plain terms. If an AI system cannot be relied on to use the right number, in the right context, and show where it came from, it does not belong in a fiduciary workflow.

Trust remains the main constraint on broader AI adoption in financial analysis as evidenced by a recent Deloitte survey [1]. Trust means you can check the answer, trace it back to the source, and verify it comes from an authoritative source and is being used in the right context. As the head of investment research and data science at an institutional investment manager, I have seen firsthand where these systems can break down and learned what it takes to make them reliable.

In this article, I outline a practical path to building that trust in AI for financial analysis, built on what I call the four pillars: outputs that are accurate, consistent, verifiable, and governable.

Where AI creates value in the investment process

AI in investment research already has practical applications across the process:

  • Screening: finding patterns across large datasets to inform return expectations
  • Underwriting: supporting valuation, risk-reward analysis, and scenario testing
  • Portfolio construction: feeding risk models and helping allocate risk budgets
  • Monitoring and client reporting: accelerating updates, commentary, and performance surveillance

According to a 2024 Mercer survey, 91% of investment managers are currently using or planning to use AI within their investment strategy—but more than half say AI informs rather than determines their final decisions. [2] Used well, AI helps investment teams make better-informed decisions and apply that judgment more consistently. Used poorly, it simply makes errors faster.

If AI creates real value, why is adoption still constrained?

The short answer is that many investment teams still do not fully trust the output. Investment managers have a fiduciary responsibility to make sound decisions, and that responsibility extends to the tools and data they rely on.

In a fiduciary setting, trust does not mean the answer sounds polished. It means you can:

  • check the answer
  • trace it back to the source
  • confirm the number comes from an authoritative source
  • know it is being used in the right context

That is where many AI workflows break down. The more common failure is not AI hallucination in the dramatic sense—it is reference error. Even in controlled benchmarks, best-in-class models still hallucinate at 1.8–3.1% on financial prompts, and real-world rates are likely higher. [3] The system may pull the wrong number, confuse a fiscal period with a filing date, mix adjusted and unadjusted figures, compare mismatched segments, or ignore corporate actions that affect comparability across time. The response may still sound coherent. The failure is not style. It is reference integrity.

A CFA Institute employer survey reinforces the point: 82% of investment industry respondents said the lack of industry-wide standards is preventing faster AI adoption. [4] The trust gap is not just about model quality. It is about data infrastructure.

Why authoritative data is the foundation of trust

Once the problem is defined this way, the next question is what actually makes AI output trustworthy. AI data quality starts with the source.

GAAP financials from official filings are among the most authoritative data sources that financial analysts rely on. They come from regulated disclosures, follow established reporting standards, and are relatively easier to extract and normalize because they are covered by XBRL standards.

But much of the information that actually drives investment insight is not covered by those standards. For example:

  • In REITs: portfolio holdings, asset valuations, and transaction activity
  • In other sectors: operating KPIs such as booking nights, ecommerce sales, new listings, utilization, or regional performance trends

When those data points are not covered by consistent standards, they are harder to source, harder to compare, and harder for AI systems to use correctly.

What authoritative data must contain: semantic context, not just values

Even when the source is authoritative, AI still needs enough semantic context to use the data correctly. Retrieving a number is only the beginning. The harder task is understanding what that number actually represents.

This is true for non-GAAP measures and operating KPIs, but often for GAAP data as well. XBRL makes many core financial facts machine-readable, but it does not fully solve the context problem AI systems face when they need to reason across periods, segments, definitions, comparisons, and management explanations.

Take Expedia's booking nights KPI. An analyst does not just need the number. The analyst needs context such as:

  • Time: annual, quarterly, monthly, trailing, year-over-year, or quarter-over-quarter
  • Region: US, international, North America, or a company-specific grouping
  • Definition: how management defines booking nights
  • Data type: absolute value, growth rate, percentage, ratio, or indexed figure
  • Management commentary: what management said drove the change
  • Change versus prior period: whether the company changed how it reports the metric

Without this semantic layer, AI can retrieve a number without understanding it. A system could compare a quarterly US-focused KPI against a global annual figure, miss a definition change, and produce a confident but meaningless conclusion. The problem is not only AI hallucination. It is context loss.

How can AI outputs be verified in financial analysis?

In investment research, the ability to verify AI outputs is what separates useful tools from dangerous ones. An answer is useful only if you can check it, trace it back to the source, and trust the number because it comes from an authoritative source and is being used in the right context.

In practice, that means grounding AI in authoritative data, requiring citations back to source documents, attaching metadata for period, unit, geography, segment, and definition, and keeping human review in the loop for high-stakes decisions.

Why determinism matters for trust

LLMs used for financial analysis are probabilistic by nature. In many investment workflows, the same analysis should produce the same output when given the same inputs. That is not how LLMs naturally behave.

There are practical ways to reduce this problem:

  • Have the LLM write code while the actual analysis runs through deterministic logic
  • Inject the relevant data directly into the prompt and require the model to cite the numbers it used
  • Separate structured retrieval, deterministic calculation, and LLM-generated explanation into distinct layers

This does not remove all variability, but it makes the analysis more testable, auditable, and trustworthy.

What can regulators do to improve trust in AI for financial analysis?

I have personally used AI to extract data from SEC filings that was previously collected by hand. The speed improvement is real, and the accuracy can be decent. But the trust question never disappears: did it extract the right value, and did it understand the context correctly?

That concern would be much lower with better XBRL coverage and more consistent tagging, especially for non-GAAP measures, operating KPIs, and the semantic context around them. Research published in the Journal of Information Systems found that XBRL adoption led to a statistically significant increase in systematic, automated requests for newly released 10-Q and 10-K filings on EDGAR—evidence that structured data directly changes how the market consumes financial disclosures. [5] AI governance in financial services requires not just firm-level controls, but better infrastructure at the market level. Regulators can help by improving structured disclosure standards, taxonomy guidance, machine-readable context for periods and segments, and expectations around traceability, validation, and human supervision.

In many cases, better data standards will do more to build trust in AI than model-level regulation alone.

Frequently asked questions

What are the main risks of AI in financial analysis?
The primary risk is not hallucination in the abstract—it is a wrong number delivered with high confidence. Even top-performing models hallucinate at 1.8–3.1% on financial prompts in controlled conditions, with real-world rates likely higher. [3] Common failures include confusing fiscal periods with filing dates, mixing adjusted and unadjusted figures, and comparing mismatched segments or regions. These errors are harder to catch because the output often reads as coherent and well-structured.

How can AI hallucinations be reduced in financial analysis?
By grounding AI in authoritative data sources, requiring citations back to source documents, using deterministic calculation layers rather than relying on LLM math, and keeping human review in the loop for high-stakes decisions.

What role does XBRL play in AI for financial analysis?
XBRL makes core GAAP financials machine-readable, which significantly improves AI extraction accuracy. Expanding XBRL coverage to operating KPIs, non-GAAP measures, and semantic metadata would further reduce errors and increase trust.

What is authoritative data in financial analysis?
Authoritative data comes from regulated disclosures, follows recognized reporting standards, and is structured in a way that preserves meaning across companies and time periods. GAAP financials from SEC filings are a prime example. The challenge is that many of the most decision-useful data points—operating KPIs, segment breakdowns, non-GAAP measures—are not yet held to the same standard.

Conclusion: the real path to trust

AI will be transformative in investment research, but not because it replaces judgment. The value of AI is not just efficiency; it is better-informed decisions, applied more consistently across the investment process.

That only works when the underlying system is built on the four pillars of trust: data and workflows that are accurate, consistent, verifiable, and governable. In finance, the hard part is not generating text. It is getting the right number in the right context, then proving that the analysis can be trusted.

References

[5] Blankespoor, E., Miller, B. P., & White, H. D. "XBRL Adoption and Systematic Information Acquisition via EDGAR." Journal of Information Systems, 33(2), 23–49. This study found that XBRL adoption led to a statistically significant increase in systematic, automated requests for newly released 10-Q and 10-K filings on EDGAR, particularly for firms with lower information accessibility.
https://publications.aaahq.org/jis/article/33/2/23/1375/XBRL-Adoption-and-Systematic-Information

[1] Deloitte Center for Controllership. "Trust Emerges as Main Barrier to Agentic AI Adoption in Finance and Accounting." July 2025. A Deloitte poll found that while 80.5% of finance professionals believe AI tools could become standard within five years, trust in the tool, underlying data, and programming was cited as the leading obstacle to adoption (21.3%).
https://www.deloitte.com/us/en/about/press-room/trust-main-barrier-to-agentic-ai-adoption-in-finance-and-accounting.html

[3] The AI Consulting Network. "AI Hallucination Rates on CRE Financial Data: 2026 Benchmark." 2026. A benchmark study of AI model performance on commercial real estate financial data, finding best-in-class hallucination rates of 1.8–3.1% on financial prompts in controlled conditions, with rates increasing 2–4x on scanned PDFs without OCR preprocessing.
https://www.theaiconsultingnetwork.com/blog/ai-model-hallucination-rates-cre-financial-data-benchmark-study-2026

[4] CFA Institute. "Employer Survey Results: AI Disruption in Investment Management." August 2024. An employer survey in which 85% of investment industry respondents said there is a need for industry-wide standards and ethical guidelines for AI, and 82% said the lack of such standards is preventing faster adoption.
https://www.cfainstitute.org/sites/default/files/-/media/documents/survey/AI-disruption-employer-survey-2024.pdf

[2] Mercer. "AI in Investment Management Survey 2024." A survey of 150 asset managers finding that 91% are currently using (54%) or planning to use (37%) AI within their investment strategy, though more than half say AI informs rather than determines their final investment decisions.
https://www.mercer.com/insights/investments/portfolio-strategies/ai-in-investment-management-survey/

Meta description: The biggest risk of AI in financial analysis is a wrong number delivered with confidence. Here's how authoritative data, verification, and governance build trust.