DeepSeek Hallucinations - Can I Trust It for Due Diligence?

In the past decade, the family office landscape in Canada has been reshaped by rapid technological advances—none more transformative or confounding than artificial intelligence (AI). As investment researchers and technology risk leads, we’ve witnessed AI tools like ChatGPT and Google’s Gemini redefine how preliminary research, parsing of complex reports, and data summarization are conducted. Yet with these promising capabilities come less obvious—and sometimes perilous—concerns. Among the most critical is the phenomenon of AI hallucinations.

This article examines DeepSeek, an emerging AI-powered search and summarization tool, through the lens of due diligence checks and citation validation required by multi-family offices managing ultra-high-net-worth (UHNW) clients. Are these systems reliable enough for due diligence? What risks does their use entail? And how does the scarcity of private markets data exacerbate the problem?

Understanding DeepSeek AI Reliability and the Risk of Hallucinations

Before delving into details, let’s clarify what “hallucination” means in the context of AI. Unlike popular usage implying fantasy or error, in machine learning, hallucinations are instances where a model generates outputs that are plausible, confident, and linguistically coherent, yet factually inaccurate or entirely fabricated.

DeepSeek, like ChatGPT or Google Gemini, relies on large language models (LLMs) to understand queries and generate answers. These models predict likely word sequences rather than recall explicit facts. As a result, while DeepSeek can generate persuasive summaries and search results, it sometimes hallucinates facts—providing erroneous company data, misstating financials, or inventing citations.

Why Are Hallucinations So Risky?

• Persuasiveness masks error: Hallucinated content is often fluid and well-structured, making it difficult for even experienced professionals to detect errors at a glance.
• False confidence: The AI’s apparent certainty can lead analysts to place undue faith in inaccurate insights, impacting investment decisions.
• No explicit provenance: Unlike databases or regulatory filings, LLM output may lack reliable citations or supporting documentation on demand.

Family Office Use Cases: Where DeepSeek Excels and Stumbles

Family offices operate in complex environments that require balancing innovation with rigorous risk management—particularly given the fiduciary responsibilities involved in stewarding UHNW wealth. Let's explore common use cases for AI tools like DeepSeek and how hallucinations might manifest.

1. Preliminary Research

Analysts often use DeepSeek to gather a snapshot of new sectors, emerging managers, or private Click here for info market trends. The promise is faster synthesis of vast textual data such as news articles, whitepapers, and conference transcripts.

• Benefit: Accelerated knowledge acquisition beyond manual searches.
• Risk: If the AI hallucinates metrics or misattributes commentary, it can distort the initial investment thesis.

2. Parsing Complex Manager Reports

Manager due diligence requires careful review of lengthy, jargon-dense documents. DeepSeek can assist by summarizing key points, performance drivers, and risks.

• Benefit: Improved efficiency and focus on salient issues.
• Risk: Summaries may omit qualifiers or alter emphasis, potentially misrepresenting risk disclosures or fee arrangements.

3. Generating Executive Summaries

Senior stakeholders expect concise, actionable reports. DeepSeek can draft these quickly, but faithful reproduction of data is paramount.

• Benefit: Time savings and consistent narrative style.
• Risk: Hallucinated data points undermine credibility and financial integrity.

Private Markets Data Scarcity: Fuel for Hallucinations

Unlike public equities with abundant, standardized data, private markets remain notoriously opaque. Sparse datasets and limited regulatory disclosure challenge AI models trained mostly on internet-scale documents.

When DeepSeek attempts to answer queries about private credit funds or early-stage venture portfolios, the absence of concrete source material forces guesswork. The model “fills in the blanks” with plausible but fabricated details. This significantly raises the risk of hallucinations impacting due diligence outcomes.

Data Environment Example Impact on AI Output Abundant Public Data Public stock performance, SEC filings High factuality, verifiable citations Private Market Data Scarce Private fund returns, proprietary deal terms High hallucination risk, unverifiable claims

Faithfulness vs Factual Hallucinations: What’s the Difference?

In AI research and governance, two terms often arise related to output reliability:

• Faithfulness: The degree to which the AI’s output accurately reflects its input data sources.
• Factual Hallucinations: Confident statements that contradict known or verifiable facts.

For family office due diligence tasks, faithfulness is critical. A summary faithful to a private equity report is more helpful than a plausible but fabricated narrative. Unfortunately, current LLM architectures, including those powering DeepSeek, still struggle to guarantee faithfulness without explicit grounding mechanisms.

How Do ChatGPT and Google Gemini Compare?

Both ChatGPT and Google Gemini serve as benchmarks in natural language generation and data retrieval. They employ large datasets and sophisticated transformer architectures but differ in data sourcing and retrieval augmentation strategies.

• ChatGPT: Excels at conversational language and general knowledge but can hallucinate, especially on niche or proprietary topics.
• Google Gemini: Integrates more search-oriented capabilities, attempting to ground responses in up-to-date web pages, but that depends heavily on source quality.

DeepSeek leverages these underlying advances but applies them specifically to document search and summarization within family office frameworks. Its reliability directly hinges on how well it integrates source validation and safeguards against hallucinations.

Best Practices for Due Diligence Checks and Citation Validation Using DeepSeek

Given these challenges, family office teams should adopt prudent workflows for integrating DeepSeek and similar AI tools into due diligence processes.

1. Always ask: “What would we show an auditor?” Before accepting an AI-generated insight, ensure it can be traced back to a credible, documented source.
2. Use AI as a supplement, not a replacement Perform human review and cross-check with primary documents or databases such as regulatory filings, Bloomberg, or internal records.
3. Request explicit citations or source quotes Prefer tools or configurations that reveal URLs, document excerpts, or timestamps backing each claim.
4. Prioritize outputs on public data first Validate AI summaries against well-understood public company or fund data before applying to obscure private markets.
5. Maintain a “things AI got wrong” tracker Record hallucination cases to inform vendor discussions, update team awareness, and improve controls.
6. Stay current on model updates and governance documentation Avoid vague assurances like “uses latest model”; request audit trails and data provenance policies.

Conclusion: Can You Trust DeepSeek for Due Diligence?

DeepSeek and similar AI tools represent powerful allies in managing information overload in ultra-high-net-worth family offices. They streamline preliminary research, parsing, and summarization—tasks once painfully manual.

However, the specter of AI hallucinations—particularly within private markets where data scarcity invites fabrication—demands cautious skepticism. At present, relying solely on generated outputs for final investment decisions or fiduciary reporting is premature.

Combining AI-driven efficiency with rigorous due diligence checks and robust citation validation protocols is essential. By coupling DeepSeek’s strengths with human expertise and proper risk management, family offices can harness AI innovation while safeguarding the trust placed in their stewardship.

In the evolving tech risk landscape, the question is less “Can we trust DeepSeek?” and more “How do we govern DeepSeek to be trustworthy?”

Disclaimer: This discussion reflects professional experience and current technology trends as of mid-2024. As AI capabilities evolve rapidly, continuous review and governance are recommended.