AI & Wealth Management: The Hidden Cost of Hallucinations

Artificial intelligence is opening up unprecedented opportunities for wealth management.
But behind its power lies a persistent risk: hallucination.

What we mean by “hallucination”

In the field of AI, a hallucination refers to a response generated by a language model (LLM) that sounds plausible but is based on incorrect, fabricated, or inconsistent data.

IBM defines hallucinations as “convincing but false responses that AI models may produce when filling in gaps with approximations rather than relying on verified sources.”

This phenomenon is not a bug — it is structural. LLMs do not reason like humans; they generate the most statistically probable continuation of a given text. As a result, they produce outputs that sound right, but may be factually wrong.

An underestimated risk in regulated industries

In creative fields like content creation or marketing, a hallucination might be harmless.
But in highly regulated domains such as law, healthcare, or finance, the consequences can be severe.

According to McKinsey, hallucinations are a key obstacle to AI adoption in high-stakes functions, especially those where regulatory compliance is critical.
Their 2023 report on AI’s economic potential highlights that unlocking substantial productivity gains will require robust safeguards — particularly in banking, insurance, and financial advisory.

Real-world examples in wealth management

When a general-purpose AI model is used without a strong professional framework, it can deliver answers that sound convincing — but are fundamentally flawed. Here are a few common scenarios:

• Inaccurate tax projections: confusion between tax regimes, outdated deductions, or completely fabricated figures.

• Misaligned asset allocation: recommendations disconnected from the client’s profile, investment horizon, or risk tolerance.

• Faulty legal structuring: proposals for inheritance, usufruct arrangements or real estate holding companies (SCI) based on inapplicable fiscal rules.

• Regulatory oversights: failure to meet the duty of care, generation of documents with no legal value.

These errors are particularly dangerous because they are often articulated fluently, logically, and with apparent authority.

Case study: When AI hallucinates — and it gets expensive

Context:
Claire, 58, a business owner, is planning her estate strategy over a 5-year horizon. Her advisor uses a general-purpose AI to simulate options: gifting, usufruct planning, holding structure creation.

What the AI suggests:
The tool proposes a compelling strategy. It recommends a bare-ownership gift with usufruct retention, combining a €200,000 tax exemption with a real estate holding structure (SCI).

The plan is well-articulated, the calculations look precise.

But...
The suggested tax exemption does not exist. It’s a blend of the standard €100,000 allowance and a temporary measure that expired years ago.
The AI hallucinated a fiscal rule and presented it as current law.

Outcome:
Months later, the notary spots the error. To ensure compliance, part of the structure must be revised.
👉 Result: wasted time, damaged credibility, legal fees running into thousands — entirely avoidable.

Why general-purpose models fall short

Models like GPT or LLaMA are trained on billions of non-specialized documents. Their goal isn’t accuracy — it’s coherence.

Innovations like Toolformer (Meta/DeepMind, 2023) allow AI to self-train in using reliable tools, helping reduce hallucination risks.
But these systems remain experimental — and more importantly, they are not tailored to specific use cases like wealth advisory, which requires a deep understanding of law, taxation, and client logic.

How Apana addresses the issue

At Apana, we believe AI only brings value to wealth management when it is:

• Specialized: our AI is trained on real-world wealth planning scenarios.

• Structured: we combine LLMs with a rule engine to ensure regulatory compliance.

• Connected: our custom-built simulation frameworks guarantee calculation consistency.

• Supervised: the advisor remains at the center. AI assists — it does not decide.

Conclusion: Reliability over spectacle

AI has the potential to radically transform how wealth professionals operate. But only if it meets the level of rigor these professions demand.
Hallucinations are not a technicality — they’re real risks that impact professional responsibility.

Our conviction: Useful AI should not dazzle through invention.
It should illuminate through precision.

👉 Request a demo to see our advisory-focused approach in action.