The Risks of Large Language Models for Big Brands

In an era where technology intertwines with every aspect of our lives, Large Language Models (LLMs) like ChatGPT have become increasingly popular. They offer promising advancements but also pose unique risks, especially for big brands in sectors like Banking and Finance, Healthcare, News and Media, Insurance, etc. A critical issue is the risk of ‘hallucination’, where a bot might provide incorrect information, such as inaccurate interest rates, misdiagnoses, misinformation, etc. This scenario raises the question: could a bank, a private healthcare centre or a media outlet be liable for such misinformation?

Understanding LLM ‘Hallucination’

‘Hallucination’ in the context of LLMs refers to the generation of factually incorrect or misleading information. Despite their advanced algorithms, LLMs aren’t infallible. They don’t possess real-time data and often rely on pre-trained information, leading to potential inaccuracies.

Implications for Big Brands

For big brands, especially in sensitive sectors like those mentioned above, the stakes are high. Imagine a scenario where a customer queries a bank’s GPT-powered bot for current interest rates. If the bot, due to ‘hallucination’, provides outdated or incorrect rates, the customer could make financially detrimental decisions based on this misinformation.

Liability Concerns

This leads us to a legal conundrum. Can a bank be held liable for the misinformation provided by a bot? This question touches on complex legal aspects such as:

  1. Duty of Care: Financial institutions are bound by a high duty of care towards their customers. Misinformation, even from a bot, could be seen as a breach of this duty.
  2. Misrepresentation: Providing incorrect financial advice, even unintentionally, might be construed as misrepresentation.
  3. Regulatory Compliance: Banks are subject to strict regulatory standards. Inaccurate information dissemination, regardless of the source, could result in non-compliance penalties.


Risk Management Strategies

To mitigate these risks, brands can do the following:

  1. Integrate Real-Time Data Systems: Ensuring that bots have access to current data can reduce misinformation.
  2. Implement Robust Oversight Mechanisms: Regular audits and updates of the LLM’s knowledge base are crucial.
  3. User Disclaimer: Clearly stating that the information provided by bots is advisory and subject to confirmation can limit liability.


While LLMs offer transformative potential, for big brands, particularly in the banking sector, the risks are significant. The ‘hallucination’ issue reinforces the need for a cautious and regulated approach. As technology evolves, so too must our understanding of its implications in various sectors. Balancing innovation with responsibility remains a paramount challenge.


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