Generative AI models have various known issues. These models have the tendency to "hallucinate," generating false outputs that are not justified by the training data and presenting them as a fact. These errors can be caused by various factors, such as improper model architecture or noise and divergences in the training data. Opaqueness about how model outcomes are generated is also an issue. With billions to trillions of model parameters that determine the probabilities of each part of its response, it is exceedingly difficult to map model outputs to the source data, including in cases of hallucination.

In addition, if the training data contains societal prejudices or if the algorithm design is influenced by human biases, the model may learn and propagate these biases in its outputs. Enterprise applications could also be vulnerable to data privacy issues and cybersecurity threats. This includes leakage of sensitive information within the training data if the model is customer-/public-facing, usage of personal or sensitive data in model training that may have needed explicit consent to use, as well as malicious attacks from hackers that aim to manipulate model outputs. These issues give rise to various legal and reputational risks, the scale of which depends on the criticality of the use case and the company's industry. For example, the financial and healthcare industries may be subject to severe consequences if problems do arise, due to the high-stakes nature of these industries.

In financial advisory use cases, model hallucinations could give inappropriate advice or offer the wrong product to undiscerning clients, which could undermine public trust in AI systems and the financial institutions using them. Opaqueness about how model outcomes are generated is also a key issue for financial institutions, as these institutions are required to be able to explain their decisions internally and to external stakeholders. Considering all this, it is best practice to implement a degree of separation between direct model outputs and the customer, where internal staff could be trained to recognize potential errors and inconsistencies in model outputs and assume ultimate responsibility for the decision-making process.

Generative AI could also offer a quick and low-cost way for financial institutions to profile their clients for marketing campaigns, risk management and identification of suspicious transactions. However, overreliance on generative AI profiling could violate anti-discrimination laws due to potential bias embedded within the models. Appropriate human judgment will need to complement generative AI models that perform client profiling. Financial institutions will also need to have strong data privacy policies and robust cybersecurity measures to address generative AI's risks to their sensitive client information and proprietary data.

In view of all these issues and risks, below are questions investors should consider when assessing companies employing generative AI tools:

• What are the risk-mitigating mechanisms and/or circumstances? Solutions include having trained internal staff act as an intermediary between direct model outputs and the customer; working to understand potential biases in the training data and address them in model design; regular and proactive monitoring of model output to promptly identify and address any signs of hallucinations; implementing robust cybersecurity measures; etc.
• What is being done to enhance model performance? Solutions include ensuring that training data is high quality, accurate and up to date; implementing iterative feedback loops to refine and improve model performance; etc.
• Are there any transparency and oversight on ethical AI principles? This pertains to providing transparency on data sourcing and data privacy concerns; defining clear policies and procedures to ensure compliance with ethical standards and emerging regulations; outlining the roles and responsibilities of individuals involved in the development, operation and oversight of the generative AI model; etc.

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