Generative AI (GenAI) took the world by storm in November 2022 when OpenAI introduced ChatGPT. It has since become a talking point across capital markets as financial institutions review its potential to deliver value, consider the challenges it raises, and question whether they have the data foundation in place to deliver meaningful, unbiased and ethical results from GenAI applications. While applications have yet to be implemented to any significant extent in the market, financial institutions are running internal proofs of concept.
The potential and problems of AI and GenAI were the subject of lively discussion at A-Team Group’s inaugural AI in Capital Markets Summit (AICMS) in London last week, with speakers exploring current and emerging trends in AI, the potential of GenAI and large language models (LLMs), and how AI can be applied to achieve efficiencies and business value across the organisation. With a note of caution, the conversation also covered the risks and challenges of adopting AI and the foundational technologies and data management capabilities that underpin successful deployment.Opening the summit and introduced by A-Team president and chief content officer Andrew Delaney, Edward J. Achter from the office of applied AI at HSBC set the scene for the day, noting the need to build AI and GenAI products that are responsible and ethical and can be scaled, and describing the importance of educating and engaging the workforce to ensure solutions are used effectively and ethically.
In more detail, the keynote speaker explained the explosion of interest in AI and GenAI following the release of ChatGPT and a change in conversation at financial institutions. He also warned of risks inherent to the technology including fairness and bias, data privacy, and the deliberate spread of false information. To mitigate risk and create value, Achter emphasised the need to get your data house in order and, perhaps a long time in the asking, pay attention to data leaders as data is the lifeblood of AI and GenAI applications.
Also important to consider are regulatory requirements around AI and GenAI, addressing the carbon emission costs of using LLMs, and perhaps most importantly, writing a clear company policy that can be shared with all stakeholders. Demonstrating the benefits of AI and GenAI products can turn scepticism into an understanding of benefits, including productivity gains that can be measured, and change negative perspectives into positive approaches to doing more with the technology.
Ultimately, a skilled workforce, educated customers, technology used in the right context of conduct, and confidence across the organisation will result in value creation.
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