By
Matthew Hearfield
March 26, 2024
Updated
November 20, 2024
Join Anna Frazzetto and Richard Lord, Managing Director and Regional Chief Information Officer, Wholesale - Asia Pacific at HSBC, as they discuss the application of AI and large language models like GPT-4 in the financial industry.
Artificial Intelligence (AI) is revolutionising the marketing industry by offering unprecedented personalisation and efficiency in customer interactions. AI-powered tools and solutions are capable of processing vast amounts of data to inform and automate decision-making processes.
In the area of staffing, AI is making equally significant strides. AI in staffing not only streamlines the recruitment process but also helps reduce unconscious bias, leading to more diverse and competent workforces.
Listen to the full episode below:
The journey from traditional data to AI
The large language models and generative AI we have today started off as old fashioned data and analytics.
AI has grown out of a need for businesses to be able to quickly and efficiently understand data and implement strategies to improve business practices quickly. This has been an ongoing process in many large data rich tech companies for a decade. The scope of AI has however moved beyond simple numerical data sets and transactional activities. AI has expanded to cover a wide range of more comprehensive and complex tasks across audio, images, text and any other way in which data is presented.
Richard Lord emphasises the evolutionary journey from traditional data analytics to the current state of AI and generative models. He highlights the financial industry's rich data environment as a fertile ground for AI applications, aiming to improve service quality, customer experience, and operational efficiency.
The responsibility of using AI
The output of AI is only as good as the data that goes in.
Any AI model is subject to the bias of the data behind it, and the bias of the humans entering that data. Depending on what data is selected or even how it's presented, an AI can reach conclusions that are biased or outright incorrect due to a lack of context or understanding. It's for these reasons that it's vital that the way data is gathered and structured is as unbiased as possible, and in sensitive industries such as finance that data must also be kept secure and not allowed to pass through an AI with personal information attached.
Richard stresses that while AI provides powerful tools for innovation and personalisation, the ultimate accountability for its deployment and outcomes rests with human operators.
Implementing AI in a secure environment
AI deployment in secure environments necessitates strict adherence to regulatory standards and ethical guidelines. Financial institutions must ensure that their AI systems are transparent and explainable, not just to satisfy regulatory bodies but also to maintain trust with their customers. This includes compliance with data protection regulations like GDPR in Europe, CCPA in California, and other global data privacy laws.Exploring and integrating AI into financial services
Where it's exciting for us is knowledge discovery and distillation of massive amounts of information.
The future of AI in financial sectors involves proactive engagement with regulators to shape the development of AI governance frameworks. By participating in regulatory discussions and advocating for sensible AI policies, financial institutions can help ensure that regulations support innovation while protecting consumers and the integrity of the financial system.
Richard's outlook on a bold approach to AI underscores the importance of not just cautiously adopting AI technologies but actively pushing the boundaries to harness their potential. This approach balances the pursuit of innovation with the need for security, ethical considerations, and regulatory compliance, paving the way for a future where AI significantly enhances the value financial institutions provide to their customers and society.
Key takeaways
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The journey from traditional data to AI: Richard Lord emphasises the evolutionary journey from traditional data analytics to the current state of AI and generative models.
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The responsibility of using AI: Richard stresses that while AI provides powerful tools for innovation and personalisation, the ultimate accountability for its deployment and outcomes rests with human operators.
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Implementing AI in a secure environment: The foundation of AI's effectiveness in secure environments is the quality and integrity of data. Richard emphasises the critical importance of using high-quality, unbiased data to train AI models.
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Exploring and integrating AI in financial services: Richard's outlook on a bold approach to AI underscores the importance of not just cautiously adopting AI technologies but actively pushing the boundaries to harness their potential.
Article and quotes have been edited for brevity and clarity
