Conference Agenda

If we are to secure the opportunities and control the challenges of artificial intelligence, it's time to legislate AI systems that are principles-based, outcomes-focused, input-transparent, and permissioned, that are paid for and understood. There are three reasons why we should: social, democratic, economic. From 1940s Bletchley to 2020s United Kingdom, it's time for human led, principle-based artificial intelligence, for transparency and trustworthiness, inclusion and innovation, interoperability, and international focus for accountability and assurance for AI developers, deployers, and for democracy itself. Our data, our decisions, our AI futures.

AI innovation continues to shape the future of humanity across nearly every industry. AI is already the main driver of emerging technologies such as big data, robotics and IoT, and generative AI has further expanded the possibilities and popularity of AI. GenAI has spearheaded the latest chapter in AI’s evolution, particularly with OpenAI developing its GPT-4 model and ChatGPT, which has led to generators that can process queries to produce relevant text, audio, images and other types of content. AI has also been used to help sequence RNA for vaccines and model human speech, technologies that rely on model- and algorithm-based machine learning and increasingly focus on perception, reasoning and generalisation. While the impact of AI in terms of technological singularity, automated weapons, data security breaches, deepfakes and misinformation, human bias and job losses must be considered, improved business automation and increased regulation could mitigate potential challenges.

While AI technologies are being developed at a fast pace, the availability of high-quality and meaningful data is essential for the development of AI. Further to this, although the use of AI raises many concerns regarding the ethics and transparency of data collection, use and dissemination, the benefits and risks of AI must be carefully assessed, which with regulation, it will. Where does that leave us? AI can then be used to improve learning and teaching methods, notably by helping education systems to use data to improve educational equity and quality, whilst promoting personalisation and better access to education. AI at scale can actively respond to the needs of the sector – whether it be healthcare, manufacturing, music or financial services. AI can be used to create innovative ways to make datasets of assets held by organisations of all types widely accessible.

The rise of ChatGPT as a mass consumption natural language model in 2023 marked the era of GenAI democratisation. While most financial services players remain bullish on the future of GenAI, the numerous use cases may change their mind. GenAI analyses customer behaviour, discovers preferences, and has the power to unlock important insights, leading to more personalised user experiences and improved customer service. We are already witnessing a shift from traditional chatbots to sophisticated ones that are highly trained in semantics and can provide knowledgeable responses. The technology is capable of not only generating immediate, precise, and natural-sounding answers, but it can also analyse a customer’s financial history and credit rating and help them source and choose the best loan option for their unique situation. AI assistants can also help staff to more quickly access key information for clients, and more intelligent robotic process automation (RPA) allows a level of usability with AI enhancements that will make it more valuable to enhance workflows and automate decisions. In addition to this, advances in natural language processing (NLP) combined with other traditional AI that can spot trends and anomalies unlocks a more complete picture. 

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Ensuring accurate checks and balances when it comes to instant compliance with regulation and instant access to data. The industry is backwards: we must prove the good actors are good actors before we suspect customers to be bad actors. AI provides an opportunity to replace how false positives work, and machine learning can support the hyper personalisation of customer service.

Considering ethics, explainability and the auditability of AI, there must be an organisational change or shift that enables myths to be busted and the human in the loop is perceived as a vital role. Human roles are only compromised if financial institutions don’t keep pace. The industry must augment AI’s role and the technology can become a source of input for the human, but bias must be managed, and teams must continue to be sensitive to data.

Where do we go now and how can AI be deployed? While emerging trends like the metaverse and Web 3 are still relevant, for cross industry representation and usage of AI, organisations must learn from the tech-savvy leaders that know how to create risk profiles in a different, more overarching way.

With standards like DORA and the EU AI Act coming to the fore, and with the drive towards digital not slowing down, financial institutions will need to be more considerate of their operational resilience and how to mitigate threats without making an impact on the user experience.