Unpacking MoneyNext IO 2024 - The Latest In AI For Banking
Dr. Brian Massey
Helping FS Leaders Solve The Organisational Challenges Of AI | Managing Partner at Anordea | Strategy Advisor, Professor & Keynote Speaker
Hello and welcome to the latest edition of Banking On AI Strategy. I’ve changed the name to reflect the work I’ve been doing over the last year for clients in the FS and Banking across Europe, the Middle East and the Gulf. Going forward there will be a bit more of a focus on the FS and Banking, to match my client and research work, but there will still be plenty here for those interested in AI in other contexts. In this edition we’ll review some recent industry developments in AI, and I’ll also cover some of the leading insights from the recent MoneyNext IO Banking AI event in London last month.
AI Industry Updates:
Policy: EU Publishes AI Code of Practice
The first draft of the General-Purpose AI Code of Practice, developed by independent experts and facilitated by the European AI Office, marks a key milestone in establishing robust guidelines for the development and use of trustworthy, safe, and transparent AI models. Following an iterative drafting process set to conclude in April 2025, the initial draft incorporates a range of stakeholder perspectives, anticipates sector-specific adjustments, and includes open questions to guide ongoing refinement. Stakeholders, including nearly 1000 representatives, EU Member States, and international observers, will discuss this draft in a series of working group meetings, and their feedback will shape subsequent iterations and ensure that measures, standards, and KPIs align with varying levels of risk, provider size, and technological evolution.
For the financial services sector, this emerging Code of Practice could serve as a critical framework to bolster compliance, risk mitigation, and transparency as financial institutions increasingly rely on general-purpose AI models. By codifying standards around systemic risk assessment, governance structures, and the responsible use of data, the Code could help financial services providers both adhere to upcoming regulatory requirements and maintain trust among consumers, regulators, and investors. Such guidance may pave the way for secure innovation, streamlined compliance processes, and enhanced market stability as AI tools play an ever more central role in financial decision-making.
Policy: EU Consultation Process Closes
The European Commission’s AI Office has invited a range of stakeholders, providers, businesses, national authorities, academia, research bodies, and civil society, to help shape future guidelines on defining AI systems and implementing prohibitions against practices that pose unacceptable risks under the AI Act. This consultation, closing on the 11th December 2024, aims to gather practical examples and clarity on real-world use cases, enabling the Commission to release tailored guidelines in early 2025 and support compliance ahead of the AI Act’s enforcement date on 2nd February 2025.
For banks and other financial institutions, these forthcoming guidelines will offer crucial direction on identifying which AI tools and practices are permissible, helping firms refine their risk management, compliance, and oversight strategies. By clarifying the AI Act’s definitions and prohibitions, financial institutions can better anticipate regulatory expectations, strengthen trust with clients, and ensure their AI-driven offerings, such as automated investment recommendations, fraud detection systems, and credit scoring models, remain both innovative and legally sound.
Research: Global AI Power Rankings Released
The latest Global AI Power Rankings from the Stanford Institute for Human-Centered AI places the U.S. firmly at the top of global AI leadership, with China and the U.K. trailing behind. This updated 2024 tool aggregates 42 indicators to evaluate 36 countries on factors such as research output, private investment, patents, and infrastructure. The U.S. emerges as a clear front-runner across most categories, investing heavily in responsible AI research and producing the most notable machine learning models. While China leads in patent filings, the gap between it and the U.S. is widening, and other countries like the U.A.E. and the U.K. are swiftly rising by making AI a national priority. The tool’s flexibility allows stakeholders to tailor rankings by adjusting indicator weights, and its developers hope it will spur improved data collection and foster stronger international collaboration around AI ecosystem development.
Financial firms looking to expand into global markets can use the rankings to identify promising AI hubs, anticipate regulatory environments, and tap into robust research ecosystems. Moreover, understanding which nations excel in areas like responsible AI development and private investment can help financial institutions partner with the most forward-looking tech ecosystems, ensuring their AI adoption remains both innovative and compliant with emerging global standards.
Technology: OpenAI Launches New Pro Subscription Level
OpenAI has unveiled a new premium tier, ChatGPT Pro, priced at $200 per month, that grants users unlimited access to the company’s most advanced AI models, including an improved “o1” reasoning model. By allocating more computing resources, “o1 pro mode” is significantly better at handling complex math, coding, and specialized knowledge tasks such as case law analysis and scientific problem-solving. This move marks a key step in OpenAI’s efforts to offset the high costs of AI development by attracting power users, like researchers and engineers, who require more reliable and in-depth capabilities. The subscription also includes the o1-mini, GPT-4o, and Advanced Voice features, as well as a new progress bar for long-running queries. To foster socially beneficial AI research, OpenAI is awarding ChatGPT Pro grants to select medical researchers, and plans to expand such grants in the future. ChatGPT Pro joins OpenAI’s existing $20-per-month Plus tier, but aims to serve professionals who need cutting-edge AI performance at scale.
For the financial services sector, this new offering could be highly disruptive. Firms that rely on advanced analysis, such as quantitative trading strategies, complex risk modelling, and in-depth regulatory research, stand to benefit significantly. The improved reasoning capabilities of o1 pro mode may lead to more accurate financial forecasts, better fraud detection, and enhanced due diligence, all while streamlining compliance and reducing operational complexity. With ChatGPT Pro, financial institutions can access a higher standard of AI-driven process work, potentially giving them a competitive edge in a market increasingly defined by the ability to process and interpret large volumes of complex data.
Cybersecurity: FMF Publishes AI Cyber Defence Brief
The Frontier Model Forum has released a cyber security brief on recent advances in general-purpose AI and how they relate to the creation or combating of cyber threats. The promise to revolutionise cybersecurity by automating tasks, refining threat detection, and facilitating rapid, more accurate incident response, is a high-potential use case for AI. While machine learning techniques have long been integral to analysing network traffic and detecting malware, frontier AI capabilities now enable defenders to identify vulnerabilities, streamline security workflows, gather open-source intelligence, and improve training simulations. These advanced systems can summarise incidents, integrate multiple data sources, enhance penetration testing, and help organisations proactively address emerging risks, all while still relying on human oversight to ensure sound decision-making. Though challenges remain, such as explainability, privacy, resource constraints, and the risk of adversarial exploitation, frontier AI systems offer defenders potent new tools to stay one step ahead of increasingly sophisticated threat actors.
For the FS, these developments can fortify defences against a growing spectrum of cyber threats targeting sensitive financial data and operations. By enhancing network monitoring, vulnerability discovery, and response capabilities, frontier AI can help financial institutions detect and remediate issues before attackers capitalise on them. This not only reduces the risk of costly breaches and regulatory penalties, but also builds client trust by ensuring that firms can safeguard their operations and maintain integrity, even in the face of rapidly evolving cyber adversaries.
AI Blueprint
Today’s newsletter is sponsored by Anordea’s AI Blueprint. At Anordea, we advise CXOs and senior leaders across Financial Services, helping them tackle their toughest problems. With the rapid pace of development and change in the AI space, many leaders are faced with a paradox of how to build on and govern the technology that is as complex as it is powerful.
Our AI Blueprint process solves this paradox quickly and effectively by giving leaders immediate insight into the state of AI across their organisation and industry, highlighting the opportunities and challenges which need to be addressed to maximise their AI opportunity in a safe and well-governed manner. Based on our leading expertise and ongoing research in the AI strategy, transformation and governance fields, the AI Blueprint offers a fast and high-impact solution to one of the biggest challenges facing leaders today.
Working with a dedicated member of our senior leadership team, the benefits of this process include ROIs in the strategy, governance, operational, financial and personal arenas. For those interested in exploring what our AI Blueprint involves, and how it can help your organisation gain a leading advantage over competitors, reach out to one of our senior partners here or contact us at info@anordea.com.
Unpacking MoneyNext IO 2024 – The Latest In AI For Banking
Two weeks ago, I had a pleasure of speaking the MoneyNext IO event on AI and Automation in Banking. I was there to talk about how AI is advancing across the banking industry while also running some round tables with industry leaders. On the day I managed to have several conversations with banking leaders and innovators who are focused on AI. I love attending industry events like this, and I believe the MoneyNext events provide a leading forum to bring innovators and leaders across the banking industry together who are especially focused on AI. Quite a few things came out of the day and there are some very exciting prospects for the banking industry next year as regards the AI agenda. Today I want to go through some of these new trends and give you some leading insight on how AI is driving change in banking and across the FS.
Use Case To Business Case To Implementation
One of the topics to crop up again and again throughout the day was the continuing problem and challenges faced around taking AI applications from use case, to business case, and then to implementation. I’m not surprised by this as there are variety of challenges along the way, many of which I’ve written about before in this newsletters, but I think what leaders in banking are realising is that it is often the second order challenges related to organisational processes and behaviour that create more of a problem, then the innovation on the technology side. Today, banking leaders can very easily find themselves confronted with an overwhelming number of potential AI use cases. This is only exacerbated by the resource constraints banking leaders face in team / staff availability and even their own time. The first challenge essentially is being able to filter through the available use cases and identify those which are worth taking further.
However, taking a use case further is not an easy thing. To go from use case to business case requires a large amount of work around value engineering, ROI estimation, and stakeholder management. This is where AI solutions and potential applications often fall and get lost in the process. And then there is the integration of a desired AI use case into business processes. There we are seeing a range of challenges around understanding of AI and centaur usage across the organisation. There is also a behaviour modification component in terms of how we expect people to engage with the technology and make use of it in their daily work practices. I think we are going to get better at solving this transition problem in the next couple of years but it will require some new ways of thinking, particularly around how we take technologies and integrate them into business processes and organisational models. More and more we are seeing that existing digital transformation models and methods that have been successful over the past two decades, fall short of what is needed to successfully manage AI deployments and transformations.
AI As a Risk Mitigation Tool
Another really interesting thing to come out of the conversations on the day was how the use of AI relates to governance and risk management. One of the leading speakers at the conference made the point that AI tools can actually aid risk management, rather than just be a source of risk. I think this is a very intelligent view to take, and that going forward we need to see a more balanced assessment of how AI technologies will impact the organisation. This relates to the challenges I was talking about above, in bringing use cases through to implementation, and we need to get away from the polarisation that exists in some organisations and beyond where AI is seen either as a potential answer to every question, or something highly destructive to authenticity and value. Neither of these two extreme views are correct. Instead, leaders are going to have to grapple with the reality which is somewhere in the middle.
We have a very powerful technology that requires a lot of shaping and calibration in order to implement it into organisations in a way that allows for value to be created meaningfully. It is one thing for us as technology innovators to take on this view however, and something entirely different to be able to convince our colleagues across an organisation to think this way too. I believe 2025 will be the year where innovators and tech leaders across the FS begin to confront the organisational challenges more head on.
Agentic AI
The new concept doing the rounds at the conference was without doubt Agentic AI. You may have already seen the news that some technology companies like OpenAI are preparing to release early versions of their AI agents next year. There is also a very interesting start-up called /Dev/Agents which has begun to attract serious VC backing. I tend to think of the current AI age in three distinct waves. The first wave is generative AI and we have all experienced to some extent this technology as it begins to emerge, though I will caution it has not yet been integrated into process and systems. The second wave is agent-based or ‘Agentic AI’, and the third wave will be what is termed general intelligence or super intelligence. The third wave is in the future, and we have no idea how far away that future is. The first wave is already here, and we are trying to figure out how to integrate generative AI into our processes. Be aware that this will take years.
However, the second wave, Agentic AI, looks like it is about to emerge much sooner than first thought. This is interesting for two reasons. Firstly, the value propositions offered by Agentic AI are truly disruptive in nature and would have huge impacts on business systems, industries and even entire economics. However, the second aspect of Agentic AI is perhaps more fascinating and perplexing, in that the creation of agent-based AI that is functional, may eliminate and destroy the use cases currently being worked on in generative AI. This presents organisational leaders and innovators with a whole new range of transformation problems.
Agentic Capability & Multiagent Systems
Simply put if an AI agent can complete a task alone, why do we need a centaur team of a human assisted by an AI copilot to achieve the same outcome? Here in lies one of the wicked problems of the AI transformation age we are experiencing, in that innovators and leaders may find the tools and solutions they are creating with generative AI, are obliterated by advances in Agentic AI, before they even get to deploy them. Potential for multiagent systems across ecosystems and perhaps even fully autonomous companies is no longer pure fiction, and I would predict in the next decade we will see zero employee autonomous companies at least on the start -up level.
AI Strategy Gaps
Bringing it back to the large firm setting for organisations like banks and other FS institutions, what does this multiagent system age, and Agentic Capability mean? Well as many of the banking leaders I spoke to at the MoneyNext event said, one factor that continues to hamper their work is the lack of a clear AI Strategy. To this point organisations have been in the experimentation phase with artificial intelligence and have been deploying use cases within sand boxes internally, before attempting to plug them into frontline operations. However, we are at the point where senior stakeholders and boards want to see a return on their investment and the implementation of AI use cases, tools and applications. To do this successfully though there must be a clear integration between what the organisations is pursuing in its overarching corporate strategy, and those use cases taking centre stage within the AI Strategy. However, if the organisation does not have an AI Strategy to begin with, there is no way to build this bridge and link AI innovations meaningfully into day-to-day operations.
Leadership Takeaways:
That’s it for this edition of Banking On AI Strategy. Subscribe now, and if you like what you read today, please like and share it with your network to help me reach a wider audience. Have a great day, and I'll see you next time!