NEWS:
404 Media, July 14, 2024. Goldman Sachs: AI Is Overhyped, Wildly Expensive, and Unreliable. Investment giant Goldman Sachs published a research paper about the economic viability of generative AI which notes that there is “little to show for” the huge amount of spending on generative AI infrastructure and questions “whether this large spend will ever pay off in terms of AI benefits and returns.” The paper, called “Gen AI: too much spend, too little benefit?” is based on a series of interviews with Goldman Sachs economists and researchers, MIT professor Daron Acemoglu, and infrastructure experts. The paper ultimately questions whether generative AI will ever become the transformative technology that Silicon Valley and large portions of the stock market are currently betting on, but says investors may continue to get rich anyway. “Despite these concerns and constraints, we still see room for the AI theme to run, either because AI starts to deliver on its promise, or because bubbles take a long time to burst,” the paper notes.
ZDNET, June 27, 2024. We need bold minds to challenge AI, not lazy prompt writers, bank CIO says. Wanted: Critical thinkers with perspectives and skills to interpret AI-generated results. (Those with an over-reliance on ChatGPT need not apply.)
Tabb Forum, July 2, 2024 [requires registration and or subscription] – Embedding Ethical AI at the Core of Financial Services AI is transforming finance and reshaping society — from high-frequency trading algorithms to AI-powered chatbots handling customer queries, and from instant fraud detection to 24/7 personalized financial advice, writes Yiannis Antoniou, Head of Data, AI, and Analytics at Lab49. Financial institutions are adopting AI to improve risk assessment, streamline compliance checks, broaden access to financial services, streamline operations and so much more, Mr. Antoniou explains, and the usage of AI will only continue to increase as its capabilities deepen and its impact expands. But its use also raises many questions and issues.
JD Supra, June 24, 2024. Artificial Intelligence – Banking on our Future: How Will AI Impact Community Banks and Who is Willing to Lead the Charge? The evolution of AI within the banking sector is progressing from exploration to comprehensive integration. To unlock the transformative potential of emerging artificial intelligence and machine learning advancements, banks are urged to transcend mere speculation and embrace the tangible applications of AI. Banks need to explore the practical avenues for implementing AI and learn the strategies for ensuring successful execution.
PAPERS:
BIS, The gen AI gender gap. PDF full text BIS Working Papers | No 1197 | 11 July 2024 by Iñaki Aldasoro, Olivier Armantier, Sebastian Doerr, Leonardo Gambacorta and Tommaso Oliviero. Generative artificial intelligence (gen AI) holds the potential to boost economic activity. Evidence suggests it makes workers more productive, especially in occupations that require advanced cognitive abilities, and spurs firm growth and innovation. Gen AI is thereby poised to have profound effects on aggregate output and wages. If unequally adopted across demographic groups, however, gen AI could exacerbate existing differences in pay and job opportunities. Increasing AI adoption would then lead to greater inequality, a key policy concern. We assess gender differences in the use of gen AI and their drivers, based on a representative survey of US consumers. Our analysis draws on special questions that were added to the New York Fed’s Survey of Consumer Expectations. The survey asked detailed questions about the use of gen AI tools, opportunities and risks respondents see in gen AI, their concerns regarding trust and privacy, and their understanding of the technology. We document stark differences in attitudes across groups, which could influence their use of gen AI and participation in the digital economy.
NBER – Demand for Artificial Intelligence in Settlement Negotiations. Working Paper 32685 DOI 10.3386/w32685 Issue Date When AI prediction substantially resolves trial uncertainty, a party purchasing AI prediction will disclose it if it is in their favour and not otherwise, signalling the outcome to the other party. Thus, the trial outcome becomes common knowledge. However, this implies that the parties will settle rather than purchase the AI prediction. When parties have differing prior beliefs regarding trial outcomes, these differences are only resolved if the AI prediction is purchased and utilised. In this case, AI will be purchased in equilibrium. Different trial cost allocation rules awarding all costs to the losing party (the English Rule) or having each party bear their own costs (the American Rule) can impact the demand for AI for settlement negotiations, but how this occurs interacts with the expectations regarding whether a settlement will occur or not in AI’s absence.
NBER – Will User-Contributed AI Training Data Eat Its Own Tail? Working Paper 32686. DOI 10.3386/w32686. Issue Date This paper examines and finds that the answer is likely to be no. The environment examined starts with users who contribute based on their motives to create a public good. Their own actions determine the quality of that public good but also embed a free-rider problem. When AI is trained on that data, it can generate similar contributions to the public good. It is shown that this increases the incentive of human users to provide contributions that are more costly to supply. Thus, the overall quality of contributions from both AI and humans rises compared to human-only contributions. In situations where platform providers want to generate more contributions using explicit incentives, the rate of return on such incentives is shown to be lower in this environment.
NBER – How will Generative AI impact Communication? Working Paper 32690. DOI 10.3386/w32690. Issue Date This paper examines the impact of Generative AI (GAI) on communication through the lens of salience and signalling models. It explores how GAI affects both senders’ ability to create salient messages and receivers’ costs of absorbing them. The analysis reveals that while GAI can increase communication by reducing costs, it may also disrupt traditional signalling mechanisms. In a salience model, GAI generally improves outcomes but can potentially reduce receiver welfare. In a pure signalling model, GAI may hinder effective communication by making it harder to distinguish high-quality messages. This suggests that GAI’s introduction necessitates new instruments and mechanisms to facilitate effective communication and quality assessment in this evolving landscape.
BIS, June 7, 2024. Literature review on financial technology and competition for banking services – As we increase our reliance on AI for critical financial decisions, we must address a systemically important question: How do we ensure this technology benefits not just an organization’s financials, but also meets the industry’s ethical obligations to customers, regulators, stakeholders, and the wider society? Literature review on financial technology and competition for banking services https://www.bis.org/bcbs/publ/wp43.htm BCBS | Working papers | 07 June 2024 PDF full text
The extent to which financial technology will shape the banking industry depends in part on the nature of competition for banking services that arises from innovation by incumbent banks and the entry of new players. This paper presents a review of the growing body of economic literature on financial technology and competition for banking services. The review highlights that fintech has spurred innovation and competition across banking services including in payments, lending, deposit taking and advisory services. Research finds that entry by new fintech-based service providers has expanded access to financial services and put pressure on the market share and pricing power of incumbent banks. The evidence also suggests that fintech-based firms that started out as lenders or payments providers have evolved to offer a broader range of services. We cannot fully know how ongoing innovation will affect business models, but so far, the literature highlights some enduring strengths for the model that bundles a variety of banking services in a single firm, ie, a bank.
SPEECHES:
BIS – Managing AI in banking: are we ready to cooperate? Keynote speech by Pablo Hernández de Cos, Chair of the Basel Committee on Banking Supervision and Governor of the Bank of Spain, at the Institute of International Finance Global Outlook Forum, Washington DC, 17 April 2024. PDF full text Good morning, and thank you for inviting me to speak at this conference today. In 1986, the historian Melvin Kranzberg published his six laws of technology. At the top of his list was his view that “technology is neither good nor bad; nor is it neutral”. Fast forward to today, and this edict could seamlessly apply to the debate about the use of artificial intelligence (AI) and machine learning (ML) in banking. Discussions about the promises and pitfalls of the use of generative AI and large language models in banking are becoming increasingly common. That talk is also being turned to action, with banks starting to use and invest in AI/ML. More boldly, some are already talking about the potential impact of the yet-to-be-established artificial general intelligence on banking. There is now an emerging narrative that lauds the purported benefits of AI in banking – in terms of operational efficiencies and improved risk management – while also cautioning about challenges, ranging from data privacy to model hallucinations to reputational risk. Yet we’re still left with an unanswered question: is the use of AI/ML in banking a net positive or negative to global financial stability, and perhaps society more generally? Have we thought through all of the potential scenarios that could play out in a world where AI/ML plays a prominent role in banking? Are we at risk of our own “consensual hallucinations” about AI/ML if we fail to take a step back and consider the bigger picture? I shall not attempt to provide a definitive answer to these questions today. Instead, I will follow Kranzberg’s framing and try to bring together both the micro- and macroprudential and financial stability considerations. My main message is that the use of AI in banking raises important prudential and financial stability challenges. We’ve yet to see how AI/ML performs across a full financial cycle – and this could be some time off. Left unchecked, such models could potentially amplify future banking crises. But these challenges and limitations are not insurmountable, provided that central banks and supervisory authorities adjust to this new reality and collaborate effectively.
BIS – Piero Cipollone: Artificial intelligence – a central bank’s view – Keynote speech by Mr Piero Cipollone, Member of the Executive Board of the European Central Bank, at the National Conference of Statistics on official statistics at the time of artificial intelligence, Rome, 4 July 2024. PDF full text It is a pleasure to be here today to discuss the implications of artificial intelligence (AI) from a central bank’s perspective. The world is witnessing extraordinary advances in the field of AI. We are moving from analytical AI models designed to perform specific tasks to generative AI models capable of creating human-like content. The burgeoning interest in generative AI has boosted AI adoption. A recent international survey revealed that almost three-quarters of organisations had adopted AI for one or more business functions, and around two-thirds of them are using generative AI. Nevertheless, just 8% reported using AI for five or more business functions – suggesting that we are still in the initial stages of AI integration. AI can be applied to a wide spectrum of activities, from routine and repetitive tasks to knowledge-based and creative work. It has been argued that AI is a general-purpose technology – akin to the steam engine, electricity or the computer – with the potential to transform our economies in the long run. But, like the computer before it, AI may involve a paradox similar to the one made famous by the economist Robert Solow: “You can see the computer age everywhere but in the productivity statistics.” The dawn of the computer era saw information and communication technology (ICT) profoundly alter our personal lives and the economy. Today, our workplaces, homes and social lives are interwoven with digitalisation…