This semi-monthly column highlights news, government documents, NGO/IGO papers, conferences, industry white papers and reports, academic papers and speeches, and central bank actions on the subject of AI’s fast paced impact on the banking and finance sectors. The chronological links provided are to the primary sources, and as available, indicate links to alternate free version.

NEWS:

Sequoia invests in AI tool that could replace junior bankers. Bloomberg, October 27, 2025. Sequoia Capital is leading a new $50–$100M round in Rogo Technologies, valuing the New York AI startup at $750M, more than double its valuation from earlier this year. Founded by former Lazard and JPMorgan bankers, ROGO builds software that automates slide decks, IPO documents, and financial models, positioning itself as an AI “analyst” for investment banking. Customers include Tiger Global, Lazard, and Moelis. The deal underscores growing competition from OpenAI and Anthropic, which are both developing AI tools for finance.

Anthropic rolls out Claude AI for finance, integrates with Excel to rival Microsoft Copilot. Anthropic is embedding its Claude AI directly into Microsoft Excel, letting analysts analyze, build, and audit financial models without leaving their spreadsheets. The move expands Claude’s presence across Microsoft’s enterprise AI ecosystem and gives it direct access to real-time financial data from major providers like Moody’s, LSEG, and Morningstar. Armed with new pre-built workflows for valuations, due diligence, and earnings analysis, Anthropic is positioning Claude as a precision finance assistant – a trusted, transparent tool designed to rival Copilot and OpenAI in one of AI’s most lucrative battlegrounds.

Defining The Big Picture Framework When It Comes To The Economics Of Transformative AI. Forbes, September 29, 2025. In today’s column, I examine a newly released research paper that tackles an important topic, namely, the need to formulate and promulgate a big picture perspective regarding the economic and societal impacts of transformative AI. The paper was recently posted by the esteemed National Bureau of Economic Research (NBER) and does a yeoman’s job in laying out an engaging and foundational big picture or framework that deserves keen consideration. I will walk you through the key aspects and aim to whet your appetite on the altogether weighty matter. We definitely need more work of this kind. The economic upheaval that might very well coincide with the rise of artificial general intelligence (AGI) and someday artificial superintelligence (ASI) requires rapt attention now. We can’t put off these crucial analyses. The usual refrain by high-tech is that we should mindlessly move fast and break things. But misguidedly breaking our economies and economic formations carries enormously adverse consequences, especially if we aren’t preparing ourselves for the consequences.

PAPERS AND SPEECHES – NGOs & IGOs:

Bank for International Settlement

Artificial intelligence and central banks: monetary and financial stability implications. Remarks prepared for delivery by Mr Tao Zhang, BIS Chief Representative for Asia and the Pacific, at the Global Fintech Fest 2025, Mumbai, 8 October 2025. BIS speech  |  08 October 2025. I am very pleased to join you here at the 6th Global Fintech Fest in Mumbai – a financial hub celebrated for its rich history, vibrant culture and, most importantly, enduring spirit of innovation. Today, central banks operate in a world of rapid transformation. Technology has reshaped not only how financial services are delivered but also how central banks interact with external stakeholders. Artificial intelligence (AI) stands at the centre of this transformation. In my speech this afternoon, I will explore how central banks are using AI to support their operations, the challenges AI poses and strategies to address the trade-offs central banks encounter in order to reconcile the risks and benefits. Central banks have improved their operations with the use of AI – The adoption of AI has extended to central banks, where it has the potential to enhance efficiency, improve accuracy and strengthen decision-making processes. AI is making significant impact in three key areas:

1) Data analysis

• Central banks are leveraging AI to unlock the potential of both traditional and non-traditional data sources. By analysing diverse data sets – from satellite imagery to social media content – AI offers new ways to understand economic activity and trends.
• Natural language processing (NLP) and large language models (LLMs) offer central banks innovative tools to extract insights and analyse survey responses. For instance, the Bank of Canada leverages AI models along with granular data to improve the monitoring of economic activities, banknote demand and sentiment across key sectors.¹

2) Economic forecasting and policy analysis

• Central banks use AI, alongside human expertise, to better understand economies and enhance forecasting or policy analysis.
  • For example, AI has become invaluable for nowcasting, providing real-time assessments of key economic indicators such as GDP growth and inflation. AI analyses consumption patterns and detects supply chain bottlenecks in real time, offering a clearer understanding of economic dynamics.²
• Machine learning models process vast data sets, uncovering trends and behaviours that often go unnoticed. For example, fine-tuned open source LLMs summarise economic narratives and predict recessions. Neural networks can also leverage detailed data sets to capture complex non-linear relationships, providing valuable insights during periods of rapidly changing inflation dynamics.
• AI supports financial stability analysis by identifying patterns in large data sets, which is useful for assessing risks across financial and non-financial firms. For example, during low liquidity and periods of market dysfunction, AI supports predictions by monitoring market anomalies.

3) Payment system oversight and global connectivity

• AI is transforming payment systems by enhancing safety, efficiency and compliance. Tools like graph neural networks improve fraud detection by identifying suspicious transaction networks, especially when data are securely pooled across institutions or jurisdictions.
• In correspondent banking, which faces challenges from compliance risks, AI could enhance anti-money laundering (AML) and know-your-customer (KYC) processes, reducing risks and their associated costs, which may restore global payment connectivity. By pooling payment data across jurisdictions, AI strengthens cross-border fraud detection and compliance.
• Central banks are adopting AI to enhance payment infrastructures. The BIS Innovation Hub is collaborating with seven central banks, including the Bank of France, Bank of Japan, Bank of Korea and Swiss National Bank, on Project Agorá. This project leverages tokenisation to implement the next-generation of correspondent banking. Beyond the focus on core features of the unified ledger, AI models may be used in the future to improve efficiencies in compliance practices.

NBER

Artificial Intelligence in Research and Development, Benjamin Jones. Working Paper 34312. DOI 10.3386/w34312. Issue Date October 2025. How much can AI accelerate progress in different research fields? This paper shows that three features—the share of research tasks AI performs, the productivity of AI at those tasks, and the strength of bottlenecks—are key determinants of AI’s implications in any area, from cancer therapeutics to software design. The model maps changes in AI capabilities to research outcomes, quantifies the “marginal returns to intelligence,” and shows how AI can shift returns to R&D investment. Concepts like superintelligence, Powerful AI, and Transformative AI are further engaged and disciplined. Finally, the framework sets a measurement agenda linking AI benchmarks to field-specific opportunities for accelerating progress.

The Impact of AI and Digital Platforms on the Information Ecosystem. Joseph E. Stiglitz & Maxim Ventura-Bolet. Working Paper 34318. DOI 10.3386/w34318 Issue Date October 2025. class=”page-header__citation-item-label”>Revision Date October 2025. We develop a tractable model to study how AI and digital platforms impact the information ecosystem. News producers — who create truthful or untruthful content that becomes a public good or bad — earn revenue from consumer visits. Consumers search for information and differ in their ability to distinguish truthful from untruthful information. AI and digital platforms influence the ecosystem by: improving the efficiency of processing and transmission of information, endangering the producer business model, changing the relative cost of producing misinformation and altering the ability of consumers to screen quality. We find that in the absence of adequate regulation (accountability, content moderation, and intellectual property protection) the quality of the information ecosystem may decline, both because the equilibrium quantity of truthful information declines and the share of misinformation increases; and polarization may intensify. While some of these problems are already evident with digital platforms, AI may have different, and overall more adverse, impacts.

SSRN

Wei, Le, A Review of AI Applications in Digital Banking Platforms: Enhancing Accessibility, Usability, and Engagement (August 26, 2025). Available at SSRN: https://ssrn.com/abstract=5406826 or http://dx.doi.org/10.2139/ssrn.5406826

Wu, Jilan and Hou, Jiaming and Xu, Xin, Ai Anxiety as a Positive Moderator in Banking Chatbot Continuance Usage Intention: Pls-Sem and Ann Analysis. Posted: 13 May 2025. Available at SSRN: https://ssrn.com/abstract=5244527 or http://dx.doi.org/10.2139/ssrn.5244527

Musch, Sean and Borrelli, Michael and Karushkov, Mitko and Orsos, Patrick and Orsos, Patrick and Ulieru, Mihaela and Heitmann, Martin and Vasiliu-Feltes, Ingrid and Boevink, Michael and Bohnert, Dave and Kohnstamm, David and Balan, Binesh and Schöne, Ina and Mishra, Anandodaya and Mishra, Anandodaya and Karathanasis, Theodoros and Kerrigan, Charles and Kohn, Benedikt and Debelle, Arno, EU AI Act: Trustworthy AI for the Digital Decade (February 21, 2025). AI & Partners B.V. 2025, Available at SSRN: https://ssrn.com/abstract=5147156 or http://dx.doi.org/10.2139/ssrn.5147156