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 versions.
NEWS
AI: Finally Bringing The ‘Strategic’ To Strategic Finance, Forbes, November 21, 2025….Nearly 80% of CFOs today still haven’t adopted what I’d consider a modern planning solution. And 45% still rely on Excel as their dominant tool for budgeting, forecasting and business planning. While SaaS FP&A adoption continues to grow, it hasn’t yet crossed the chasm—particularly in the midmarket. To understand why, let me first clarify what everyone means when we say “strategic finance.” It’s not simply having a better tool. It’s the fundamental shift from finance as a cost center focused on compliance and historical reporting, to finance as a growth engine that informs decision-making, identifies opportunities and mitigates risk—in real time. Strategic finance means your CFO and team spend their days analyzing “what if” scenarios, identifying correlations between operational metrics and financial performance, forecasting with confidence and providing real-time insights that shape strategy. It means moving from answering “What happened?” to answering “What should we do?”
The State of AI: How war will be changed forever – MIT Technology Review – Helen Warrell and James O’Donnell. November 17, 2025. Welcome back to The State of AI, a new collaboration between the Financial Times and MIT Technology Review. Every Monday, writers from both publications debate one aspect of the generative AI revolution reshaping global power. In this conversation, Helen Warrell, FT investigations reporter and former defense and security editor..
The state of AI in 2025: Agents, innovation, and transformation. November 5, 2025 | Survey
Key findings:
- Most organizations are still in the experimentation or piloting phase: Nearly two-thirds of respondents say their organizations have not yet begun scaling AI across the enterprise.
- High curiosity in AI agents: Sixty-two percent of survey respondents say their organizations are at least experimenting with AI agents.
- Positive leading indicators on impact of AI: Respondents report use-case-level cost and revenue benefits, and 64 percent say that AI is enabling their innovation. However, just 39 percent report EBIT impact at the enterprise level.
- High performers use AI to drive growth, innovation, and cost: Eighty percent of respondents say their companies set efficiency as an objective of their AI initiatives, but the companies seeing the most value from AI often set growth or innovation as additional objectives.
- Redesigning workflows is a key success factor: Half of those AI high performers intend to use AI to transform their businesses, and most are redesigning workflows.
- Differing perspectives on employment impact: Respondents vary in their expectations of AI’s impact on the overall workforce size of their organizations in the coming year: 32 percent expect decreases, 43 percent no change, and 13 percent increases.
BANK FOR INTERNATIONAL SETTLEMENT – BIS
AI agents for cash management in payment systems. BIS Working Papers, by Iñaki Aldasoro and Ajit Desai | No 1310 | 26 November 2025. Payment systems are the lifeblood of modern economies and facilitate the transfer of value between individuals, businesses and governments. We examine how generative artificial intelligence (gen AI), such as ChatGPT, can assist in managing cash and liquidity in real-time gross settlement (RTGS) payment systems. These systems are essential for processing large financial transactions between banks in real time. Managing liquidity within them is a delicate balancing act. Cash managers must ensure there is enough liquidity to process payments without incurring unnecessary costs or delays.
GOVERNANCE
BDO Research: Audit Technology Achieves Maturity, but More Guardrails Needed. Journal of Accountancy, People Powered, Tech Led Audits Unlock New Era in Audit Quality. BDO’s second annual Audit Innovation Survey, released today, finds that the use of advanced technologies in the audit has crossed from cautiously experimental to increasingly mature. The research also finds that as tech adoption increases, the role of the auditor in providing judgment, professional skepticism, and the ability to interpret complex data is becoming more critical than ever before. BDO’s research reveals that advanced technology is enhancing quality, efficiency, transparency, and risk management across the finance department. Leaders report using AI strategically for audit applications like data management and transformation (61%), risk detection and management (54%), automated data entry (50%), fraud detection (45%), and predictive trend analytics (43%). The report also finds that technology is helping facilitate a smoother audit engagement experience. This year, 63% of finance leaders said they believe using technology in the audit leads to more efficient processes and collaboration, an eleven-point jump from last year. The data also suggests that auditor and client technology is increasingly aligned, with 93% of finance leaders report that their auditor’s technology sophistication now matches their own (up from 89% in 2024), and 85% say their auditor’s technology meets or exceeds expectations, an eight-point jump from 77% the previous year.
Despite AI adoption surge, finance leaders’ data governance confidence drops – Despite the value leaders place on advanced technologies, their data governance confidence has declined from 55% rating it “mature” in 2024 to just 46% in 2025. This may suggest growing awareness that new technologies raise the bar for what constitutes strong governance. Additionally, while 92% of finance teams have either implemented AI or plan to do so in the next 12 months, just 43% of organizations say they have a formal AI governance framework in place. This is particularly concerning given the risks finance leaders associate with AI: 82% cite cybersecurity concerns, 80% worry about data privacy, and 71% fear AI-generated inaccuracies. Without robust governance frameworks, these risks can undermine the very trust that advanced technology is meant to reinforce.
PAPERS – NBER
Social Dynamics of AI Adoption November 2025 – Working Paper34488
Artificial Intelligence, Competition, and Welfare – November 2025 – Working Paper 34444
We propose a policy-relevant research agenda examining how market power in up-stream artificial intelligence (AI) affects downstream prices, industry structure, factor returns, and welfare especially whether labor-displacing AI leaves workers worse off. In our open-economy general equilibrium model.
PAPERS – SSRN
Ante, Lennart, Autonomous AI Agents in Decentralized Finance: Market Dynamics, Application Areas, and Theoretical Implications (December 14, 2024). Available at SSRN: https://ssrn.com/abstract=5055677 or http://dx.doi.org/10.2139/ssrn.5055677
This paper investigates the intersection of AI agents—autonomous software entities capable of adapting, learning, and executing multi-step operations—and decentralized finance (DeFi) ecosystems. It highlights how the adaptive decision-making capabilities, flexible governance frameworks, and data-driven optimization strategies of AI agents reshape market coordination and organizational architectures. Drawing on a qualitative analysis of 306 major crypto AI agents, the study introduces a typology that maps their diverse application areas, including algorithmic trading, portfolio management, sentiment-driven communities, and immersive entertainment. To further conceptualize the role of AI in decentralized governance, the paper develops a quadrant-based framework that distinguishes four archetypal system configurations: Traditional DAO Tools, Maximally Distributed Agency, Closed Systems, and AI Dictatorships. These configurations, defined by varying degrees of autonomy and decentralization, reveal critical trade-offs between transparency, efficiency, adaptability, and control. This framework serves as a lens to theorize how AI agents reconfigure trust mechanisms, power dynamics, and decision-making processes in decentralized ecosystems. Grounded in economic and socio-technical theory, the paper positions AI agents as transformative intermediaries in tokenized environments. While demonstrating their capacity to streamline operations, enhance decision quality, and enrich user engagement, the study also addresses the governance risks posed by algorithmic control and systemic opacity. Taken together, the conceptual and empirical insights lay a foundation for ongoing interdisciplinary inquiry into the evolving role of AI agents in decentralized finance.
Mazumder, Pristly Turjo, Human-AI Collaboration with ChatGPT: A Systematic Review of Implications for Finance, Law, and Healthcare (August 17, 2025). Available at SSRN: https://ssrn.com/abstract=5394893 or http://dx.doi.org/10.2139/ssrn.5394893
Balogh, Attila and Didisheim, Antoine and Somoza, Luciano and Tian, Hanqing, AI in Finance and Information Overload (March 03, 2025). Available at SSRN: https://ssrn.com/abstract=5163754 or http://dx.doi.org/10.2139/ssrn.5163754
Artificial intelligence is reshaping financial markets, yet the limits to its rationality remain underexplored. This paper documents information overload in Large Language Models applied to financial analysis. Using earnings forecasts from corporate calls and market reaction predictions from news, we show that predictive accuracy follows an inverted U-shaped pattern, where excessive context degrades performance. Larger LLMs mitigate this effect, increasing the optimal context length. Our findings underscore a fundamental limitation of AI-driven finance: more data is not always better, necessitating empirical tuning to determine the right amount of context for each task.
Almada, Marco, The EU AI Act: logic, content, and implications for finance (February 05, 2025). Available at SSRN: https://ssrn.com/abstract=5125735 or http://dx.doi.org/10.2139/ssrn.5125735
The AI Act is the cornerstone of the EU’s approach to AI regulation. One of the first laws on artificial intelligence to be enacted worldwide, it governs AI technologies with a risk-based approach. This approach aims to foster the adoption of AI systems in the EU by creating a legal framework that balances the economic opportunities created by AI with the need to address the risks that the use of AI might create in terms of protecting public values such as fundamental rights, democracy, and the rule of law. In general terms, the Act relies on an ex-ante assessment by the lawmaker of the risks associated with some AI technologies, which will determine the legal framework applicable to AI-based products and services. Each framework establishes different legal requirements drawn from different strands of EU product safety law (Almada & Petit, 2025) which address the perceived risks that AI creates in that context. In the following pages, we examine the core elements of the AI Act.