Since ChatGPT's release in 2022, demand for artificial intelligence (AI)–related skills in finance has grown rapidly, as generative AI drives significant technological changes in both the financial research field and the broader economy. We show that financial occupations are highly exposed to the productivity effects of generative AI, review the literature on the impact of ChatGPT on firm value, and provide directions for future research investigating the impact of this major technology shock. Generative AI also holds great potential as a tool for finance researchers and practitioners: We review and describe innovations in research methods linked to improvements in AI tools, along with their applications. We offer a practical introduction to available tools and advice for researchers in academia and industry interested in using these tools.
Artificial intelligence and personal finance
Artificial intelligence (AI) is transforming how consumers access and use financial information, education and advice for personal financial decision making. While consumers’ increasing use of AI tools and AI-generated content for personal finance brings opportunities in terms of accessibility, personalisation and decision making, it also increases risks related to bias, hallucinations, commercial influence, data privacy and exclusion, with uncertain benefits on long-term financial well-being. This policy paper provides policymakers and stakeholders with an overview of current trends, opportunities and risks in the use of AI in personal finance and in the design and delivery of financial education. It also proposes a set of financial literacy competencies to support the use of AI in personal financial decision making.
Fi Money rolls out AI feature for users' queries on personal finance via ChatGPT, Gemini- Moneycontrol.com
Until now, users relied on generic prompts and manual inputs to use AI tools for financial advice, Fi has replaced this with a structured, consent-driven way to share real financial data, the neo-banking firm's co-founder Sumit Gwalani has said
Artificial Intelligence for Economic Development Conference: Roundup of 27 presentations
Is artificial intelligence the future for economic development? Earlier this month, a group of World Bank staff, academic researchers, and technology company representatives convened at a conference in San Francisco to discuss new advances in artificial intelligence. One of the takeaways for Bank staff was how AI technologies might be ...
Gender disparities in the impact of generative artificial intelligence: Evidence from academia
Abstract. The emergence of generative artificial intelligence (AI) tools such as ChatGPT has substantially increased individuals’ productivity. In this stu
Investing for Programmers
Maximize your portfolio, analyze markets, and make data-driven investment decisions using Python and generative AI.
In spite of hype, many companies are moving cautiously when it comes to generative AI | TechCrunch
Companies are extremely interested in generative AI as vendors push potential benefits, but turning that desire from a proof of concept into a working product is proving much more challenging.
The CEO’s Guide to Generative AI: Cost of compute
The IBM Institute for Business Value uses data-driven research and expert analysis to deliver thought-provoking insights to leaders on the emerging trends that will determine future success.'
Consumer Finance AI Standard
AI (artificial intelligence) is making consequential decisions about consumers’ financial lives, including who gets access to credit, which claims get paid, what financial products consumers are shown, and how users are advised to manage their accounts, among many others. It’s doing this at scale, largely out of sight, and with almost no accountability when it
The end of theory? AI and ignorance in financial markets
AI’s growing role in finance challenges traditional expectations of transparency and theoretical understanding. While machine learning (ML) models enhance financial decision-making, they remain largely agnostic to established financial theories, producing knowledge and ignorance in ways that differ from traditional models like VaR, DCF, and Black-Scholes. This essay explores the decoupling of AI models from theoretical financial knowledge and the resulting forms of ignorance. Using 22 semi-structured interviews, we investigate how ML models generate epistemic uncertainties. We focus on causal ignorance: AI systems, including those supported by XAI, fail to provide genuine causal explanations. Because understanding causation is inherently theoretical, AI-driven finance remains theory-agnostic and marked by theoretical ignorance. We explore how this ignorance differs from that of traditional models and what it implies for the role of theory in finance. Finally, we present three possible scenarios for the future of theory in finance and outline directions for further research.
AI security issues dominate corporate worries, spending
Two reports illustrate how business leaders are thinking about and budgeting for generative AI.
How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt
The term technical debt is often used to refer to the accumulation of design or implementation choices that later make the software harder and more costly to understand, modify, or extend over time...
AI is probably not a bubble
AI companies have revenue, demand, and paths to immense value
AI is probably not a bubble
AI companies have revenue, demand, and paths to immense value
AI Agents Will Be The Key To Achieving ROI From AI
AI agents are emerging as a crucial type of application for enterprises looking to derive more value from their AI investments in the near term.
The AI productivity myth is more harmful than you think
Perceived productivity may be up thanks to AI, but there's debt collecting in the shadows.
Ideation with Generative AI—in Consumer Research and Beyond
Abstract The use of generative AI (genAI) in consumer research is rapidly evolving, with applications including synthetic data generation, data analysis, and more. However, their role in creative ideation—a cornerstone of consumer research—remains underexplored. Drawing on the human creativity literature, we propose that ideation with genAI is facilitated by its productivity and semantic breadth, which are psychologically analogous to the dual pathways of persistence and flexibility in human ideation. Further, we distinguish between the utility of genAI as a key ideator versus humans as key ideator, conceptualized through the genAI ideation roles of Designer and Writer and of Interviewer and Actor. While genAI excels in generating incremental improvements, its potential for groundbreaking innovation could be unlocked by leveraging its ability to prompt human creativity. This article advances the theoretical and practical understanding of genAI in ideation for consumer research, offering numerous practical guidelines for integrating generative AI into research while emphasizing human–AI collaboration to achieve radical insights.
