## AI’S REVERBERATIONS ACROSS FINANCE

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### Financial embrace and industry investment
- Sales of software, hardware, and services for AI systems will climb 29 percent this year to $166 billion and top $400 billion in 2027, according to International Data Corp.
- Financial sector spending will more than double to $97 billion in 2027, with a 29 percent compound annual growth rate—the fastest of five major industries.
- JPMorgan Chase & Co. had 3,600 AI help-wanted postings in June 2023, according to Evident Insights Ltd.
- JPMorgan spends more than $15 billion a year on technology and deploys almost a fifth of its approximately 300,000 employees in technology roles; an AI research group employs 200.
- Hedge funds: nearly half use ChatGPT professionally; more than two-thirds of those use it to write marketing text or summarize reports or documents, per a BNP Paribas survey of funds with $250 billion in combined assets.
- Amundi SA (Europe’s largest investment company) with €2 trillion ($2.1 trillion) under management uses AI-based tools to customize portfolios for some of its more than 100 million clients.

### Central bank and supervisory applications
- Brazil’s central bank built a prototype robot to download consumer complaints about financial institutions and categorize them through machine learning.
- The Reserve Bank of India hired McKinsey and Accenture in 2023 to help deploy AI and related analytics in its supervision work.
- The Basel Committee on Banking Supervision found that AI can make lending more efficient in credit decisions and in thwarting money laundering; it cited risks such as understanding outcomes from opaque models, potential for bias, and greater cyber risks.
- The Bank for International Settlements (BIS) Innovation Hub’s Project Aurora demonstrated neural networks can help detect money laundering by identifying patterns and anomalies in transactions.
- The Bank of Canada built a machine learning tool to detect anomalies in regulatory submissions; automated daily runs free up staff for follow-up analysis.
- The European Central Bank (ECB) automates classification of data from 10 million business and government entities, scrapes websites to track product prices in real time, and uses AI to help bank supervisors parse news stories, supervisory reports, and corporate filings.
- The ECB is exploring large language AI models to help write code, test software, and make public communications easier to understand.

### Benefits, limitations, and operational cautions
- AI enables uses ranging from prospecting and marketing to risk management, fraud prevention, payment processing, and money movement systems worldwide.
- AI can help clean and interpret exponentially growing data, especially unstructured data, improving human ability to make important distinctions.
- Limitations: AI can be limited by unreliable data or by unprecedented high-impact situations; wholly AI-driven processes can be dangerous without human interpretation, understanding, and checks on algorithmic outputs.
- “Artificial intelligence cannot replace the brain,” and interpretation and oversight remain essential.

### Financial stability risks and systemic concerns
- US Securities and Exchange Commission Chair Gary Gensler warned AI could spark a financial crisis and demand “new thinking on systemwide or macroprudential policy interventions,” noting AI may heighten financial fragility by promoting herding when actors receive the same signal from a base model or data aggregator.
- Jon Danielsson (London School of Economics): AI’s advantage diminishes with complexity; humans retain an edge in unexpected situations drawing on economics, history, ethics, and philosophy.
- Anselm Küsters: AI tools may exacerbate crises because they are trained on past data that may not reflect reality in unprecedented situations; algorithmic prediction can create new systemic risks and amplify negative feedback loops, especially in a “polycrisis” environment.
- The Basel Committee and BIS emphasize risks from opaque models, bias, and cyber vulnerabilities, while noting supervisory processes will need to evolve to distinguish responsible from irresponsible innovation.

### Aggregate implications and talent dynamics
- Demand for AI talent in finance is global; three of the top 10 cities in Evident’s talent index are in India.
- Rapid adoption of AI across financial institutions and central banks presents both opportunities (efficiency, inclusion, improved supervision) and threats (bias, opacity, cyber risk, systemic amplification).
- Financial institutions foresee AI as “an absolute necessity” for competitiveness and operational capability.

*Source: “AI’S REVERBERATIONS ACROSS FINANCE,” Finance & Development, December 2023.*

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_Source: https://www.imf.org/-/media/files/publications/fandd/article/2023/december/38-41-kearns-final.pdf_