AI in Finance and Banking, May 15, 2024

This semi-monthly column highlights news, government reports, NGO/IGO papers, industry white papers, academic papers and speeches 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.


The New York Times, May 14, 2024. Senators Propose $32 Billion in Annual A.I. Spending but Defer Regulation Their plan is the culmination of a yearlong listening tour on the dangers of the new technology…The lawmakers recommended creating a federal data privacy law and said they supported legislation, planned for introduction on Wednesday, that would prevent the use of realistic misleading technology known as deepfakes in election campaigns. But they said congressional committees and agencies should come up with regulations on A.I., including protections against health and financial discrimination, the elimination of jobs, and copyright violations caused by the technology.

Cryptopolitan, May 13, 2024. The Pitfalls of Mixing Up AI and Automation in Finance. Forex (FX) trading, the distinctions between automated trading, traditional artificial intelligence (AI), and generative AI are becoming increasingly difficult to distinguish. This confusion is a fundamental obligation that may compromise the sustainability of trading services with unrealistic needs. There is a great need to make clear the differences among these technologies so that they will be incorporated effectively and keep a competitive edge.

Forbes, May 14, 2024. Leveraging AI In Finance—Move From Theory To Practice. There is a reason why GenAI is gaining some traction in compliance and risk management. Simply stated, the benefits of deploying AI tools to strengthen these activities are proving to be well worth the investment. For example, finance groups are currently deploying GenAI tools to complete standard compliance forms and bolster fraud detection and protection. AI’s ability to scrutinize vast data sets and then identify anomalies and predict trends gives finance groups, compliance teams, and data privacy and security groups an opportunity to get a jump on fraudulent activity. Predictive detection helps retailers reduce shrinkage and bolster loss prevention efforts. Insurers leverage AI to respond quickly to suspicious variations in claim patterns. CISOs can spot privacy breaches as they occur, and companies in cyclical industries use AI to detect and address peculiar purchasing patterns.

FT Advisor, April 26, 2024. How AI will change financial services. In today’s evolving banking, financial services and insurance landscape, the integration of artificial intelligence has become imperative for service providers to stay competitive and meet the increasing demands of its customers. Regulatory pressure, enhancing the customer experience and ensuring platforms are robust and secure against the increasing threat landscape, are all front of mind for business leaders. So, what are the challenges or opportunities? Here we explore the role of AI in revolutionising BFSI.

Forbes, April 14, 2024. The Future Of Banking: Morgan Stanley And The Rise Of AI-Driven Financial Advice

IMF. Rising Cyber Threats Pose Serious Concerns for Financial Stability. Greater digitalization and heightened geopolitical tensions imply that the risk of a cyberattack with systemic consequences has risen. Cyberattacks have more than doubled since the pandemic. While companies have historically suffered relatively modest direct losses from cyberattacks, some have experienced a much heavier toll. US credit reporting agency Equifax, for example, paid more than $1 billion in penalties after a major data breach in 2017 that affected about 150 million consumers. As we show in a chapter of the April 2024 Global Financial Stability Report, the risk of extreme losses from cyber incidents is increasing. Such losses could potentially cause funding problems for companies and even jeopardize their solvency. The size of these extreme losses has more than quadrupled since 2017 to $2.5 billion. And indirect losses like reputational damage or security upgrades are substantially higher. The financial sector is uniquely exposed to cyber risk. Financial firms—given the large amounts of sensitive data and transactions they handle—are often targeted by criminals seeking to steal money or disrupt economic activity. Attacks on financial firms account for nearly one-fifth of the total, of which banks are the most exposed.

The New York Times, February 1, 2024. A new generation of artificial intelligence is poised to turn old assumptions about technology on their head. For years, people working in warehouses or fast food restaurants worried that automation could eliminate their jobs. But new research suggests that generative A.I. — the kind used in chatbots like OpenAI’s ChatGPT — will have its biggest impact on white-collar workers with high-paying jobs in industries like banking and tech. A report published Thursday by the Burning Glass Institute, a nonprofit research center, and SHRM, formerly the Society for Human Resource Management, stops short of saying the technology will do away with large numbers of jobs. But it makes clear that workers need to better prepare for a future in which A.I. could play a significant role in many workplaces that until now have been largely untouched by technological disruption.


NBER. Artificial Intelligence and the Skill PremiumDavid E. Bloom, Klaus Prettner, Jamel Saadaoui & Mario Veruete Working Paper 32430. DOI 10.3386/w32430. Issue Date How will the emergence of ChatGPT and other forms of artificial intelligence (AI) affect the skill premium? To address this question, we propose a nested constant elasticity of substitution production function that distinguishes among three types of capital: traditional physical capital (machines, assembly lines), industrial robots, and AI. Following the literature, we assume that industrial robots predominantly substitute for low-skill workers, whereas AI mainly helps to perform the tasks of high-skill workers. We show that AI reduces the skill premium as long as it is more substitutable for high-skill workers than low-skill workers are for high-skill workers.

NBER. Identifying Monetary Policy Shocks: A Natural Language Approach. S. Borağan Aruoba & Thomas Drechsel. Working Paper 32417. DOI 10.3386/w32417. Issue Date We develop a novel method for the identification of monetary policy shocks. By applying natural language processing techniques to documents that Federal Reserve staff prepare in advance of policy decisions, we capture the Fed’s information set. Using machine learning techniques, we then predict changes in the target interest rate conditional on this information set and obtain a measure of monetary policy shocks as the residual. We show that the documents’ text contains essential information about the economy which is not captured by numerical forecasts that the staff include in the same documents. The dynamic responses of macro variables to our monetary policy shocks are consistent with the theoretical consensus. Shocks constructed by only controlling for the staff forecasts imply responses of macro variables at odds with theory. We directly link these differences to the information that our procedure extracts from the text over and above information captured by the forecasts.


A Roadmap for Artificial. Intelligence Policy in the U.S. Senate. The Bipartisan Senate AI Working Group. May 15, 2024. Maj. Leader Chuck Schumer, Sen. Mike Rounds, Sen. Martin Heinrich, and Sen. Todd Young. Our Bipartisan Senate AI Working Group was brought together by a shared recognition of the profound changes artificial intelligence (AI) will bring to our world and the need to proactively work to harness the opportunities and address the risks of this transformational technology. To help the Senate better understand the policy landscape of AI, we convened nine AI Insight Forums earlier this Congress. These forums were designed to complement committee hearings and sought to identify areas of consensus, as well as areas of disagreement, while also revealing where further work and research is needed. This roadmap summarizes our findings and lays out a number of policy priorities that we believe merit bipartisan consideration in the Senate in the 118th Congress and beyond. Ultimately, it is our hope this roadmap helps to inform consideration of bipartisan AI legislation, ensure the United States remains at the forefront of innovation in AI, and helps all Americans benefit from the opportunities created by AI.

Posted in: AI, AI in Banking and Finance, Congress, Cybersecurity, Economy, Financial System