The International Swaps and Derivatives Association (ISDA) published a whitepaper on generative AI (genAI) identifying a range of potential use cases in the derivatives market, including document creation, market insight and risk profiling. It also explores regulatory issues in key jurisdictions and addresses the challenges and risks associated with the use of genAI.
The paper concludes with a set of recommendations for stakeholders. These include investing in talent development, fostering collaboration and knowledge sharing with technology providers, prioritizing ethical AI principles and engaging with policymakers to promote an appropriate regulatory framework.
“The rapid development of artificial intelligence has generated considerable attention, both within financial markets and across society more broadly. As the technology advances, there is a significant opportunity for genAI to support more efficient, data-driven decision making in the derivatives market, but we need to approach this carefully, making sure the implications and risks of the technology are properly addressed. Fresh perspectives will be needed as future opportunities and challenges are considered,” said Scott O’Malia, ISDA’s chief executive in a statement.
Use cases and risks
There are several promising use cases for genAI in the derivatives market. The first relates to its ability to create new language based on precedent and synthesize data into a human-readable summary. GenAI is a useful tool for market participants to summarize complex derivatives agreements and suggest clauses based on deal terms and firms’ existing precedent agreements, which has the potential to significantly decrease negotiation and drafting costs. In addition, genAI can be used to extract unstructured data from derivatives documentation to provide summaries of derivatives transactions required for operations and front-office processes.
GenAI can also synthesize various jurisdictional regulations and present these in an easy-to-read format, comply with industry or firm standards and provide checks against trades and trade documentation. While not a replacement for a human lawyer, it can significantly accelerate the review process and act as an additional regulatory compliance check.
The second use case identified is with respect to genAI’s use in application development to propose new code changes. McKinsey has estimated that using genAI in this way can make coding up to 56% faster. The third use case is to analyze data, including nuanced human emotion data, to provide market insights that can be useful in trading.
The fourth use case is to improve operational efficiencies, such as to summarize margin and collateral requirements for the business and assist in selecting the least costly collateral or create synthetic data that can be used for model testing. Finally, genAI can be used to assist in the development of derivatives markets in emerging markets, by aiding firms in summarizing local regulations and market conditions, paving the way for a more efficient entry into such markets.
Governments are also looking at use cases for genAI and proposing regulations to safeguard consumers and financial markets. These proposals are still in their infancy, but reviewing the current state shows the direction in which regulators and policymakers are heading.
While these use cases offer great efficiencies, the use of genAI does not come without its challenges and risks. Due to the nature of genAI and the large amount of data needed to train the models, data breaches can be a significant challenge and lead to reputational, confidentiality, intellectual property and legal risks. The use of genAI for trading can also create regulatory issues and, without proper oversight, could lead to fines and sanctions from financial regulators.
Additionally, genAI is associated with producing bias and could be used to discriminate against protected classes, leading to civil and possible criminal liability for companies. Lastly, there is significant risk of model failure, in which the results produced are sub-standard or simply false. This could lead to erroneous trades and diminished trust within a financial institution.
The whitepaper was written by ISDA Future Leaders in Derivatives (IFLD), its professional development program for emerging leaders in the derivatives market, represented by 38 individuals representing buy- and sell-side institutions, law firms and service providers from around the world.
“This year’s IFLD group was drawn from a diverse range of institutions and jurisdictions, and we have worked together over the past six months to explore the development of genAI in the global derivatives market. It is clear this technology has the potential to add significant value to multiple industry processes. We hope the paper will help market participants, policymakers and other stakeholders as they look to harness the technology and address the associated challenges,” said IFLD participant Takuya Otani, director for Counterparty Portfolio Management Desk at Mizuho, in a statement.