GenAI can help transform OTC derivatives markets, said ISDA whitepaper

The risks of GenAI, however, include data breaches, regulatory issues, bias, as well as sub-standard or simply false results.

A whitepaper by ISDA Future Leaders in Derivatives (IFLD) made the case for harnessing the power of generative artificial intelligence (genAI) in transforming the over-the-counter (OTC) derivatives market.

There are several promising use cases for genAI in the derivatives market, the whitepaper argued, including the ability to create new language based on precedent and synthesize data into a human-readable summary.

The use cases of GenAI

Besides being able to summarize complex derivatives agreements and suggest clauses based on deal terms and firms’ existing precedent agreements, genAI can be used to extract unstructured data from derivatives documentation to provide summaries of derivatives transactions required for operations and front-office processes.

Additionally, GenAI can synthesize various jurisdictional regulations, comply with industry or firm standards and provide checks against trades and trade documentation. The whitepaper warns genAI is, however, no replacement for a human lawyer.

The second use case for genAI is in application development to propose new code changes as genAI can make coding up to 56% faster, according to McKinsey. 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.

The risks of GenAI

However, 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 a significant risk of model failure, in which the results produced are sub-standard or simply false.

To conclude, the whitepaper argues that firms should implement comprehensive cyber security and data security policies to safeguard their IT systems from cyberattacks and malicious use of genAI technology. Lastly, firms should develop model risk mitigation policies to ensure the models conform to expected results and any deviation or failure can be quickly corrected.