Citi and Morgan Stanley are among a group of big banks banding together to uncover hidden risks in AI. It shows how far Wall Street has come in embracing the technology.
Wall Street is as competitive as it gets, but sometimes everyone benefits from working together. And when it comes to understanding the intricacies of Artificial Intelligence, some of Wall Street’s largest financial firms have recognized the benefit of putting their heads together.
Citi and Morgan Stanley are among a group of large global banks banding together to create a working group examining the potential risks they may face when using Artificial Intelligence, according to three sources involved in the project.
While it’s still early days, and specific goals for the group haven’t been established, the hope is that by working together Wall Street will develop a better understanding of how best to use the innovative technology appropriately, according to the sources. The impetus for forming the group earlier this year, one source said, was the recognition that there are risks around the usage of Artificial Intelligence that needed to be addressed as Wall Street continues to adopt more the tech.
Spokespeople for Morgan Stanley and Citi declined to comment.
Wall Street stands to benefit from taking a unified approach to understanding how best to use AI in the absence of direction from regulators. Rulemakers have largely avoided crafting specific regulation pertaining to the appropriate use of AI in finance.
Meanwhile, banks have put significant resources towards development around AI-based tools in recent years with the hopes of cutting cost and gaining a competitive edge. As firms get more comfortable with the technology, the laundry list of use cases where AI can be applied to improve manual, labor-intensive processes continues to grow. Banks are experimenting applying AI to everything from chatbots and fraud detection to more market-facing areas such as trading and risk management.
According to a report published by IHS Markit in April, the global business value of Artificial Intelligence in finance will be $300 billion by 2030. In 2018, the report estimated $41.1 billion in cost savings and efficiencies was recognized thanks to AI’s use on Wall Street.
However, for all the promises of AI improving how things are done, risks still remain. Interpretability and explainability are two major hurdles. The former refers to understanding how an AI-based tool reaches a solution. The latter is the ability to explain to someone — like a regulator — how that solution was reached.
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