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On Thu, 12 Dec, 8:03 AM UTC
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AI a productivity boost to banks but making money from it is a challenge
NEW YORK (Reuters) - Artificial intelligence is expected to show big gains in productivity at banks, panelists said at the Reuters Next conference in New York, but it has so far been harder to make money from the technology. Major banks have been applying AI to virtual assistants for clients and introduced tools for employees to use for human resources, risk, compliance and finance as well as trying to develop products within wealth management. Goldman Sachs Chief Executive David Solomon told the Reuters Conference on Tuesday that deploying machine learning and AI could improve productivity in areas including coding. "We have 11,000 engineers. We do an enormous amount of coding," Solomon said. "If we can increase with these tools their coding productivity by 20 or 30%, it's a huge tailwind for us." Fellow U.S. bank BNY is also investing in AI tools, CEO Robin Vince told the conference. "We have thousands of people at BNY who are now enabled to be able to build and commission agents to be able to help them with their daily tasks," Vince said. However, AI is not helping financial firms make money yet. Banks still need to specify their exact use cases for the technology, the chief AI and data officer of BMO Financial Group told the conference on Wednesday. "The hype cycle brought a lot of positive attention to this space. I am chief AI officer now because there was a little bit of a hype cycle," said Kristin Milchanowski, who was appointed to the role at BMO, one of Canada's largest banks, in October. "I do believe that people thought it was going to impact the revenue or have a cost takeout different from what the effect actually has been," Milchanowski said, adding, "We're not seeing revenue-generating activity." So far, AI has been most useful in tasks such as shrinking the time BMO's equities teams need to produce reports - an important part of many investment banks' offerings - from more than four hours a day to less than one, leaving the analysts free to do more creative tasks. It is important to identify specific use cases for AI in the future, Milchanowski added, and pinpointed potential applications in optimising trades and generating clients. (Reporting by Isla Binnie and Megan Davies; additional reporting by Saeed Azhar and Davide Barbuscia; Editing by Rosalba O'Brien)
[2]
AI a productivity boost to banks but making money from it is a challenge
NEW YORK, Dec 11 (Reuters) - Artificial intelligence is expected to show big gains in productivity at banks, panelists said at the Reuters Next conference in New York, but it has so far been harder to make money from the technology. Major banks have been applying AI to virtual assistants for clients and introduced tools for employees to use for human resources, risk, compliance and finance as well as trying to develop products within wealth management. Goldman Sachs (GS.N), opens new tab Chief Executive David Solomon told the Reuters Conference on Tuesday that deploying machine learning and AI could improve productivity in areas including coding. "We have 11,000 engineers. We do an enormous amount of coding," Solomon said. "If we can increase with these tools their coding productivity by 20 or 30%, it's a huge tailwind for us." Fellow U.S. bank BNY (BK.N), opens new tab is also investing in AI tools, CEO Robin Vince told the conference. "We have thousands of people at BNY who are now enabled to be able to build and commission agents to be able to help them with their daily tasks," Vince said. However, AI is not helping financial firms make money yet. Banks still need to specify their exact use cases for the technology, the chief AI and data officer of BMO (BMO.TO), opens new tab Financial Group told the conference on Wednesday. "The hype cycle brought a lot of positive attention to this space. I am chief AI officer now because there was a little bit of a hype cycle," said Kristin Milchanowski, who was appointed to the role at BMO, one of Canada's largest banks, in October. "I do believe that people thought it was going to impact the revenue or have a cost takeout different from what the effect actually has been," Milchanowski said, adding, "We're not seeing revenue-generating activity." So far, AI has been most useful in tasks such as shrinking the time BMO's equities teams need to produce reports - an important part of many investment banks' offerings - from more than four hours a day to less than one, leaving the analysts free to do more creative tasks. It is important to identify specific use cases for AI in the future, Milchanowski added, and pinpointed potential applications in optimising trades and generating clients. Reporting by Isla Binnie and Megan Davies; additional reporting by Saeed Azhar and Davide Barbuscia; Editing by Rosalba O'Brien Our Standards: The Thomson Reuters Trust Principles., opens new tab Suggested Topics:Artificial Intelligence Isla Binnie Thomson Reuters Isla Binnie reports on how company directors and executives manage stakeholder and shareholder interests, with a focus on compensation, corporate crises, dealmaking and succession. She also covers how politics, regulation, environmental issues and the broader economy affect boardroom discussions. Isla previously covered business, politics and general news in Spain and Italy. She trained with Reuters in London and covered emerging markets debt for the International Financing Review (IFR).
[3]
AI a productivity boost to banks but making money from it is a challenge
Major banks have been applying AI to virtual assistants for clients and introduced tools for employees to use for human resources, risk, compliance and finance as well as trying to develop products within wealth management. Goldman Sachs chief executive David Solomon told the Reuters Conference on Tuesday that deploying machine learning and AI could improve productivity in areas including coding.Artificial intelligence is expected to show big gains in productivity at banks, panelists said at the Reuters Next conference in New York, but it has so far been harder to make money from the technology. Major banks have been applying AI to virtual assistants for clients and introduced tools for employees to use for human resources, risk, compliance and finance as well as trying to develop products within wealth management. Goldman Sachs chief executive David Solomon told the Reuters Conference on Tuesday that deploying machine learning and AI could improve productivity in areas including coding. "We have 11,000 engineers. We do an enormous amount of coding," Solomon said. "If we can increase with these tools their coding productivity by 20 or 30%, it's a huge tailwind for us." Fellow U.S. bank BNY is also investing in AI tools, CEO Robin Vince told the conference. "We have thousands of people at BNY who are now enabled to be able to build and commission agents to be able to help them with their daily tasks," Vince said. However, AI is not helping financial firms make money yet. Banks still need to specify their exact use cases for the technology, the chief AI and data officer of BMO Financial Group told the conference on Wednesday. "The hype cycle brought a lot of positive attention to this space. I am chief AI officer now because there was a little bit of a hype cycle," said Kristin Milchanowski, who was appointed to the role at BMO, one of Canada's largest banks, in October. "I do believe that people thought it was going to impact the revenue or have a cost takeout different from what the effect actually has been," Milchanowski said, adding, "We're not seeing revenue-generating activity." So far, AI has been most useful in tasks such as shrinking the time BMO's equities teams need to produce reports - an important part of many investment banks' offerings - from more than four hours a day to less than one, leaving the analysts free to do more creative tasks. It is important to identify specific use cases for AI in the future, Milchanowski added, and pinpointed potential applications in optimising trades and generating clients.
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Major banks are leveraging AI for productivity gains, but struggle to generate revenue from the technology. While AI enhances efficiency in various operations, financial institutions are still exploring ways to monetize these advancements.
Major banks are increasingly adopting artificial intelligence (AI) to boost productivity across various operations. At the Reuters Next conference in New York, industry leaders highlighted the significant potential of AI in enhancing efficiency, particularly in coding and daily tasks 123.
Goldman Sachs CEO David Solomon emphasized the potential productivity gains in coding:
"We have 11,000 engineers. We do an enormous amount of coding. If we can increase with these tools their coding productivity by 20 or 30%, it's a huge tailwind for us." 1
Similarly, BNY Mellon CEO Robin Vince reported widespread AI adoption within the bank:
"We have thousands of people at BNY who are now enabled to be able to build and commission agents to be able to help them with their daily tasks." 2
Banks are currently applying AI in several areas:
BMO Financial Group has seen significant time savings in report generation. Kristin Milchanowski, Chief AI and Data Officer at BMO, noted:
"AI has been most useful in tasks such as shrinking the time BMO's equities teams need to produce reports - an important part of many investment banks' offerings - from more than four hours a day to less than one, leaving the analysts free to do more creative tasks." 3
Despite these productivity gains, banks are struggling to generate direct revenue from AI investments. Milchanowski, appointed to her role at BMO in October, candidly addressed this issue:
"The hype cycle brought a lot of positive attention to this space. I am chief AI officer now because there was a little bit of a hype cycle. I do believe that people thought it was going to impact the revenue or have a cost takeout different from what the effect actually has been. We're not seeing revenue-generating activity." 123
As banks continue to explore AI applications, industry experts emphasize the importance of identifying specific use cases. Milchanowski highlighted potential future applications:
The banking sector remains optimistic about AI's potential, but recognizes the need for more targeted strategies to translate productivity gains into revenue growth 3.
Reference
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