Most-read AI stories of 2024

December 26, 2024 2:45 PM

Banks were alternately bashful and boastful about their AI projects in 2024. Some, like JPMorgan Chase, were happy to share ambitious rollouts of generative AI to all employees. Others remained reserved and cautious, waiting for a green light from regulators before publicly sharing any sweeping AI initiatives. The following are the AI stories American Banker readers clicked on the most.

Mario Tama/Photographer: Mario Tama/Getty I

Why Citi is rolling out generative AI to all its developers

Like most large banks, Citi is evaluating hundreds of use cases for generative AI, assessing the business impact and risks of each. But it is moving forward quickly on a few.

For instance, the bank will have a road map for rolling out GitHub Copilot to all developers – about 40,000 employees – by mid-April, according to Shadman Zafar, CIO of personal banking and wealth management at Citi and lead for its generative AI work. This should save a lot of time, especially where reusable code can be found within the bank's own repository. Citi is also using generative AI to modernize legacy systems and do first drafts of compliance assessments, among other things.

"I do believe it's a technology that will, in a sustainable way, have a long-term impact on how we do work for a couple of decades to come," Zafar said in an interview.

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'It's a frenzy': JPMorgan Chase, Capital One dominate AI arms race

Over the past twelve months, the largest banks have been in an AI arms race. An industry ranking reveals how they are fighting for the prize.

JPMorgan Chase, Capital One and Royal Bank of Canada have been named the top AI leaders in banking in Evident's AI Index for 2024. Three banks, Morgan Stanley, TD Financial Group and HSBC, made it into the top ten for the first time. Wells Fargo, Citi, CommBank in Australia and UBS round out the first ten on the list.

Evident's team of data scientists annually assesses 50 of the largest banks in North America, Europe and Asia in the categories of AI talent, innovation, leadership and transparency, using mostly publicly available information.

"This year — I've never seen anything like it. It's a frenzy and it's super competitive," said Alexandra Mousavizadeh, CEO of Evident, in an interview. "So even while you might see some of the same banks at the top, what we've seen is that the leading banks are moving at a pace that is double the rest of the banks."

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Banking on AI: How financial institutions are deploying new tech

Despite both consumer and institutional interest in artificial intelligence continuing to grow across the financial services industry, the majority of leaders are still unsure about the technology and its potential uses — leaving a select group of executives to lead their organizations into the fray.

Arizent, the publisher of American Banker, surveyed 127 financial institution professionals to find out how traditional and generative AI is unfolding in the industry with respect to applications, risks versus rewards, impact on the workforce and more.

Respondents represent banks ranging from less than $10 billion of assets to more than $100 billion of assets, as well as credit unions of all asset sizes.

The results showed that familiarity is the largest hurdle for adoption. Tech-minded changemakers helping prepare their organizations for AI said the top two things they are doing are researching providers and attending industry conferences or events on AI. They are also creating working groups for responsible AI usage and educating stakeholders.

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A third of banks ban employees from using gen AI. Here's why.

Though financial services leaders are aware of and hopeful about the benefits of using artificial intelligence to become more efficient and to help employees do their jobs, 30% still ban the use of generative AI tools within their company, according to a survey conducted by Arizent, publisher of American Banker.

About 15% said they have completely banned the use of generative AI — algorithms that can be used to create new content — for all employees. Another 20% said they restrict use of gen AI to specific employees for limited functions or roles; another 26% said they don't ban gen AI today, but they are considering putting a policy in place. Asked if they're going to loosen or remove employee restrictions on publicly available generative AI tools in the next year, 39% said no; 57% said maybe.

A key thing that holds bankers back from using generative AI is the difficulty of assessing the risk of a gen AI application, according to Chris Nichols, director of capital markets at SouthState Bank, a $45 billion-asset institution based in Winter Haven, Florida, and early adopter of generative AI.

"The mere fact that generative AI does not answer a question the same way each time gives bankers cause for concern," Nichols said. "No model in the human experience has been like that — not traditional AI, not Google search and not any bank model."

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Fraud, productivity are top of mind for AI thought leaders in banks

Bank technology executives honored on a list of artificial intelligence leaders released Wednesday see no letup in their companies of AI investment and effort, and are focused on practical use cases like fraud detection and personalization.

Krish Swamy, chief data and analytics officer at Citizens Financial Group, expects the bank's investments in AI in 2025 and 2026 will be higher than in 2024.

"We will do fewer things and do them really, really well versus trying to do 20 different things and expecting them to take flight," he said in an interview.

"My sense is that there is still an ongoing, sharp increase in AI investment in the broader industry, which I would expect to continue for some time," said Dan Jermyn, chief AI officer at Commonwealth Bank of Australia. "Unlike most other technology advancements, the implications of AI are profound for every aspect of a modern financial services organization, and so this is as much about investing in organizational transformation as it is in being able to deploy the latest models."

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The racial wealth gap is getting wider. Can technology fix it?

Four years ago, George Floyd was choked to death by a police officer after trying to use a possibly counterfeit $20 bill at a Minneapolis convenience store. Widespread outrage about the killing spurred the largest U.S. banks to vow to do their part to fix the inequalities in the American financial system.

Despite the tens of billions of dollars banks have spent, the racial wealth gap has actually widened over this time period.

According to the Federal Reserve Board's most recent report on racial inequality, median wealth among white families was $285,000 in 2022, compared with $44,900 for Black families. That's a difference of about $241,000. In 2019, the difference was roughly $191,000. For Hispanic families, the median wealth totaled $61,600 in 2022. That means the wealth gap between Hispanic and white families totaled $224,000, up from roughly $177,000 just three years earlier.

And while 72.7% of white Americans own their own home, only 44% of Black Americans do, according to the National Association of Realtors. Among Hispanic families, the home ownership rate is 50.6%; among Asian families, it's 62.8%. Black people account for only 4.3% of the 22.2 million business owners in the U.S.

"The reality is, white America and people of color America are living in two different financial realities," said Silvio Tavares, CEO of VantageScore. "And as Americans, we know that that's not sustainable. Putting aside the moral aspects of it, just as a business proposition, that's just not sustainable."

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'More impactful than the internet': How AI will reshape banking jobs

One of the fundamental questions leaders in the banking world have looked to answer over the past year is whether artificial intelligence will replace human jobs.

Views on the matter vary wildly.

Some anticipate the impact of AI on banking jobs will be akin to the effects of automated teller machines on tellers. That's the view of Zor Gorelov, CEO of Kasisto, a conversational AI company that builds products for the finance and banking sector.

"Look at the ATM — it didn't replace tellers, it just took over some of the more simple tasks, but many people still count on bankers to handle some of the more complex activities within the branch," Gorelov said. "AI is doing the same across a number of functions within the banking ecosystem."

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American Express unveils its approach to generative AI

American Express is sitting on a trove of data, from cardholder transactions to merchant records, that it hopes will aid its foray into generative artificial intelligence in the future.

For now, Amex is leaving customer data out of its early pilots as it explores generative AI. The financial services company is sticking to third-party generative AI models and copilots as it unveils its approach to this technology, which has included experiments with customer service, coding, content creation and marketing. Its early results have been promising: Counselors in its travel agency, for example, reported that conducting research using an AI copilot has reduced customer call times by about a minute, while engineers using a copilot for coding estimate it has cut down their workload by 10%.

Amex's approach is representative of financial services companies edging into the world of generative AI but hesitant to mix customer data into their usage.

Bhavi Mehta, the global head of AI for financial services at Bain, finds that the vast majority of financial services companies use closed-source generative AI models from OpenAI, Google or Microsoft, while larger institutions with more resources have also tilted toward open-source models from Llama, Mistral and the like. As usage mostly revolves around closed-source models or established models with public data, "it creates a lot more perceived risk," said Mehta. "Almost every financial services organization is starting with non-PII [personally identifiable information] and non-customer data," such as internal policies and procedures.

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RBC rolls out AI-based tools to financial advisors

Royal Bank of Canada's U.S. wealth management arm has partnered with TIFIN AG to provide RBC's team of advisors with a suite of artificial intelligence tools for deepening client relationships and spotting potential missed opportunities.

"Your typical advisors are not a big fan of change and sometimes are slow to adopt or embrace [new technology]. But there was a natural curiosity around this partnership to see what this technology can do," said Greg Beltzer, head of technology for RBC Wealth Management-U.S.

The first phase of testing for the pilot between RBC Wealth Management and the AI-powered wealth management platform TIFIN AG began in August of last year, when 60 advisors were selected to see how the model performed and help design the programs.

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AI use in customer service faces legal challenges that could hit banks

Two recent lawsuits challenge companies that use artificial intelligence in their chatbots and call centers, as many U.S. banks do.

In one case, a customer whose grandmother had just died booked a plane trip on Air Canada and was assured by the airline's generative AI-based chatbot that he had 90 days to apply for a bereavement discount. This turned out to not be the airline's policy and it refused to give the steep bereavement discount, saying its policy was stated correctly on its website. A civil resolution tribunal ordered Air Canada to award the customer the discount and pay fees.

In the other case, several customers sued Patagonia after finding out that Talkdesk, a contact center technology vendor Patagonia uses, was recording and analyzing customer-support calls and using them to train its AI model. The customers say they would not have made these calls if they knew Talkdesk was eavesdropping on them, and that this was a violation of California's data privacy law. The complaint was filed on July 11 and has not yet gone to trial.

The first case casts doubt on the use of AI in contact centers, something many U.S. banks do, mainly to analyze customer sentiment and call center rep performance and to summarize calls for their records. The second case calls into question any use of a customer-facing generative AI model by a retail company like a bank.

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