Artificial intelligence

As artificial intelligence reshapes banking, small lenders know they can’t ignore the technology, said BNY’s head of community banking, Shofiur Razzaque. 

“There's skepticism around the controls: ‘How do I keep us safe?’ There’s no skepticism around, ‘We need to do this; otherwise, we’re in trouble,’” Razzaque said.

The New York City-based bank launched an initiative in September that offers free training, led by BNY senior leaders, for 1,000 community bank executives on topics such as AI, cybersecurity, data analytics, corporate finance and strategic planning.

Dozens of community bank leaders attended program events in December, Razzaque said. As the initiative is tailored to meet community bank needs, an upcoming session for human resources practitioners will be led by a BNY HR leader. 

BNY has $2.2 trillion in assets under management, and the custody bank’s clients include other financial institutions. Razzaque’s community banking and solutions group offers local lenders services such as payment solutions within BNY’s global payments and trade business. 

In engaging with community bankers, it became apparent there was a desire for more information on AI, cybersecurity and digital assets, Razzaque said in a recent interview.

As a large bank, BNY sees the move to assist banks with less than $10 billion in assets “as a duty,” he said.

“These players matter,” he said. “If we help them, the entire financial system gets stronger. Is there an aspect of it where there's commercial opportunity? That's secondary.”

Editor’s note: This interview has been edited for clarity and brevity. 

BANKING DIVE: Tell me about your role and this unit at the bank.

SHOFIUR RAZZAQUE: When we set this team up, we thought about two ways we could help community banks. One is through access to our products, services and capabilities. My job on that aspect is, how do we open the door for BNY? How do we bring them in? So I go out, I meet community banks, I'm at conferences, getting to know these organizations and explaining how we can help them on their journey. 

The second aspect: On some of these emerging capabilities, whether it's AI, digital assets, treasury service trends, we have deep experts who do this for a living. Smaller organizations face challenges such as running on a really lean staff, so they're always looking for some form of education, guidance, insight. So we introduced a free financial education program, allowing community bank leaders, middle managers to take part in sessions on topics such as AI.

Are bank executives turning to their peers more than consultants for this kind of information?

Consultants add a different level of value. But when you are a banker, when you're talking to your own colleagues or folks that have the same background, you're speaking the same language. And when there's no cost associated with that, and you could really ask about use cases and how to increase adoption, I think it's different. It goes from hypothetical, strategic to really practical discussion.

What’s your take on the AI opportunity within community banks?

These banks know every customer that walks through the door. The relationship banking is so pure. But when you go to the back office, a lot of them are doing things the way they've done for many years. That's where AI could really unlock potential, if you can free up employees to interact with customers while you apply AI to some of these mundane tasks. Adoption is scary, but they have to embrace AI; otherwise, if you don't start using and applying it in some shape or format, you fall behind.

Using AI, at first blush, would seem to inhibit the relationship banking community lenders are known for – not so, in your mind? 

The area where AI unlocks is growth. When you're in a smaller organization and what you're focused on is increasing your loan portfolio and increasing your client base, I don't imagine AI doing that. I imagine banker Mr. Smith, who knows the local clients, that's who's going to do that. If you give Mr. Smith more time to focus on that, because you remove some of the tasks from his day to day, that’s the growth. 

What are the hurdles to adoption for these banks?

When you run a bank, you're regulated, you're risk-oriented. You're always worried about controls. But also, not knowing where to start, and almost overthinking it and trying to use it in too many different places. And, not having the right partners who can enable it for you. You don't necessarily need to build your own AI to use it. A lot of these institutions could leverage it through their Microsoft engagement. They just need a blueprint and answers to some of these scary questions, such as, what are the guardrails? What are the controls? 

One in two banks is already investing in AI in some way. Adoption into some core operations still slightly lags, but I think the adoption rate will be much higher in the next few months.

How can community banks begin weaving AI into their operations? 

At BNY, we jumped onto this AI journey several years ago. Today, all employees have access to our AI tool, which is called Eliza, and 50% of our non-engineering employees are using it to build their own agents. We got there with simple use cases. Informing the team: AI is available. You can use it to help write emails. You can use it to review your notes. It’s these small adoptions in community banks. Encourage the team to use it, create an environment where it’s talked about often, and then build to some of the more complex operational uses, which come fast.

Are there areas of the bank in which community lenders should avoid using AI – perhaps customer service?

No, I don't think there's any spots they shouldn't, but I think there's some areas where it might be easier to integrate on Day One. I don’t think interrupting client engagement is the right place to start. But if you can improve some back office operations, create efficiency, it might be the ideal area to start.

Artificial intelligence and robotics trends observed at January’s Consumer Electronics Show are set to further raise the bar for customer service experiences, said Don Relyea, U.S. Bank’s chief innovation officer.

That includes helping customers handle tasks in seconds rather than minutes, he said. 

“That expectation of the bank just taking care of stuff for you with zero friction, it's only going to increase,” said Relyea, who attended the show in Las Vegas with his team. 

After the U.S. Bank team observed agentic AI innovation at 2025’s CES, “do it for me” has “become a key pillar of our digital strategy,” Relyea said during a recent interview. He declined to comment on innovations the lender is planning to launch.

“The new bar for digital excellence is, what can we do for our customers, as opposed to having them do it themselves,” he said. “Everybody’s working on how we’re going to execute on that.” 

The Minneapolis-based super-regional bank is also working with partners on the agentic commerce front, after scouting developments at CES in recent years, Relyea said. He’s attended the show each of the six years he’s been at the bank.

This year, Relyea brought along a digital risk vice president, which was “useful and fun,” and an old friend who’s a bank customer, which “made me think about things through a slightly different lens,” and afforded in-the-moment feedback on whether an innovation would appeal to a bank customer.

Editor’s note: This interview has been edited for clarity and brevity. 

BANKING DIVE: What struck you at this year’s show?

DON RELYEA: AI was still the big theme this year. The thing that was most interesting about it: it was everywhere and in everything, but it wasn't always up front. We saw it in so many different form factors, everything from rings to credit cards, to sneakers, golf clubs, robots, robotic components.

There are little wearables that could be transcribing a call. While that consumer utility is going to be everywhere, it raises a lot of privacy concerns for individuals, as well as companies. When I think about our security folks, how are they going to be able to keep these rings and things out of secure areas? That's going to be a thing we're going to have to figure out.

Last year, we saw janky user interfaces with some of the AI stuff. This year, it’s definitely gotten to a better level of polish and interaction.

The other giant trend was robots and automation. This is the year of “rise of the robots.” And not all of them are ready for prime time. Some are only applicable for large-scale commercial applications. 

The health, wellness and longevity tech is interesting. I can get all this information about my health. If you have money, you can basically invest in more to extend your life, and that's coming. What if you could look at your financial health and longevity and marry it up with your health timeline? So new tech comes out, OK, I'm going to live for another 20 years. What's the financial conversation that needs to happen there?

A friend who’s a customer was interested in being able to have a data-driven conversation with his portfolio in a graphical format, similar to what you can get with all of your health vitals. There's an opportunity for something interesting there, beginning to think holistically about health and financials and have a combined, data-driven conversation. Not all the data is there yet for this stuff, but I think it's coming.

Last year, you talked about the importance of not crossing the “creepy line” with AI-powered “do-it-for-me” tasks. Where does that stand a year later?

The market's definitely moving toward, “if an AI can automate something for me, I want that,” in the consumer space. It doesn't feel as creepy anymore to people, and I think it's just going to get less creepy. 

My concern is, can we move fast enough to meet those expectations, which I think we can, but we've got to start early. We can test with actual customers, make sure that our hypotheses are accurate.

What are you most excited to talk through with your team? 

The bank’s AI Center of Excellence came out of our group a few years back. We just launched our Digital Assets and Money Movement organization out of innovation this past fall. The team is now thinking about, what is the next big business unit or product features and sets that we can get another enterprise win like those we’ve had over the past five years? And while we didn’t find it, we found a lot of really good fishing holes, with tons of ideas. This one, we came back with a lot of work to do, to get these products and ideas to the next level. 

I found companies that are going to save the innovation team money. And we found some great partners that are going to help us explain the value of some of the things that we’re doing better.

When we look across the team, we’ll probably have a dozen or so potential partners and opportunities we’ll vet. We found a bunch of interesting companies in the fleet technology space, where embedded AI sensors and the Internet of Things concept allows you to track the health of that physical asset, like a truck or rental car. The bank finances and insures these things, so there’s an assurance component to it: Are the assets still worth what we think they’re worth? Or did somebody drive it off a cliff and then refurbish it? So we’re bringing all that tech back to our fleet partners, as well.

Sponsored content

By Starburst Data

For executives, and especially CDOs, CIOs and CTOs, 2026 may be less about launching new AI pilots and more about confronting a harder question:

Can our data architecture actually support AI at scale?

Across the industry, banks are accelerating investment in generative AI, agentic systems and advanced analytics. Boards want productivity gains. Business leaders are looking for faster fraud detection and sharper personalization. Regulators want explainability.

But AI does not operate in isolation. It sits on top of decades of accumulated infrastructure decisions, legacy cores, fragmented warehouses, siloed cloud environments and regulatory reporting systems built for specific mandates.

AI isn’t just another workload you slot into the stack. It exposes everything that’s brittle underneath.

Recent industry research shows that more than 90% of banking data users say the data they need is unavailable or too slow to retrieve, and 81% cite data quality as a top challenge. That gap between AI ambition and data accessibility is widening.

For technology leaders, this is no longer a pilot problem. It is an architectural one.

Fragmentation Is The Core Constraint

Most financial institutions operate across hybrid estates, legacy on-premises data warehouses, private clouds, multiple public cloud providers and SaaS platforms. Over time, regulatory mandates such as CCAR, liquidity risk management and AML modernization have improved data discipline within specific domains. But those improvements often remain siloed.

AI systems, particularly retrieval-augmented and agentic models, require contextual visibility across customer, transaction, product and risk data simultaneously. When data must be repeatedly copied into centralized environments to make it usable, latency rises, governance complexity increases and cross-border exposure expands.

The architectural shift underway is subtle but significant:

From asking, “Where should we move the data?”

To asking, “How do we securely access and govern distributed data where it already lives?”

Institutions that fail to resolve this tension risk slowing AI deployment or introducing unintended compliance and security gaps.

AI Is Forcing a Redesign of the Data Layer

Banks have spent years modernizing platforms and migrating workloads. But systems designed for reporting and analytics are not automatically designed for intelligent automation.

To make AI work beyond pilots, the data layer has to change in a few fundamental ways.

Reliability:

AI amplifies whatever it is trained on. If underlying data lacks consistency, lineage or auditability those weaknesses scale with the model.

Technology leaders must be able to trace where data originated, how it was transformed and how it influenced downstream decisions. In a regulated environment, reproducibility and explainability are not optional.

Without that foundation, AI becomes difficult to defend, both internally and externally.

Frictionless, Policy-Aware Access

AI depends on broad access to distributed data. But access cannot mean uncontrolled replication.

If every new use case requires building fresh pipelines or copying sensitive data into new environments, innovation slows and risk increases. What’s needed instead is unified, policy-aware access across systems, whether data resides on-premises, in private cloud or across hyperscalers.

Speed doesn’t mean everything must be real-time. It means reducing friction so teams aren’t rebuilding plumbing for every initiative

Context and Consistency

AI doesn’t just need more data. It needs coherent data.

Customer definitions must align across lines of business. Transaction data must connect to behavioral signals, documentation and external feeds. Shared semantics become essential for reducing bias and improving model precision.

More data without alignment creates noise. But too narrow a view limits what models can learn.

Governance Built In, Not Bolted On

In banking, governance cannot sit outside the AI stack.

Privacy controls, cross-border restrictions, entitlements and usage policies must be enforced at the point of data access, not retrofitted after deployment. As AI systems become more autonomous, governance has to move from periodic oversight to embedded enforcement.

Security and compliance aren’t guardrails around AI. They’re part of the architecture.

None of This Works in Isolation

These elements reinforce each other.

Speed without reliability accelerates bad decisions. Context without governance magnifies risk. Governance without usability slows innovation to a crawl.

The challenge for CDOs, CIOs and CTOs isn’t optimizing one dimension. It’s strengthening all of them at once, across distributed environments that were never originally designed for AI-scale workloads.

Many AI initiatives stall not because the models fail, but because the data architecture beneath them cannot support sustained, governed scale.

Artificial intelligence may define the next chapter of banking innovation. But for technology leaders, the competitive advantage will be determined deeper in the stack; in the structure, interoperability and governability of enterprise data.

Artificial intelligence’s effect on the labor market “may go too fast for society,” potentially necessitating government and business collaboration to retrain workers and provide income assistance, JPMorgan Chase CEO Jamie Dimon said at the World Economic Forum in Davos, Switzerland, in January.   

Dimon said he expects AI will result in the biggest U.S. bank employing fewer people in five years, even as the lender grows around the world. The bank had about 318,512 employees as of the end of the fourth quarter. 

AI will eliminate and change jobs, and likely add some, too, Dimon expects. As the technology evolves rapidly, companies are clamoring to embed it into their operations, potentially upending the labor market. That means contingency plans could become essential, he indicated. 

“If it goes too fast for society, that’s where government and business in a collaborative way should step in together and come up with a way to retrain people,” Dimon said. If, for example, some 2 million commercial truckers in the U.S. suddenly lost their jobs due to automation, it could spark “civil unrest,” the CEO said.

Such a move would be better phased in, he argued. “You can’t lay off 2 million truckers tomorrow,” he said. 

“You want the government to tell you you can’t lay off a whole bunch of people at JPMorgan?” interviewer Zanny Minton Beddoes, editor-in-chief of The Economist, replied. 

“And we would agree,” Dimon said, “if we have to do that to save society.”

With AI poised to usher in massive change, “do you have plans in place to make it work better if in fact it does something terrible?” Dimon said.

The CEO – who tends to pontificate on geopolitical and financial risks and domestic policy priorities in his annual shareholder letter – envisions local governments offering companies incentives to retrain workers, slow the pace of change AI has on their workforces and provide people with income assistance.

Dimon has said AI could be as transformational as the printing press or electricity, and he expects the technology to affect every job. 

AI could be both an efficiency improver for the New York City-based lender and reinvent what JPMorgan is at a fundamental level, he said. The technology is “used extensively” at the bank, in areas such as risk, fraud, marketing, customer service, idea generation, hedging and credit.

“If you take it to the next step – agents – that could change your business,” he said. 

“It will change the way customers face us. We have to be very adept at saying, what do you want? You’re the customer, how do you want to access it?” Dimon said. “It may lead to us winning and losing in big areas.” 

Two decades ago, JPMorgan’s biggest competitors were Bank of America, Wells Fargo, Goldman Sachs and Morgan Stanley. Today, that list includes Stripe, PayPal, Dave, Chime, SoFi, Revolut and other fintechs, Dimon said. 

“They’re coming at you, some to pick a sliver of the business, some to take your whole business,” the CEO said. “If you put your head in the sand, you will lose. I think that was true 30 years ago, but I think it’s probably more true today.” 

“The brain power and money that’s going into this thing is extraordinary,” he said. “If we don't do our job faster, quicker – yeah, we’ll lose, too.” 

JPMorgan Chase’s asset management arm will no longer employ external advisory firms for proxy voting advice at U.S. companies and will instead use an in-house artificial intelligence-powered tool to help make voting decisions, Industry Dive confirmed Wednesday.

J.P. Morgan Asset & Wealth Management announced the change in a January client memo first reported by The Wall Street Journal. The tool, “Proxy IQ,” will aggregate and analyze proprietary data from the over 3,000 annual meetings, according to a portion of the memo seen by Industry Dive.

In switching to Proxy IQ, JPMorgan said it is “the first major investment firm to fully eliminate any reliance on external proxy advisors for [its] U.S. voting process,” the memo said. (Wells Fargo made a similar move weeks later.)

JPMorgan will complete its transition from using external advisors to Proxy IQ in the first quarter of 2026, a company spokesperson said. The change comes after the bank’s CEO, Jamie Dimon, called out “undue influence”  such firms exert. Meanwhile, the Trump administration and Republican state officials have targeted proxy advisers — namely, Institutional Shareholder Services and Glass Lewis — for their diversity, equity and inclusion and environmental, social and governance policies. 

Proxy IQ will be available on the asset manager’s Spectrum investment platform, which had over $3 trillion in assets under management across more than 11,000 portfolios at the end of 2024. 

“By harnessing advanced AI, we no longer need third-party data collection or voting recommendations in the U.S.,” the memo said. “This reinforces our unwavering commitment to vote solely in clients’ best interests, using our information advantage.”

In Dimon’s 2024 letter to shareholders, the CEO announced the bank’s asset management arm would increase the participation of portfolio managers and decrease the influence of third-party advisers in the voting process. 

J.P. Morgan Asset Management planned to excise third-party voting recommendations from its internal voting systems by the end of 2024 and work with third-party advisers to have any research reports exclude voting recommendation.

ISS and Glass Lewis collectively make up over 90% of the proxy adviser market and accounted for 48% and 42%, respectively, of that market’s assets under advice in 2021, according to the Harvard Law School Forum on Corporate Governance. By 2024, the firms collectively accounted for around 97% of the market, Harvard Law’s corporate governance forum later found.

President Donald Trump signed a December executive order that accused ISS and Glass Lewis of using their combined market share “to advance and prioritize radical politically-motivated agendas.” The order directed the Securities and Exchange Commission, Department of Labor and Federal Trade Commission to review all SEC publications that implicate DEI or ESG, consider reviewing or rescinding the rule that currently governs the proxy voting process, and review ongoing state-level antitrust investigations into proxy advisers.

A Texas anti-ESG law targeting proxy advisory firms, including ISS and Glass Lewis, was temporarily halted in 2025, after a federal judge ruled the law to be a form of viewpoint discrimination. Texas Attorney General Ken Paxton initiated a probe into ISS and Glass Lewis following the injunction. The two firms have also been probed by the attorneys general of Florida and Mississippi over their DEI and ESG policies.

The foundation of digital transformation in the artificial intelligence era begins with clean data. At least, that will be part of the focus for Sunwest Bank Chief Technology and Strategy Officer Ben Xiang in 2026 as he unfolds his AI strategy. 

“This is probably the most exciting time that I can think of to get into an industry that is really primed for innovation with AI,” Xiang told Industry Dive. 

Big banks including Bank of America, JPMorgan Chase, Goldman Sachs and Citi are funneling significant investments into AI initiatives aimed at transforming workflows and improving productivity and efficiency. Xiang said he’s chasing similar goals for Sunwest Bank in 2026, with a sharp focus on improving the data layer. 

“Ultimately, once you have the data in place, then you can start to automate things and you can leverage artificial intelligence in order to realize business value from that,” he said. 

Sunwest Bank, a privately held commercial bank headquartered in Sandy, Utah, tapped Xiang to lead its multiyear modernization initiative and technology projects in September. Xiang has also served as a member of the bank’s board of directors since 2015 and held the role of interim CIO in 2019, according to a press release. 

Amid the modernization efforts, one of the bank’s projects is the rollout of Microsoft Copilot powered by OpenAI’s GPT-5 model, Xiang said. The AI tool will provide a central source of information for employees, combining publicly available and shareable information within the bank. 

The next component of Sunwest's AI strategy hinges on connecting disparate data sources within the bank into a data lake. Cleaning up the bank’s data and centralizing it will enable future advanced agentic workflows, Xiang said. 

“By doing this, we’re able to provide some pretty comprehensive and advanced data analytics that will enable a lot of automation that we’re looking to implement throughout the bank,” he said. 

In search of productivity gains

Ultimately, Xiang’s goal is to assess existing workflows that could benefit from automation and intelligence tools — while not misallocating investments into innovative projects that “won’t yield business results.” 

Xiang said he’s paid attention to external research indicating how some generative AI projects aren’t translating into return on investment, which is why he plans to pursue “low-hanging fruit” and projects with the highest chance of success within the organization. 

“We’re really looking to uplift the overall productivity and efficiency at the bank,” Xiang said. “There are a lot of metrics we’re going to be looking at internally over the course of the next 12 months to see if the AI projects we’ve embarked on really succeeded.” 

The banking industry is set to take greater advantage of agentic AI because of advancements in large language models, as well as the maturation of enterprise agent development tools, according to an Accenture report published in January. 

Indeed, connecting LLMs to other applications and layering agentic AI on top of that foundation is where “people are really starting to see the value,” Xiang said. 

“Being able to have an AI agent that can do multiple things, that can introduce intelligence to a workflow, that has really opened up people’s eyes and opened up the doors for what you can do within an organization in order to dramatically improve efficiencies in a business,” Xiang said. 

Part of the bank’s strategy for success comes down to ensuring staff members have the tools they need and are educated on AI “to really take advantage of this incredible technology,” Xiang said. 

Technology leaders will need to make AI agents easy to use for long-term success, particularly as employees will likely be managing multiple agents that are working for them in the future, said Michael Abbott, senior managing director and global banking lead at Accenture. 

“It has to be as easy as Excel or PowerPoint to use,” Abbott told Industry Dive. “It can’t be a mystery novel.” 

Combating payments fraud isn’t likely to get easier in 2026, and some professionals acknowledge it could get tougher as artificial intelligence spawns new threats.

U.S. consumers and businesses remain juicy targets for fraudsters rolling victims by way of duplicitous impostor scams, check washing operations and synthetic identity schemes, among other ploys. The increasing acceptance of digital assets in the marketplace, as the Genius Act brings stablecoins into payments, may provide yet another avenue for taking advantage.

Criminal use of artificial intelligence, with its deepfake potential to sharpen fraudsters’ tricks, is expected to spool up in 2026, even as the payments industry embraces AI for agentic commerce. As companies jockey for an AI edge, they’ll teeter between positive prospects and perilous possibilities in a period of transition.

“Where there is confusion, there is opportunity,” industry consultant Peter Tapling said regarding AI’s arrival. “It’s moving fast, and it’s going to take time,” he said during a January interview. He said the AI age has set in quickly for the industry, but is still developing.

Classic fraud types, such as check tampering, remain problematic despite President Donald Trump’s 2025 executive order forcing the federal government to stop sending paper checks. About two-thirds of businesses encountered check fraud in 2024, and it was the method most frequently cited for fraud that year, according to the latest survey on the subject from the Association for Financial Professionals. 

Nonetheless, the industry will likely be focused on new, emerging threats in 2026. The payments industry has become arguably obsessed with AI-powered agentic commerce, and the possibilities it poses for creating agents that will not only shop, but also buy goods and services on behalf of consumers and businesses. Card networks, processors and fintechs are racing to harness the increased payments volume potential.

At the same time, AI presents dangers for the industry. Criminals have already made big strides industrializing their fraud schemes and scaling those attacks. Now, AI may allow for directing even more believable come-ons at victims, seeking to dupe them into sending money under false pretenses. 

How AI is benefiting fraudsters

Bad actors are increasing their personalization in payment cons and better mimicking of trusted contacts to be more convincing, said Colin Parsons, head of fraud product strategy at the financial software firm Nasdaq Verafin. That’s true even for savvy individuals, he said.

This growing sophistication shows up in fraudsters’ use of synthetic or stolen identities to open accounts; creating fake bank websites to elicit customer information; and in impersonating grandchildren to dupe the elderly, he said.

“Those situations just continue to become more sophisticated, and harder for an individual to understand that they’re not actually interacting with the person that they think they are through the chat, or through a phone call, through email,” Parsons said in a January interview.

The impact of AI on payments is complicated by tandem trends happening in payments, said Forrester analyst Lily Varon, noting agentic commerce and the evolution of passwords. These trends sometimes challenge the existing rules for payment authentication, authorization and fraud liability, among other core principles, in the payments processing system, Varon said.

“We've got a few different streams of things happening at once,” Varon said in a December interview. “AI is improving the quality of deepfakes; [and] the growing adoption of AI tools is facilitating the creation of them,” she said. “And even though the main vector for fraud, with deepfakes, will be social engineering, it can and will impact payment authentication as well.”

Password practices evolve

That’s happening as passwords and passcodes are increasingly seen as ineffective, Varon noted. So, as the industry tries to move toward a future without passwords – historically seen as the best way to authenticate users – it’s grappling with how best to verify users.

That issue is exacerbated by the notion of agentic commerce because it entails shopping and payments by formerly suspect bots that now are empowered to complete transactions on consumers’ behalf. 

“There's a lot of excitement about agentic AI and how it might apply it to payments, but there's also a lot of concern about how it might be taken advantage of, just as deepfakes have taken advantage of generative AI's ability to process voice and image and text,” Tapling said.

Biometrics may bolster defenses

Varon predicted biometric authentication will help bridge the gap, as more service providers and consumers embrace Fast Identity Online, or FIDO, passkeys. Those involve cryptographic keys tied to particular devices that are accessed by fingerprints, face scans or other such physical attributes. 

AI is also bolstering anti-fraud defenses by spotting fraud. “Artificial intelligence is really allowing technology providers and institutions to leverage this new technology and use it in a way that's very positive for identifying fraud,” Parsons said. 

Tapling noted the same, saying AI is used against fraud both on the front lines, interacting with consumers, and also in the back office. He pointed to the New York company Reality Defender using AI to detect deepfakes. “We're now using AI to identify AI,” he said. “They've built an AI model that senses, ‘is the video that I'm looking at a deepfake?’”

Companies are also employing AI, with help from financial crime software companies like Unit 21, to improve tracking of fraudulent transactions, Tapling said. “There's all kinds of ways that we can use AI, both at the time of a transaction event, to try and give me more signals or enrich the signals that I have, but also at the back end,” he said.

Fighting push-payments scams

One of the most pernicious frauds to gain traction in recent years is push-payment fraud, those instances where criminals hoodwink consumers and businesses into voluntarily sending money to crooked accounts. It’s been particularly difficult to fight because of the voluntary nature of the money transfers.

It has attracted the attention of lawmakers as stories of victims facing major losses in such scams have proliferated. 

Organizations have sought to educate the public about such snares and companies have acted to upgrade systems and tools to confront such swindling. Concerns have climbed as speedier payments systems have become available.

Since the Federal Reserve launched its instant payments system FedNow in 2023, users have called for such increased capabilities. The Fed said this month that it’s working on anti-fraud upgrades to FedNow.

The central bank has added a feature that gives FedNow users “the ability to verify a beneficiary name associated with the account of an intended payment,” according to a Jan. 15 press release. 

The central bank also has a FedNow pilot program underway that will let banks and credit unions that use the system “pre-check” a receiver account before sending a payment over the real-time system, according to the release.

Sharing information to fight fraud

Apprehension among financial institutions about the exchange of information – whether due to privacy laws, competitive instincts or other considerations – has long hampered communication and cooperation that many believe could better thwart payments fraud.

Fed Vice Chair for Supervision Michelle Bowman pointed to the barriers standing in the way of a stronger collective financial institution front in the battle against fraud.

“When banks share the latest information about fraud prevention among themselves – if some of the data is currently classified as [confidential supervisory information] – the disclosure can be prohibited, even if sharing it could make all banks more resilient to emerging fraud risks,” she said Jan. 7 in California.

She suggested in that speech, which was focused on modernizing regulations, that the federal government needs to change its approach to better address fraud. The Fed is reviewing how to better define instances where such information might be shared.

Verafin also attempts to help financial institutions share intelligence to fight fraud.

“The primary way that we see for financial institutions to prevent this type of fraud [via individuals] is by leveraging the intelligence across networks, understanding what's happening, not only within their institution, but outside of their institution, and working together to prevent financial crime,” Parsons said.

AI model advances and maturation of enterprise tools for agent design and deployment will let the banking industry take greater advantage of agentic AI in 2026, according to Accenture’s Top Banking Trends for 2026 report published in January. 

Banks will turn to platforms from major cloud providers to build artificial intelligence agents that align with their brands, compliance and service standards, the report said. AI agents are already improving performance for the financial services industry in software engineering, risk management and customer service. 

“Executives must empower employees to reimagine workflows and co-design intuitive human-AI interactions that elevate work rather than replace it,” Andrew Young, Accenture’s global talent and organization lead for financial services, wrote in the report. 

Banks are already reaping the rewards of agentic AI and will be working to add to the tools’ scope and capabilities throughout 2026. 

“Leading banks are deploying AI agents across operations, where they work alongside employees and independently handle defined tasks,” Young said. 

BNY has invested heavily in AI, with plans to build 150 AI-powered offerings to address issues throughout the bank’s operations. It has leveraged its AI platform, Eliza, to allow employees to design AI agents that can accomplish different tasks and integrated AI-powered “digital employees” that can execute on tasks with oversight. 

Over the next three years, 57% of banking executives expect AI agents to be fully embedded in risk, compliance and audit functions, as well as fraud detection and transaction monitoring, according to the Accenture report. Another 56% believe AI agents will reach broad adoption in credit assessment and loan processing, as well as “know your customer” functionalities. 

AI adoption is anticipated to drive up to 20% in net cost reductions for banks, according to a 2025 report from McKinsey & Co. However, the extent of banks’ cost reductions partially revolves around the industry’s ability to become fully agentic, the firm said. 

It will be critical for technology leaders to support workers along the AI journey, as many employees over time might manage teams of specialized AI agents to improve productivity, according to Accenture. Indeed, nearly half of banks and insurers are creating roles to supervise AI agents, according to the World Cloud Report for Financial Services 2026, published by the Capgemini Research Institute. 

Most CIOs expect AI agents will operate under a central governance model. CIOs will need to enable real-time monitoring and telemetry tracking of AI agent activity and system interactions, as well as implement safeguards such as multiagent validation for sensitive tasks, according to Accenture. 

“As AI agents become embedded across banking operations, they must be integrated into the enterprise technology stack with defined identity and access controls,” the Accenture report said. The firm recommends executives establish an agent identity framework enabling authentication, authorization and permission across operations.

U.S. banks outpace their global counterparts in artificial intelligence adoption maturity, according to Finastra data published in February. Financial services organizations in the U.S. have 65% of their AI use cases in active deployment, compared with 61% of global businesses.

AI adoption efforts are expected to continue as 42% of U.S. financial companies plan to accelerate AI investments by more than 50% this year, the software company’s report found. Findings were based on a survey of 1,509 financial leaders from 11 regions, including the U.S., U.K., Japan, Germany and France. 

Half of U.S. executives surveyed said regulatory and compliance hurdles are a barrier to scaling modernization efforts, compared to 40% of global executives, according to Finastra. 

While U.S. financial executives view the pace of technological change with optimism, they’re also facing regulatory challenges and skills gaps as they modernize tech stacks and adopt AI.  

Part of the regulatory dilemma stems from rising data sovereignty concerns, the Finastra report found. U.S. firms are more likely to prioritize implementing data handling controls, “even if it slows the pace of modernization,” the report said. 

Another 50% of U.S. executives reported talent and skills gaps as a barrier to technology modernization and AI adoption compared with 43% of global executives. 

Demand for digital skills is outpacing available talent, especially in the U.S., Singapore, Japan and the United Arab Emirates, according to Finastra. 

“To fully realize this moment, the industry must work together to bridge the talent gap and navigate an increasingly complex regulatory landscape,” Finastra CEO Chris Walters said in a press release. 

The Fintech Open Source Foundation in 2025 launched a coordinated effort with banks including Citi, Morgan Stanley and Bank of America supporting the Common Controls for AI Services initiative. The effort aims to define vendor-neutral controls for safe AI adoption in the financial services industry. 

It’s also attempting to provide financial services firms with a clearer path for adopting AI amid changes in the regulatory environment at the federal level. 

“Bank of America is proud to support FINOS’ mission to establish an industrywide AI controls framework,” Duncan McInnes, CIO for global markets at Bank of America, said in a press release at the time. “Their collaborative method toward establishing a standardized AI governance approach will help strengthen the financial services industry’s ability to deliver secure and innovative AI solutions.”

U.S. financial services firms focus their live AI use cases in data analysis and reporting, document intelligence extraction and credit underwriting and decisioning more commonly than global financial services firms, according to Finastra. 

Additionally, 42% of companies in the U.S. banking industry indicated that their customers expect a mix of human and digital interactions, compared with 30% globally.

Upstart has named co-founder and technology chief Paul Gu its next CEO, effective May 1, the artificial intelligence lending fintech said in a February release.

Gu will succeed fellow co-founder Dave Girouard, who will remain executive chair and transition to a special adviser role.

Gu, Girouard and former Google executive Anna Counselman founded Upstart in 2012 as a lending platform that established creditworthiness using nontraditional variables such as education and job history.

The company returned to profitability in 2025, after reporting revenue of roughly $1 billion for the year – a 64% increase from 2024, according to earnings also disclosed in February.

“When we co-founded the company, Paul was barely old enough to drink – and I was older than his parents,” Girouard said of Gu’s elevation. “This generational transition is a rare opportunity to have a founder-led company for decades to come.”

The company announced additional leadership changes. CFO Sanjay Datta was named president and chief capital officer, effective immediately. Andrea Blankmeyer, CFO of healthtech firm Cityblock Health, will join Upstart in March as CFO. Grant Schneider, Upstart’s ex-vice president of machine learning, rejoined the company in Gu’s former role as technology chief.

"I can't imagine a succession plan more thoroughly considered or executed with any finer level of detail than what we've done at Upstart in the last few years to prepare Paul and the company for this day," Girouard told analysts during an earnings call.

Gu co-founded Upstart less than a year after dropping out of Yale University to join the inaugural class of Thiel Fellows, to whom billionaire investor Peter Thiel awards $100,000 if they agree to dive into entrepreneurship instead, Business Insider reported.

“Credit is one of the cornerstone industries of civilization. Changing it is hard, but can be unimaginably impactful,” Gu said. “Dave and I spent the past 14 years building the foundations of a company that can do that, and I'm excited to now have the opportunity to show the world what’s possible in the years to come.”

Upstart reported $18.6 million in profit for 2025’s fourth quarter – and the fintech’s revenue jumped 35% year over year.

However, Upstart’s share price fell by about 19.5% in the days after earnings and the upcoming leadership transition were announced, according to Yahoo Finance.

A Citizens analyst characterized market jitters as stemming from comments suggesting Upstart will rely more heavily on secured loans, such as auto and home equity lines of credit, which offer tighter profit margins.

“We have grown rapidly in secured products and prime borrowers, segments which come with lower take rates but dramatically larger market sizes,” Gu said on the earnings call.

Upstart was an early entrant into the AI-powered credit space. It became the first company to obtain a no-action letter from the Consumer Financial Protection Bureau. 

The fintech used the regulator’s sandbox to experiment on its alternative data model and underwriting without the threat of penalties. Upstart showed in 2019 that its alternative credit data drove loan approval up 27% over two years, with an average 16% drop in the annual percentage rate on those loans.

Upstart went public in 2020, but its no-action deal with the CFPB ended in 2022, when the agency established its Office of Competition and Innovation, which de-emphasized individual innovation agreements in favor of “incubation events” such as sprints, hackathons, tabletop exercises and war games.

Upstart received a subpoena from the Securities and Exchange Commission in late 2023 over disclosures the fintech made in relation to its use of artificial intelligence models in lending, the company said.

As large companies strive to employ artificial intelligence to bolster efficiency and growth, Citi’s Tim Ryan believes getting employees to buy into an AI strategy is even more important than the right technology choices.

“There's no way we can sit in New York and come up with the strategy for a global company and implement it if we're not bringing our people on the strategy,” Ryan, head of technology and business enablement at the bank, said at the Money20/20 conference in Las Vegas in October.

AI is “a standing topic” with Citi’s board, noted Ryan, who joined the bank in 2024. He had been a senior partner at PricewaterhouseCoopers when he was tapped to lead Citi’s efforts to transform its operations and adopt emerging technologies with efficiency in mind. 

About 180,000 of the New York City-based bank’s 230,000 employees are using its AI tools, Ryan said. Staffers wrote 7 million prompts in the first eight months of this year, and have done about 1 million automated code reviews that have freed up 100,000 hours each week, he said.

“Companies that are our scale, they will win because of how they bring their people on this journey,” he said. “That's what will determine the winners and the losers. It won't be somebody standing on this stage saying, ‘I should have chosen this tech stack versus that tech stack.’ It won’t happen. What’ll happen is, it'll be, ‘I could have done a better job getting our people to buy into the strategy, equipping them to implement the strategy we're designing.’” 

That approach creates what Ryan called “an innovation dividend,” where employees then spot new ways to serve customers, drive revenue growth, manage risk and operate more efficiently.

“It's the single biggest reason we have momentum,” Ryan said. 

In his experience working with CEOs while at PwC, Ryan said change management can be underestimated in optimizing a certain strategy.

“Don't just sell tech,” Ryan told conference attendees. “You have amazing technology. When you're talking to people like me or my peers, think about, how does it get implemented at the scale of the organization? 

“Sell tech and people, and that will win,” Ryan said.

AI ‘accelerators’

Citi’s 4,000 “AI accelerators” rank as a key element of the bank’s change management approach, Ryan said. Those are staffers across all five of the bank’s businesses and various functions who’ve “leaned in” to the bank’s strategy around AI, he said.  

“These are the people that we meet with on a regular basis, to understand what's working in the tools, what's not working in the tools, what systems do we need to integrate with, what the next release of our tools needs to bring,” Ryan said. 

In conjunction with that, it’s crucial for Ryan to “have a leadership team around me that really embraces the concept of listening,” he said, “which is easy to say and hard to do.”

Ryan also noted the importance of the bank’s cloud-first infrastructure in enabling innovation, especially when it comes to agentic AI. 

The bank employs a hybrid cloud strategy, investing both internally and working with partners such as Google, Anthropic and Cognition Labs. 

“Our cloud strategy allows us to bring in the right parties and cohesively get that scale,” Ryan said.

One of the biggest challenges is to speed it up safely, he said. 

“If it takes 12 days to get something tested, I think it’s too late,” Ryan said. 

The lender has made adjustments over the past two years to bump up its testing time frame, from days to hours, “and that agility is incredibly important.” The bank is also able to deploy changes faster, he said.

In risk, the bank is using AI agents and working to scale that effort, “but not taking that human out of the loop,” he said. That offers the opportunity “to give our stakeholders more confidence around risk and controls.” 

Citi is also eyeing opportunities to use AI agents in its wealth and retail banking businesses, he said.

The bank recently rolled out agentic AI capabilities to 5,000 employees using its proprietary AI platform. Workers issue a prompt, and an agent can take multistep directions and complete tasks quickly. “We will scale that going into next year,” Ryan said.

There was no shortage of AI-related discussion at Money20/20, including its effect on jobs in financial services. 

E-commerce giant Amazon in October said it was cutting about 14,000 workers as it invests further in AI. Top banks such as JPMorgan Chase, Bank of America and Goldman Sachs have been eager to highlight how they’re employing AI; at the same time, the companies are cutting workers or slowing hiring. Citi, for its part, has trimmed its headcount as part of a broader effort announced in 2024 to cut 20,000 employees by 2026.

Ryan, however, emphasized the importance of employees’ role in carrying out the bank’s AI strategy. 

“Our job as leaders, regardless of whether you're in a fintech or a large financial organization like Citi, our job as leaders is to bring our people along, not to scare them,” Ryan said.