Canada

The Fed’s conclusion in the report titled “Stablecoins vs. Tokenized Deposits: The Narrow Banking Debate Revisited” is deceptively simple: the choice between stablecoins and tokenized bank deposits is not about cryptocurrency at all. It is about whether society wants money and lending fused together or pried apart.

In the New York Fed’s model, stablecoins function as “safe money” fully backed by low-risk assets, while bank-issued (including tokenized) deposits can fund loans and investment, tying money creation to credit expansion. The choice unpacked by the report is not one between “crypto” and “traditional finance,” but between two institutional models where one separates payments from lending while the other preserves their longstanding integration.

That creates a policy tradeoff whereby stablecoins may make the payments system safer but may reduce lending, whereas deposit-based tokenized money supports credit but can carry risk and require heavier oversight.

For finance leaders, the New York Fed’s analysis reframes stablecoins and tokenized deposits not as competing payment tools but as instruments with fundamentally different balance-sheet consequences. The choice between them affects liquidity strategy, counterparty exposure and ultimately the cost and availability of credit across the economy.

Read more: CFOs Eye Stablecoins as Capital Tool, Not a Crypto Bet

Expect Regulation to Shape Treasury Infrastructure 

The difference between stablecoins and tokenized deposits can be blurred in public discussion. Both are digital claims denominated in fiat currency and designed to function as settlement instruments on distributed ledgers. Their economic roles, however, diverge in ways that matter far beyond payments infrastructure.

One of the report’s clearest conclusions is that the dominance of stablecoins or tokenized deposits will not be decided by technology or consumer preference alone. It will hinge primarily on regulatory design and the incentives those rules create for banks.

Stablecoins provide a payments instrument insulated from bank risk, but they do not contribute to loan formation. Bank-issued tokenized deposits support lending, yet inherit the incentive problems and regulatory complexities of insured banking.

As regulation raises the cost of deposit creation, stablecoins become more attractive as transaction media; when regulation is lighter, banks expand tokenized deposits instead. Between these two poles lies a middle zone in which both forms of money circulate simultaneously, producing what the Fed report’s authors described as an optimal balance between efficient payments and productive lending.

The eventual balance between stablecoins and tokenized deposits may hinge less on policy than on how blockchain adoption unfolds.

Ultimately, banks will choose payment rails based on “the path of least resistance,” meaning the lowest barriers across risk, compliance, fraud prevention and technology migration, Himal Makwana, global head of corporate strategy at FIS, told PYMNTS in August.

If blockchain payments largely substitute for existing transactions, banks can migrate their deposit model into tokenized form without major structural change. If, however, digital networks expand the total volume of trade thereby enabling new categories of decentralized exchange, the demand for transaction balances may grow faster than banks can efficiently supply them, increasing the role of stablecoins.

See also: Why Banks Want to Issue Stablecoins

Two Forms of Digital Cash, Two Different Economies

The rise of blockchain money does not so much invent a new category of finance as it forces a decision about how closely society wants money tied to risk-taking institutions.

The traditional financial services landscape is already testing the waters around tokenized deposits. Federal Reserve Governor Michael Barr in October called tokenized deposits “more robust” than stablecoins; ex-Consumer Financial Protection Bureau Director Rohit Chopra said the same in May; and big banks like BNY, Citi, HSBC, JPMorgan and more are experimenting with them.

Stablecoins are also gaining traction across regulated financial products, with PYMNTS covering last week (Feb. 9) how banks and asset managers are integrating stablecoins into payments, settlement and asset servicing, noting how  what is taking shape is not a single “bank stablecoin” model, but a family of instruments that reflect where inefficiencies are most painful, and where incumbents believe blockchain rails can quietly outperform legacy systems.

Financial systems have repeatedly oscillated between integrating payments with lending and separating them to reduce systemic risk. Blockchain technology has not resolved this tension but may have made the boundary easier to redraw. The argument now unfolding among regulators, banks and FinTech firms is therefore less about cryptocurrency than about institutional design.

The PYMNTS Intelligence and Citi report “Chain Reaction: Regulatory Clarity as the Catalyst for Blockchain Adoption” found that blockchain’s next leap will be shaped by regulation; that evolving guidance is beginning to create the foundations for safe, scalable blockchain adoption; while at the same time, implementation challenges continue to complicate progress.

It’s overhyped because the commercial applications most people imagine—the turbocharged artificial intelligence (AI)—and magical optimization, are largely fiction.

It’s underestimated because the one thing quantum machines will almost certainly be able to do is break the cryptographic locks that secure the modern financial system. And migrating those locks will take years.

“The time to start thinking about migrating to quantum-resistant methods of encryption is now,” said Professor Scott Aaronson, who recently joined StarkWare as scientific advisor, during a conversation hosted by PYMNTS CEO Karen Webster.

Not next year. Not when a working machine appears. Now, he said, because the transition itself is the bottleneck.

That’s the paradox at the center of quantum computing in 2026 as Aaronson sees it. The technology is nowhere near delivering on its grandest promises. But its most dangerous capability, codebreaking, doesn’t need to arrive tomorrow to demand action today.

The Threat That Doesn’t Need to Be Imminent to Be Urgent

Consider the timeline problem. Aaronson said that even optimistic estimates place practical quantum attacks five to ten years out. But implementing a cryptographic transition across a major financial institution, one that touches every protocol, every system, every vendor relationship, could easily consume that same window.

The margin for error is essentially zero.

Worse, the attack surface is democratized. Unlike, say, a nation-state’s nuclear program, quantum computers will likely be accessed through the cloud. That changes the risk calculus entirely.

“At some point they will be useful for attacking cryptography,” Aaronson said. “And at that point, no one quite knows how to monitor all the requests coming in to see which ones are trying to sneak in breaking a cryptographic code.”

This isn’t a theoretical exercise in contingency planning. It’s a migration problem with a hard, if fuzzy, deadline. And the work is enormous.

A Computational Lemon—and a Very Small Glass of Lemonade

So if the threat is real, what about the opportunity? Here’s where Aaronson said the story can get uncomfortable for anyone selling quantum’s future.

The honest scientific consensus is that quantum computing’s credible commercial sweet spot is remarkably narrow.

Simulating quantum systems. Drug discovery. Battery chemistry. High-temperature superconductors. Industrial chemical reactions.

These are problems where classical computers genuinely struggle, because the size of the quantum state grows exponentially with each interacting particle.

“Nature has given us this computational lemon,” Aaronson said. “Why don’t we make lemonade out of it?”

Today’s machines, roughly 100 qubits running a few thousand operations, he says, are beginning to simulate simplified models. In narrow cases, they may even edge past classical methods. But “edge past” is the operative phrase. Transformative applications will require far larger, fault-tolerant systems with robust error correction, and the overhead to run such a system is staggering. A modest quantum advantage doesn’t easily justify building one.

That hasn’t stopped the pitch deck industrial complex, Aaronson said. Startups continue to hawk quantum-enhanced everything, from handwriting recognition to portfolio optimization to neural network training, with evangelistic fervor.

Aaronson’s assessment was blunt: “People eat that up with mustard. But to the scientists, most of this is worthless. If you’re not actually beating a classical computer, then it’s not interesting to us.”

The AI Question: Asymmetry in Both Directions

An inevitable question arises: What happens when quantum meets AI?

The relationship turns out to be lopsided, but not in the direction most people assume.

AI is already accelerating quantum research. Neural networks are improving error correction decoding, and deep learning is optimizing circuit design.

AI is helping quantum get better.

Is quantum returning the favor to AI? Much less likely, Aaronson posited. The reason is structural.

Quantum algorithms work by exploiting probability amplitudes, the complex numbers that can interfere constructively or destructively, to cancel out wrong answers and amplify the right one. It’s an intricate choreography that only works for problems with very specific mathematical structures.

Aaronson likened it to “this bizarre hammer that you have to find some nail that it can hit.”

Claims that quantum computers will revolutionize machine learning don’t hold up to his scrutiny or those of his fellow scientists.

“I don’t think the science supports it,” Aaronson said, adding that theoretical advantages like Grover’s algorithm exist on paper, but they’re modest and tend to get swallowed by the overhead of error correction.

He said he sees an even more fundamental problem: Classical computing won’t stand still while quantum catches up.

“Classical computing is a moving target,” Aaronson said. Again and again, claimed quantum advantages have evaporated once classical researchers sharpened their own algorithms. “You have to beat it. That’s the hard thing in this field.”

Compare that to AI, whose explosion comes from sheer breadth of application. “There seems to be no limit to what it is good for,” Aaronson said. “Any intellectual work that any human does … It has become harder and harder to find examples where AI could not do the same thing.”

Narrow Doesn’t Mean Trivial

Quantum computing’s impact, even in the best case, will be narrow. But Aaronson stressed that narrow is not the same as trivial. A handful of genuine breakthroughs in drug design or materials science could catalyze billion-dollar industries. The hammer is strange, but the right nails are worth hitting.

For financial institutions, though, the calculus is different. They don’t need to bet on quantum’s upside. They need to defend against its downside. And the window to do that is closing faster than the technology itself is advancing. The commercial payoff may be a decade away. The security risk isn’t.

That’s the brutal math Aaronson. And solving for it, he said, starts now.

Canada’s payments landscape is being recast at high speed, and the once-predictable routines of corporate treasurers are being pushed into the digital fast lane.

The change is as much cultural as technological for the practitioners who sit at the center of cash management.

“I have been on the ground here in Canada … for over 25 years,” Lyndsay Langford, Bank of America’s head of Global Payment Solutions for Canada, told PYMNTS. “In the past, we had wires, EFT and checks. Today, clients demand faster payments, less friction and richer remittance data.”

Those demands have forced treasury teams to abandon manual processes and build real-time visibility into positions and forecasts. The pandemic “shifted us toward a more digital mindset,” accelerating projects that once sat on multiyear road maps, she said.

That shift is playing out against a sweeping upgrade of Canada’s underlying rails. Payments Canada began its modernization plan in 2016, replacing the legacy wire system with Lynx and preparing the country’s first real-time rail (RTR).

“We’re heavily focused right now as a country on the buildout and the launch of real-time payments,” Langford said.

Once live, RTR will settle payments in seconds, carry data-rich messages and eventually enable QR-code requests for payment and cross-border links to other real-time payment methods. Interac — once a person-to-person tool — now supports near-instant business-to-consumer disbursements. Two years ago, Bank of America folded the channel into its global digital disbursements platform alongside Zelle and PayPal.

The glue that makes many of those ambitions useful to a treasurer is ISO 20022, a global messaging standard that expands the amount of information that can travel with each transaction. Bank of America has been running a formal ISO migration program since 2019 and has already completed “over 10 clearing migrations around the world,” Langford said.

Early Canadian adopters are exploiting the richer data to “remove a lot of manual work from the reconciliation process,” she said because ERP systems can auto-match invoices to payments the moment funds hit. The bank is building direct ISO feeds into corporate accounting platforms to maximize straight-through processing.

Risk Mitigation

Yet data alone does not erase the uncertainty of sending money across borders. Pandemic shocks, geopolitical tension and exchange rate swings have only amplified that uncertainty.

“Risk mitigation for cross-border payments is rising to the top of the priority list,” Langford said.

Bank of America’s answer includes a guaranteed foreign exchange (FX) rate solution that allows clients to lock in an FX rate for up to a year — “market leading” in tenor, she said — and CashPro Forecasting, a machine learning tool that predicts liquidity needs so companies can “make more intelligent working capital decisions” and pivot when volatility strikes.

Speed, meanwhile, is not always paramount.

“We want to make sure the options we put in front of clients are cost‑effective and fast where speed is important,” Langford said.

Still, the bank also aims for a globally consistent user experience so a Canadian treasurer responsible for Asia or Europe sees the same interface and controls.

Much of that experience is delivered through CashPro, Bank of America’s digital treasury suite used by more than 40,000 corporate clients worldwide. When the bank combined its Global Transaction Services and Enterprise Payments teams under the banner of Global Payment Solutions in 2023, it created a pipeline for ideas developed in the retail bank to flow into the commercial platform. The most visible import is CashPro Chat, built on the artificial intelligence backbone that powers Erica, Bank of America’s consumer virtual assistant. The tool lets treasurers ask questions and receive 24/7 answers from an “intelligent virtual service advisor,” escalating more complex issues to human specialists, Langford said.

“The more we collectively use it, the more it learns and grows,” she said.

Integration has also sped product rollouts. A U.S. electronic payments collections service that lets businesses accept card and ACH payments will soon debut in Canada after clients “raved about it for years,” she said, illustrating how the new structure “allows us to consider how investments can be leveraged more broadly.”

Listening to the Pros

Listening to clients is as important as engineering. CashPro advisory boards meet regularly “with clients around the world, and that includes right here on the ground in Canada,” ensuring new features target the highest priority wishes, Langford said.

The next inflection point is already on the horizon. The Retail Payment Activities Act will allow nonbank providers to participate directly in the RTR, intensifying competition and giving corporates more ways to customize how they pay and get paid.

“With this complexity, … the goal is to make things easier as they become more complex,” Langford said.

She said she expects that competitive pressure — and the data fabric of ISO 20022 — will trigger a wave of innovation in financing, supply‑chain optimization and value-added analytics.

“The real-time rail will be the platform for future innovation in Canada,” Langford said. “It’s going to increase competition, and when you have increased competition that benefits all Canadians — consumers and commercial entities alike.”

For treasurers, the tools are arriving quickly, and those who master them will turn liquidity management from a back-office chore into a source of strategic advantage.

“We need to be at the table — and we are at the table — to ensure our clients’ voices drive the next three- to five-year journey,” Langford said.

The pay later company and the travel search engine have offered services to travelers in the United States since 2023 and are now bringing their collaboration to Canada, Kayak said in a Thursday (May 22) press release.

“Consumers are increasingly turning to Affirm when booking their flights, hotels, rides and more as flexible payment options remain a top priority for travelers across Canada,” Affirm Chief Revenue Officer Wayne Pommen said in the release. “This expansion with Kayak is a natural next step for our long-standing partnership as we look to offer even more travelers peace of mind when paying for their next trip using Affirm.”

By choosing Affirm at checkout on Kayak’s Canadian website, approved travelers can split the cost of flights, accommodations, and car rentals or car sharing into monthly payments, according to the release.

Affirm already works with several other travel companies, including Booking Holdings brands Agoda, Booking.com and Priceline, which itself owns Kayak.

Kayak Chief Product Officer Matthias Keller discussed with PYMNTS Thursday the company’s launch of Kayak.ai, a conversational travel booking assistant powered by generative artificial intelligence.

“This is not just a chatbot,” Keller said. “It’s a ChatGPT just built for travel. You can ask any travel question, and in a conversation, you get real-life rates that are also bookable on Kayak.”

Kayak.ai was created to emulate natural human conversation and the kind of planning a traveler might do with a knowledgeable agent or friend. It isn’t fully automated, but future versions of the assistant will allow for seamless and even semi-autonomous bookings within the chat itself, Keller said.

“Right now, it’s more like an attended booking within the chat,” he said. “You’ll still see what you’re booking, confirm the price, and maybe enter your credit card. But over time, this could get more and more semi-automated. Maybe you leave your details, and the booking happens in the background.”

Meanwhile, Kayak’s integrations with major airlines, hotel chains and global distribution systems (GDS) offer it a competitive edge, Keller said.