By John L. Culhane, Jr., Ballard Spahr/Consumer Finance Monitor
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The Treasury Department’s Financial Crimes Enforcement Network (FinCEN) has issued an Alert “urging financial institutions to detect, prevent, and report suspicious activity connected to fraud schemes targeting student aid programs administered by the Federal government.”
“Every dollar stolen from Federal student aid is a dollar taken from taxpayers and deserving students,” said Secretary of the Treasury Scott Bessent.
“Fraud rings use stolen and fraudulent identities, as well as other tactics, to enroll in educational institutions and unlawfully acquire funds from Federal student aid programs,” according to FinCEN officials. “The schemes not only result in losses to Federal student aid programs, but in some cases, real students face difficulties enrolling in classes because of the number of fraudulently enrolled ‘students.’”
They said that the Education Department has launched an “effort to prevent fraud in Federal student aid programs to protect taxpayers while significantly reducing associated administrative burdens on colleges and universities. [The Education Department and its Office of Inspector General] have consistently worked to detect, investigate, and facilitate the prosecution of fraudsters, as well as to communicate emerging fraud risks associated with Federal student aid and other ED programs.”
In late 2025, ED officials said that it had prevented $1 billion in Federal student aid fraud during that calendar year.
Elaborating on how many of these sehemes work, FinCEN said that fraudsters often steal personally identifiable information to create “ghost students.”
“To create ghost students, fraudsters may illegally obtain Personally Identifiable Information (PII) to impersonate an identity theft victim and pose as a legitimate student,” according to FinCEN. “Fraudsters may also use artificial intelligence or other tools to overcome identity verification by generating fraudulent documents that combine stolen PII with fabricated details, commonly referred to as synthetic identities. Victims whose identities are leveraged as part of ghost student schemes, including minors, are unaware that fraudsters are receiving Federal student aid using their PII.”
FinCEN officials said that in addition fraud rings sometimes use complicit “straw students” to obtain federal student aid. Straw students are individuals who, for a fee, provide their PII to fraudsters, who enroll them at educational institutions and collect financial aid refunds issued in their names.
They added that corrupt staff at educational institutions may also take advantage of their positions to defraud student aid programs by recruiting straw students and managing their educational records.
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By Tara Seals, Dark Reading
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In bad news for financial institutions, online retailers, cryptocurrency exchanges, and people in the dating pool who rely on identity verification to make sure they’re not getting taken for a ride, global fraud gangs are aggressively industrializing their scam efforts.
That’s according to Eric Huber, senior manager for adversary intelligence and disruption at TD Bank, who said that major centers for organized crime specializing in financial fraud (notably in Southeast Asia and West Africa) are bypassing “know your customer (KYC)” rules and other identity-verification methods with an updated AI tool set that offers the ability to build completely believable synthetic identities capable of fooling even advanced AI-enabled behavioral defenses.
His research into fraud cartels dovetails with recent numbers from Interpol, which in March said that, since 2024, the number of fraud-related campaigns it tracks has increased by 54%, fueled by AI. Over the same period, Interpol-supported member countries in more than 1,500 transnational fraud cases lost assets valued at $1.1 billion; it also deduced that AI-enhanced fraud is 4.5 times more profitable than traditional methods.
Speaking from the stage of the AI Summit at Black Hat USA 2026, Huber pointed to a real-world example of the threat landscape: recent updates to three-year-old tradecraft used by Nigerian scam rings called ProKYC, which is a turnkey KYC-bypass kit that defeats document-plus-selfie customer onboarding with convincing forged IDs and deepfaked “liveness” videos.
ProKYC: West African KYC-Bypass Supercharges Its Chances
To show how ProKYC is iterating in concerning ways, Huber first demoed a legacy version of the tool, which takes any photograph and generates a non-existent person using stolen personally identifiable information (PII). It first creates a convincing facsimile of a passport (in the demo, it’s an Australian document), as well as a video showing a person moving their head around, which should satisfy liveness detection. If asked, it can also emulate a mobile camera feed, so the verifier “sees” a live session with the fake person.
“It’s very convincing, and yeah, I’m sure you’re going to see a successful attack at the end,” Huber said. “Are bad guys very pleased? Yes.”
That’s impressive enough, but in the age of deepfake detection and other AI fraud-rooting defenses, this baseline deepfaking isn’t as successful as it once was. So, newer versions sold via Telegram channels offer specifically tailored workflows for a broad target list across crypto-exchanges and fintechs, with new AI-enabled features that can match the verifier’s request flow automatically.
One of the more notable features is auto-generation of “selfie-with-ID” uploads (essentially, a person holding a “real” ID that was just purportedly uploaded for verification, featuring a desk, keyboard, or table in the background for verisimilitude).
“The thing is, if you were defending yourself, how are you going to defend yourself against a perfectly convincing [fake picture supposedly taken in real time]? One of the things you do is you’re going to look at the ID, and [ask for backup verification],” said Huber. “So ProKYC will go ahead and create that selfie, or even emulate the supposed customer taking a picture of the picture on a keyboard or a table.”
Other recent AI-enabled features include automatic geo-tailored EXIF metadata insertion on the fake images to evade origin checks. Thus, a selfie-with-ID picture “taken” in Nigeria can be made to say it was taken in Sydney, in the Australian example.
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By Patrick Sickels, CUSO Magazine
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There exists in the plaintiff litigation world a strategy occasionally referred to as the “edge,” the “hit list,” or more commonly as the “reptile” strategy. Paraphrasing the foundational principle of this strategy, plaintiffs are seeking to trigger a fear response in a juror by framing defendants as dangerous to the public, with the verdict being the only method to protect innocents, and by extension the jurors themselves.
This is very relevant to credit unions and credit union service organizations because privacy and data security lawsuits heavily rely on these tactics to receive favorable, large-dollar verdicts.
Background to the reptile strategy
In the late 2000s, David Ball and Don Keenan published Reptile: The 2009 Manual of the Plaintiff’s Revolution. The strategy borrowed a (since discredited) neuroscience theory that humans have a reptile brain that governs self-preservation underneath the rational and emotional layers.
Although the underlying science itself may have been disproven, the reptile strategy has proven highly effective in litigation. The structure is to create in the minds of the jurors a safety rule that has been violated by the defendant. The final framing is to suggest a monetary verdict favoring the plaintiffs is the only way to ensure the safety of the community.
Reptile strategy also has the advantage of clarity, where jurors are presented with “right versus wrong” or even “good versus evil” arguments. A verdict favoring the defendant is not just wrong but is immoral.
Building the perfect reptile
This strategy is developed in the early phases of litigation, through discovery such as depositions. The goal is to frame the issue at hand where denial of a question’s premise damages the defendant’s credibility, but where answering it affirmatively creates a standard that is impossible for the defendant to meet.
An example would be: “Is a credit union obligated to follow the federal regulations on data security? … And do you agree those regulations are imposed for the safety of the public?”
The goal of this question is to create a trap where a judgment call is turned into an absolute. GLBA regulations are transformed from a regulatory compliance framework into “safety rules” where any violation endangers the jurors. Unless these questions are objected to, nearly all defense witnesses will answer yes to these questions. At that point, the plaintiffs have already won a significant concession, since any potential violation is now a matter of public safety which affects the jurors.
Another powerful strategy is to use contract and policy language against the defendant, by finding absolute language. Trap words include statements such as “never,” “always,” “immediately,” and “highest,” which permit the plaintiff to argue a judgment call is in actuality an absolute standard.
In one pixel tracking case, the plaintiffs noted in their complaint that the defendant’s “privacy policy stated expressly that ‘we never provide advertisers or any other third parties any information that reveals a personal health condition or personal health information.’” The plaintiff went on to say that “sensitive personal information communicated … including health information relating to medical treatments and prescriptions, was disclosed to and intercepted by some of the largest advertising and social media companies in the country…”
Use of the word “never” in a public-facing policy opened the door for an argument framing the defendant as a hypocrite and dangerous to public safety. The plaintiffs ultimately won a monetary settlement award.
Use of reptile arguments
Plaintiffs using reptile strategies will ensure that it is designed to have maximum impact on a potential juror. The kinds of highly persuasive closing arguments will have elements designed to sway jurors away from legal arguments and instead towards abstract notions of morality.
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2026 NASCUS Legal Symposium
Stay Current. Get Connected. Be Prepared.
Join us, Nov. 12–13 in New Orleans, as we bring together agency general counsel, credit union counsel, and private practitioners from across the country.
This new 1½-day event is designed to support professional development and meaningful dialogue around the legal issues shaping the credit union system. Through focused sessions and peer exchange, attendees will gain practical insight they can take back to their organizations.
Learn MoreBy Jason Miller, Federal News Network
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SBA is proposing to reduce the number of and simplify the approach to the NAICS codes and reduce the total size standard categories from nearly 1,000 to 338.
The Small Business Administration is trying to open the federal contracting door to more small businesses. In a new proposed rule issued today, SBA is updating the small business size standards that would make more than 114,000 companies eligible for federal small business contracts.
Under this new regulation, small business employee-based and revenue-based thresholds would significantly increase as part of how the agency says it’s trying to address long-standing complaints about how the government determines size standards.
Additionally, SBA is proposing to reduce the number of and simplify the approach to the North American Industry Classification System (NAICS) codes as a way to define small business markets. The agency wants to revamp the existing NAICS structure of five or six digits to a four-digit classification system that reduces total size standard categories from nearly 1,000 down to 338 broad industry groupings. Of the 338 new groupings, the SBA says 45 would retain their current size standards, while 86 would change from revenue based to employed based and the remaining size standards would continue to be revenue based.
“This proposal ensures that these job creators have the regulatory certainty to scale, expanding small business eligibility by 0.3%, or over 110,000 firms. By streamlining definitions, the SBA will expand access capital, counseling and contracting opportunities, which in turn create jobs and drive growth,” said SBA Administrator Kelly Loeffler in a statement.
Along with the new size standards, SBA is proposing major changes to the methodology to determine these thresholds.
SBA wants comments on the new size standards, and particularly six specific questions about the proposed methodology changes, by Sept. 21.
The agency last updated size standards, mainly for inflation purposes, in 2022 and it last changed the methodology in 2024.
Missing a critical step?
Experts say this may be the first time SBA released an updated size standard methodology at the same time it released the new size standards and that is worrisome.
During the administration of President Joe Biden, John Shoraka, chairman of GovConPros and a former SBA associate administrator of Government Contracting and Business Development, said the agency released the draft methodology, received comments and made changes. Only then, he said, did SBA issue new draft size standards. He said the government never finalized the update to the revenue-based size standards and the revamped employee-based size standards never got out of SBA even as a proposed rule.
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By Taylor C. Nelms, Financial Health Network
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What Americans can afford today shapes what they can build tomorrow. Our upcoming Pulse report offers a new look into affordability and financial health.
What do we mean when we say “affordability”?
Is it inflation, a way to translate CPI numbers into kitchen-table economics, from the price of eggs to the hammer of a hospital visit? Is it wages, which may be rising (unequally) but are still falling behind the cost of living? Is it the sticker shock of a house or a college education—things that have historically served as on-ramps to long-term wealth-building? Is it broken markets for things like utilities or health insurance or childcare?
Back in January, The New York Times polled voters about their feelings on the affordability of a range of goods and services. The majority felt that a middle-class life was out of reach and pointed to the cost of things like buying a home, paying for healthcare or childcare, earning a degree, or saving for retirement as evidence.
In June, the Times published an op-ed by former Federal Reserve Vice Chair Lael Brainard and former Consumer Financial Protection Bureau (CFPB) Director Rohit Chopra presenting additional survey data showing that the cost of food—that most basic of necessities—has become a leading source of financial strain for middle-class families.
These pressures aren’t just today’s financial headaches. They’re the core factors shaping which households will enjoy financial security and opportunity—and which ones won’t—in the years ahead.
The Growing Unaffordability of a Middle-Class Life
The “middle class” is a slippery thing to pin down. Its meaning has changed over time and means different things to different people. Income alone can’t capture its emotional, social, and political force. What the phrase really encompasses, I think, is the suite of things that we collectively agree should furnish a good life—the kind of life we want to live and feel we ought to be able to live.
Affordability is the word we now reach for to name what it costs to live that life, however you may define it. Affordability is both the cost of getting ahead, and it is the cost of getting by. It’s the mortgage, and it’s the rent. It’s the student debt payment, and it’s the cost of groceries.
All of these things are drifting further out of reach, but their retreat is uneven in a way that is increasingly dividing us. The wealthiest 10% of households hold roughly 87% of the total value of corporate equities and mutual fund shares, and spending by the highest-earning households is outpacing that of lower-income households at a growing rate. By one popular estimate, the top 10% of earners account for almost half of all consumer spending. Is it any wonder consumer sentiment is at historic lows?
The etymology of “to afford” predates modern money as we know it. In early English, it meant “to accomplish or carry out”. Only between the 14th and 16th centuries, during the transition from European feudalism to global capitalism, did it acquire the sense of bearing a cost or having enough to buy something. But the two meanings never fully separated. To afford something is still, at its root, to be able to achieve it.
So, when we ask whether a family can afford groceries, a middle-class life, or the “American Dream,” we’re really asking about their capability to meet basic needs—both today and tomorrow—while still feeling control over a future they cannot see.
That capability has a name: financial health.
We Need the Receipts
For nearly a decade, the Financial Health Network has taken the temperature of household financial health in the U.S. through the annual Financial Health Pulse®. This initiative tracks whether and how families can spend, save, borrow, plan, and protect their finances in ways that let them meet their needs, absorb shocks, and pursue opportunity over time. Our approach is deliberately holistic, looking past income or credit scores alone to see how financial lives hold together—and how changes in financial health outcomes relate to material hardships, disparities across place and population, and long-term security and prosperity.
If affordability is the price of entry to live a good life, then financial health stamps the ticket.
As I’ve thought about the growing national conversation about affordability, I’ve found myself returning to a modest, throwaway artifact: the receipt. The receipt records two things: what you paid, and what you got for it. Affordability, then, is the growing gap between them, driven both by a higher price and by a deterioration in what that money buys—fewer ounces in the bag, thinner insurance coverage, the app that now charges for a version that does half of what it used to do for free.
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By Grace Huckins, MIT Technology Review
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The models responsible for last month’s agent hack of Hugging Face had been inadvertently trained to cheat and to communicate with each other, according to an OpenAI technical report released today. The hack, which a group of agents undertook to find solutions for a cybersecurity test that they were stuck on, has confirmed some experts’ fears that AI models might take actions that defy human desires and expectations.
Since the hack, OpenAI employees—as well as researchers at the AI evaluation nonprofit METR, which released its own report on the hack today—have worked to understand what went wrong and how similar missteps might be prevented in the future. OpenAI has already put some preventative measures in place based on what they discovered. But making sure AI models do what we want them to do, or “alignment,” remains a gnarly problem, and some of the root causes of the hack will take much longer than a month to resolve.
“It’s not something you can solve overnight,” says Kai Chen, who runs OpenAI’s alignment research team. “There are challenges we’ve been tracking for a very long time, and we’re now seeing them with much greater precision.”
The Hugging Face hack was a product of months of misbehavior from OpenAI agents, first as they were being trained and then as their abilities were being evaluated. This May, agents in training figured out how to use OpenAI’s infrastructure to communicate with one another and get support with difficult training tasks, including some that were impossible to solve without hacking or otherwise misbehaving. That “message board” was shut down.
Then in July, while being evaluated for their cybersecurity abilities, some models created a new message board. They were supposed to be isolated from the internet, but by working together they managed to get online, hack Hugging Face, and obtain solutions for the cybersecurity problems that had stumped them.
Based on their investigation, OpenAI researchers believe that events during the training phase led directly to the hack. “For almost every behavior that was worrisome at evaluation time, [we were able to] find some sort of associated behavior at training time that actually we think might have contributed to it,” says Eric Wallace, a member of OpenAI’s alignment research team.
When models correctly solve problems during training, the behaviors that led them to that solution are reinforced, and they become more likely to engage in them in the future. So if a model completed a task in May after using the original message board, it became more likely to participate in a new message board later on. This phenomenon, where AI agents misbehave in ways that are reinforced during the training process, is known as reward hacking.
Reward hacking also helps to explain why the models worked so hard to make their way onto the internet. During its investigation of the incident, the OpenAI team found that, over the course of training, the models became more and more likely to probe their digital environment for weaknesses and use the tools at their disposal in unexpected ways—a sign that these behaviors were being gradually reinforced. By the time the models were facing tricky cybersecurity problems, they had learned that hacking was an effective way to achieve their goals.
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By Rachel Atarah, Finsurance Biz
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What is crowding out in macroeconomics? Crowding out is the reduction in private investment, consumption, lending, or net exports that can occur when government borrowing or spending competes with private economic activity.
The traditional crowding-out effect begins when a government finances a budget deficit by issuing debt. Additional public borrowing increases demand for financial capital and may raise real interest rates or private credit costs. Businesses and households may consequently postpone factories, equipment purchases, construction, housing, research, and other interest-sensitive spending. Understanding what is crowding out in macroeconomics helps explain how fiscal policy can influence private borrowing and investment decisions.
Crowding out can also occur without a large increase in general interest rates. Government projects may compete directly with businesses for skilled workers, land, energy, machinery, and construction materials. Banks may also allocate more credit to government securities, leaving less financing available for private borrowers.
In an open economy, higher domestic interest rates can attract foreign capital and strengthen the currency. Although foreign capital may soften the decline in domestic investment, currency appreciation can make exports less competitive and reduce net exports.
However, government borrowing does not automatically reduce private investment dollar for dollar. The outcome depends on domestic saving, monetary policy, inflation, international capital flows, financial conditions, economic capacity, and how effectively the borrowed money is used.
Government spending may produce the opposite result—known as crowding in—when productive public investment improves infrastructure, technology, education, demand, or the profitability of private projects. Therefore, answering what is crowding out in macroeconomics requires considering both the private activity displaced and the economic value created by government spending.
Quick Answer: What Is Crowding Out in Macroeconomics?
What is crowding out in macroeconomics? Crowding out in macroeconomics happens when government economic activity replaces private economic activity that would otherwise have occurred.
The traditional process is:
- Government spending exceeds tax revenue.
- The government borrows to finance the budget deficit.
- Demand for savings and financial capital increases.
- Real interest rates or private credit costs may rise.
- Businesses and households reduce borrowing.
- Some private investment, consumption, or construction is delayed or canceled.
Crowding out is generally stronger when an economy is near full employment and productive capacity is already heavily used. It is often weaker during a recession when unemployment is high, private demand is weak, and businesses have unused equipment and facilities.
Understanding what is crowding out in macroeconomics also requires recognizing that its strength depends on economic capacity, private credit demand, monetary policy, and how the government finances its spending.
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By PYMNTS
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Debit cards are used several times a day without attracting much attention until the moment something goes wrong.
A declined grocery purchase, an unfamiliar merchant name in a banking app or a card shut down because of suspected fraud can quickly change how a customer views the institution behind the card. The stakes are greater when debit is the consumer’s principal means of payment, and there is no readily available alternative.
That puts pressure on banks and payments providers to look beyond whether a transaction technically works and consider the experience surrounding it, Justin Monk, vice president of Debit ATM and Software at FIS, told PYMNTS.
The relationship now begins with digital account opening and extends through instant provisioning, wallet access, money movement, transaction controls and fraud management.
The ease of opening another account has also changed the consequences of a poor experience. Consumers can add a competing financial relationship without formally leaving their existing bank, creating what Monk described as a “soft switch” in which customers test another provider and gradually redirect their activity.
“The big pain points in the ecosystem have been figured out over time,” Monk said.
More recently, however, consumers want to customize their experiences.
Expectations can vary by generation. Young consumers may be more comfortable with digital enrollment, instant credentials and new security features, while older customers may require more explanation or support. That complicates product development because an issuer cannot assume that every feature that improves security or functionality will be understood in the same way by every cardholder.
The optimal debit experience, then, is one in which the card remains available when the legitimate customer needs it, while security controls operate with as little unnecessary interruption as possible. That balance becomes especially important when a fraud system declines a valid purchase.
The accumulated effect of such incidents can eventually undermine the relationship, Monk said.
“This is not to say that one [pain point] by itself will cause a cardholder to go someplace else, but multiples of these stack up over time, and it adds to a level of frustration,” Monk said.
Better Data Can Reduce Debit Friction
One source of friction, the quality of transaction data moving through the payments system, sits far from the consumer interface.
“The data is foundational to all the downstream systems that utilize it,” Monk said, pointing to chargebacks and fraud rules.
Improving that information flow can help issuers make better fraud decisions, reduce false declines and prevent avoidable chargebacks. The consequences can be particularly disruptive with debit because freezing or replacing the card can temporarily separate a consumer from the money used for everyday expenses.
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By PaymentsJournal
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As financial institutions merge and evolve, the pressure on back-office operations grows just as quickly as it does on member-facing services. Accounting teams that once relied on manual processes and patchwork systems are now expected to deliver greater accuracy, faster reporting, and the flexibility to support future growth.
As a result, many banks and credit unions are reevaluating whether their current accounting platforms can keep pace—and looking for partners that can support both today’s demands and tomorrow’s challenges.
In a PaymentsJournal Podcast, Kellie Rychwalski, Chief Financial Officer at Del-One Federal Credit Union, Kandra Person, Senior Solution Consultant at Fiserv, and James Wester, Co-Head of Payments at Javelin Research and Strategy, discussed the accounting solutions available to financial teams today. Newer platforms have made significant advances compared to the way things were handled in the past.
“I was just looking for efficiencies,” said Rychwalski. “Simply being able to attach a PDF of an invoice to an accounts payable or fixed asset transaction instead of filing is a huge time saver.”
Seeking a Platform with Greater Functionality
When Rychwalski joined Del One in 2012 as the Director of Accounting, she found an integrated general ledger (GL) system that lacked much of the functionality the credit union needed.
“We were looking for something that was core agnostic,” said Rychwalski. “We knew that we would be changing data processors or core systems at some point, and didn’t want to have to continuously move the GL.”
Del-One eventually selected Fiserv’s financial accounting and finance operations platform, Prologue, in a hosted environment. The credit union would receive full support from Fiserv, and if they changed core systems in the future, they wouldn’t need to replace the entire GL again.
When the credit union merged with Louviers Federal Credit Union and migrated its GL into Prologue, the transition was easy for the team to absorb. From day one, they were able to produce consolidated financials without waiting for the operational merge date.
“We could still balance to the different core processors of their different outside vendors, but we could bring our financial statements together as one consolidated financial statement,” Rychwalski said. “For the person who spent two months manually combining them, that was a really big deal.”
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Published on PYMNTS.com
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Digital bank customers are turning the mobile wallet from a convenience into their default way to pay.
That shift stands out in “Pay by Bank Deep Dive: Digital Bank Users Are Ready to Switch,” a PYMNTS Intelligence report with Trustly based on a survey of 2,071 U.S. bank customers. The research found that digital bank users are younger, more mobile-focused and far more likely than other consumers to prefer digital wallets. That familiarity could also make them receptive to Pay by Bank, which lets shoppers authorize payments directly from a bank account through a digital flow.
Key Points:
- 44.6% of digital bank customers prefer digital wallets, compared with 22.7% of consumers overall.
- 40.9% of digital bank users prefer wallets for retail purchases, while 29.5% favor debit cards and 18.9% prefer credit cards.
- 51.9% use wallets for rideshare payments, while 43% prefer them for subscriptions and 37.7% choose them for groceries.
- The findings show that digital wallet use among digital bank customers extends well beyond one or two mobile-first categories. Wallets lead for retail, subscriptions, rideshare, gambling and account-to-account payments. Debit still holds an edge for groceries and bills, though wallets remain a significant choice in both categories.
That broad use gives banks, merchants and payment providers a clearer path to introduce new payment options. Digital bank users already understand login-based checkout and tokenized credentials. Moving them toward Pay by Bank may feel less like teaching someone a new language and more like adding a familiar word to the conversation.
The report also found that incentives could accelerate adoption. Digital bank users said they would shift as much as 35.4% of account-to-account transactions to Pay by Bank when discounts and buyer protection are included. They could also move 32% of bill payments, 28.8% of gambling transactions and 27.3% of rideshare purchases.
Immediate cash benefits ranked as the leading incentive among 43.1% of digital bank users, while 16.9% cited buyer protection. More broadly, 69% of digital bank customers already view Pay by Bank as a debit substitute or would do so with rewards, buyer protection or both.
For banks and merchants, the opportunity is encouraging. Consumers who already organize much of their financial lives through phones appear open to another digital payment option, provided the experience stays simple, protections remain clear and the financial value is easy to see. The wallet may serve as the bridge between familiar card payments and a broader range of direct bank transactions.
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By Jeffrey Young, Principal Research Scientist, Partnership for an Advanced Computing Environment, Georgia Institute of Technology, Published in The Conversation
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You’ve probably heard artificial intelligence models described as “open” or “closed.” These are not descriptions of the model’s personality. Large language model AIs like the one under the hood of ChatGPT don’t have actual personalities, despite appearances.
The labels refer to whether all of the information about how an AI model works is publicly available and the model can be modified, or whether the model’s developer keeps its inner workings secret and the model itself private property.
Open-source software
The concept of open-source software originated in the free software movement of the 1980s and ’90s. The movement’s founders believed that software creators and users had the right to “four freedoms” – to run the program, to study and modify it, to distribute copies of the original, and to distribute copies of subsequently modified versions. The fundamental requirement was that the source code – the basic instructions – for a program should be made available.
In the late 1990s, software developers associated with projects such as the Netscape web browser and the Linux operating system coined and promoted the term “open source” to refer to these ideals.
As part of the evolving movement, certain organizations developed open-source licenses that specified how a particular piece of source code could be used and distributed, including the Gnu General Public License, Apache License, MIT License and the Berkeley Software Distribution. Each type of license also specified any potential restrictions on how software patents applied to the source code.
Open source or open weight?
The open-source idea has risen to prominence again in the past several years as artificial intelligence large language models have surged, notably OpenAI’s ChatGPT, released in 2022. Developers first train new models on large datasets, then deploy the models for use by other people.
Meta was one of the first large companies to release an open-source large language model, called LLaMa. The company released LLaMa on Feb. 24, 2023, and made available the “inference” source code – the instructions that run the model. And it released the so-called weights, the encoded knowledge the model learned during training. However, open-source organizations such as the Open Source Initiative have stated that the LLaMa licensing guidelines prohibit commercial reuse, which the initiative maintains is not truly open source.
Other companies have released “open weight” models, such as DeepSeek from DeepSeek AI and Qwen from Alibaba. The models have less restrictive terms for reuse, and the AI community has adopted them rapidly. Still, many developers believe that a true open-source AI model must not only include the source code and weights but also the data that is used to train the model.
A lot to open up
The Open Source Initiative’s definition of a fully open-source AI model includes the training data as a key element. Some developers wonder, however, how feasible it is to distribute the enormous datasets required.
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By Carter Pape, American Banker
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Scam victims report to their bank, not the government.
When Americans reported a scam last year, most went to a bank, credit union or payment app. Federal agencies such as the FTC and the FBI learned of only a small share, leaving the government with a fraction of the full picture.
Americans told the federal government they lost about $16 billion to scams in 2025. They actually lost closer to $68 billion, a new survey estimates, and most of the people who reported a scam took it to a bank or a payment app, not to the government.
That gap is the central finding of “United States of Scams,” a report Gallup and the nonprofit Stop Scams Alliance released last month. It draws on a survey of 5,173 U.S. adults. The money scammers stole last year, it estimates, runs several times higher than Washington’s tally.
Victims reported only 13% of scams to federal law enforcement or the Federal Trade Commission, or FTC, the survey found. Respondents took 55% of their scam reports to a bank, credit union or other financial institution, and 25% reported them to a payment app.
About seven in eight scams never reached a federal agency at all. That makes banks and payment companies the first place most scam losses surface and the closest thing the country has to a national scam-reporting system.
The catch is that almost nothing a bank sees travels any further. The reports that land at a bank rarely reach the national systems law enforcement uses to track scams.
The findings feed a fight in Washington over whether banks should get legal cover to share the data they see about scams and whether banks need to cover more of what customers lose to these scams.
What the FTC counts
The FTC’s estimate of $16 billion in total losses to scams comes from consumer complaints. In 2025, consumers filed 3 million fraud reports with the agency and said they lost $15.9 billion, up from about $12 billion the year before, according to March testimony by FTC leadership before a congressional committee.
Consumers reported more than 1 million imposter scams, making it the most commonly reported category of scam. Investment scams took $7.9 billion, making it the most costly.