2 Years Ago vs Today: Bank-Grade Security’s Shift From Protection to Precision

Life comes at you fast, and so does money. That’s creating a growing problem for banks and their fraud, financial crime and cybersecurity defense perimeters.

Two years ago, “bank-grade security” sounded like a fixed standard. It suggested encryption, compliance rigor, multifactor authentication, fraud monitoring, data protection and the operational discipline associated with regulated financial institutions. The phrase worked because it implied that banks and payment firms operated inside hardened environments, protected by rules, controls and institutional habits that most technology companies could not easily match.

Today, that definition is no longer enough. The financial system has moved faster, become more programmable and embedded itself into more consumer and business experiences.

See also: The End of the Artisanal Hack: How AI Industrialized Cybercrime

Bank Grade Security Now Means Living Intelligence, Not Fortress Walls

Two years ago, a financial institution could still describe security as a compliance posture. Today, security is increasingly a real-time operating capability.

The financial system has moved from protected environments into distributed experiences. Consumers pay through wallets, marketplaces and apps; while businesses move money through enterprise software, embedded banking platforms and real-time rails. Credentials are increasingly tokenized, passwords are giving way to passkeys, and to top it all off, fraudsters are using more advanced tools to fool financial institutions (FIs) while the legitimate customers and members of those FIs have been trained, on their end, expect fewer interruptions — making authorization friction an even more delicate balancing act.

“If a human can do it, we are now at a stage where the machines can do it in plausible ways,” Adam Hiatt, vice president of fraud strategy at Spreedly, said. “It’s an arms race.”

That changes the role of security. It must be portable across ecosystems, contextual enough to understand the transaction and invisible enough not to break the experience. Authentication, device intelligence, behavioral signals, token controls and fraud scoring are becoming part of the product architecture itself.

The PYMNTS Intelligence report “Defending the Member: How Credit Unions Are Responding to a New Fraud Landscape,” a collaboration with Velera, found that 77% of credit unions experienced unauthorized network access during the past year, underscoring how broadly the problem now reaches across the industry.

The new meaning of bank-grade is not just strong walls, but smarter flows. The challenge is precision. Too little friction creates losses. Too much friction teaches customers to ignore warnings or abandon legitimate transactions.

“The amount of transactions that are falsely declined vastly outweighs the actual fraud,” Dewald Nolte, co-founder and chief strategy officer at Entersekt, told PYMNTS in a June interview. “Ironically, we’re declining a lot more good transactions than we’re actually stopping fraud.”

See also: AI Fraudsters Are Building the Perfect Fake Borrower

Faster Payments Push the Fraud Clock Toward Adaptive Trust

For banks, FinTechs and payment providers, security is becoming a growth issue versus a purely risk concern. Real-time payments have made the shift more urgent. Speed improves cash flow, settlement certainty and customer experience. It also compresses the window for stopping fraud.

The security implication is not that instant payments are inherently unsafe. It is that they require a different operating model. When money moves quickly and with greater finality, institutions have less time to detect anomalies, contact customers or recover funds. Risk decisions must move closer to the point of authorization.The provider has to know whether the account is trusted, whether the beneficiary is suspicious, whether the behavior is unusual and whether the customer may be acting under manipulation.

“We’re moving beyond an era where I’m looking to stop ‘one thing,’ and instead I’m looking for behaviors. It’s how people interact with you that is becoming more relevant,” Richard Swales, chief risk and compliance officer at Paysafe, told PYMNTS in an earlier interview.

Authentication is changing for the same reason. Passwords, SMS codes and traditional multifactor authentication were built around proving access. But many modern fraud schemes do not begin by breaking encryption but instead by tricking the user. Authentication is necessary, but it is no longer the whole answer when a valid login does not guarantee a valid payment.

See also: The Cybersecurity Hit List: From Enterprise AI to Compromised Coffee Machines

Embedded finance has also pushed financial functions into channels controlled by someone else. A consumer may open an account, finance a purchase, store a credential or authorize a payment inside a merchant, software or platform environment. The bank may carry the regulatory obligation, but it may not own the full interaction.

The PYMNTS Intelligence report “Vendors and Vulnerabilities: The Cyberattack Squeeze on Mid-Market Firms” found that hackers are increasingly going after middle-market firms, which depend on third-party cloud providers, software-as-a-service platforms, managed service and logistics providers, which can leave them vulnerable to attack.

Tokenization has also changed the security model. Card credentials and payment data are becoming less static and more conditional. A token can be limited by merchant, device, wallet, network or use case. If compromised, it is often less useful outside its intended context. The larger trend here is that security is becoming dynamic. Payment data is not simply stored and protected. It is activated under rules. Those rules can be updated, revoked, scored or stepped up depending on the transaction.

As a result, the most important security question now is not, “Is this the customer?” It is, “Is this what the customer actually intends?” Providers need to identify whether the payment destination is risky, whether the behavior fits the customer, whether the user is under pressure and whether the transaction resembles known scam patterns.