Social Engineering Is Now the Industry’s #1 Fraud Concern

Fraud professionals now rank social engineering as the industry's top fraud concern—ahead of malware, hacking, synthetic identity fraud, and even deepfake-based impersonation, according to ThreatMark's Fraud Readiness Benchmark 2026. For banks, the finding signals a fundamental shift in where fraud succeeds.

The industry has spent years strengthening cybersecurity, authentication, and account protection against threats such as account takeover fraud. Yet increasingly, fraud succeeds by manipulating people rather than compromising technical safeguards—turning legitimate customers into the means of getting past the controls designed to protect them. 

While the pretext varies widely, from bank impersonation scams and fake crypto investments to long-term romance fraud, the principle remains the same. Strip away the different scenarios, and the social engineering definition comes down to this: deceiving people into taking actions that ultimately benefit the criminal.

The means, however, are becoming increasingly sophisticated. Phishing, smishing, and vishing continue to evolve, while new tactics emerge—powered by AI-generated voices, hyper-personalized messages, and other forms of deception built to scale. At this point, it’s becoming harder to find a scam where AI doesn’t play a role somewhere along the way. 

Why Scams Keep Winning

Social engineering isn't just the industry's top concern. For more than half of financial institutions, it already accounts for over 50% of fraud. In most banks, social engineering now sits behind the majority of fraud they encounter.

That prevalence makes more sense when you look at fraud from the attacker's perspective. Why crack a secure system or steal credentials when a well-executed scam can achieve the same result? AI helps fraudsters personalize attacks at scale, instant payments shorten the window for intervention, and organized criminal operations have turned deception into a highly efficient business model.  

And the model is working. When more than half of institutions say social engineering accounts for the majority of fraud they encounter, criminals have little incentive to change course.

The findings leave little room for complacency. Social engineering is no longer one fraud threat among many. For a growing number of institutions, it is the fraud problem.

A Warning Sign from Europe

The shift is not happening at the same pace everywhere. While 62% of European respondents say social engineering is linked to the majority of fraud at their institutions, 49% of North American respondents say the same.

That 13-point gap may not seem dramatic, but it’s telling.

Europe's more mature instant payments landscape offers some context for that gap. The easier it becomes to move money in real time, the more attractive social engineering becomes as an attack strategy. And as instant payments gain ground in the US, North America could face similar pressure. Juniper Research forecasts that the value of instant domestic payments in the US will rise from $5.9 trillion in 2026 to $28.5 trillion by 2030.

In that context, the 49% figure is less a reassurance than a sign of what's to come. 

What Transactions Don't Tell You

The findings point to a growing mismatch between how fraud happens and where many detection strategies were originally designed to look. As social engineering becomes more prevalent, fraud teams need visibility beyond the transaction itself—to the human behavior surrounding it.

The challenge is that, in a social engineering scenario, many of the usual signs of fraud may be absent. The customer may be using their own device, logging in with valid credentials, and authorizing the payment themselves. Viewed purely as a transaction, what is actually a scam can look remarkably legitimate.

That’s because the transaction captures only the final stage of a scam that may have been unfolding for hours, days, or even weeks. By the time money moves, much of the fraud has already happened—the trust has been built, the story established, and the customer manipulated into acting.

The Difference Is in the Behavior

A customer being scammed may still look like the right customer. But they don't necessarily behave like themselves.

This is where behavioral intelligence comes in. By analyzing how customers interact with digital channels in real time, it can identify signals that may indicate manipulation, coercion, or a scam in progress. Unusual hesitation, repeated changes in behavior, signs of remote access or guidance, or deviations from a customer's established interaction patterns can all provide valuable context that the payment itself may never reveal.

These behavioral signals are particularly valuable because social engineering rarely produces a transaction that is inherently fraudulent. The same payment journey can represent a legitimate transaction, an investment scam, or an impersonation fraud. What separates them is often not the transaction itself, but the behavior surrounding it.

Capturing that context helps close one of the biggest visibility gaps in modern fraud prevention: understanding not just what a customer is doing, but the circumstances under which they are doing it.

When the Right Customer Makes the Wrong Payment

The fact that social engineering ranks as both the industry’s top fraud concern and one of its most prevalent forms of fraud confirms just how far the battleground has shifted.

Protecting passwords, accounts, and networks remains essential. But these controls alone cannot address a threat that increasingly succeeds by manipulating legitimate customers into making legitimate-looking payments. Fraud strategies now need to address manipulation as directly as they address technical compromise.

That shift has financial consequences, too. As reimbursement obligations expand across markets, the more fraud losses are driven by social engineering, the more exposed financial institutions become.

The industry has spent years learning how to detect compromised accounts. The next challenge is identifying compromised decisions. 

See how in-session detection cuts reimbursement exposure. Book a ThreatMark walkthrough.