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Banks are facing pressure on multiple fronts.
Regulators are cracking down on peer-to-peer payment (P2P) scams, holding banks accountable for payment scams that happen on platforms like Zelle.
Customers — fatigued by frequent account takeover fraud (ATO) attempts and scam alerts — demand both stronger account security and a seamless experience.
Product teams are expected to deliver on the promise of biometric authentication and AI–driven fraud models.
Underlying these challenges is one single, critical requirement: Banks must be able to reliably distinguish legitimate customers from fraudsters every time. In this article, we’ll break down key fraud risks for banks and share best practices for staying ahead of them.
P2P scams and account takeovers in the spotlight
Peer-to-peer (P2P) payment scams have become a focal point for regulatory scrutiny and consumer frustration. Zelle, in particular, has drawn attention from lawmakers and government agencies, which continue to pressure the largest banks to refund scam payments and implement stricter, real-time controls to prevent other types of fraud schemes like money muling.
Additionally, customers that use P2P platforms are also at risk of ATO. The challenge here for banks is that ATO activity many times can look authentic: For example, fraudsters can use credentials obtained in a data breach to access real accounts to transfer money to accounts they control. By the time the legitimate account holder notices suspicious activity, it’s often already too late to recover the funds.
To stay ahead, fraud teams must be able to identify and block mule accounts, intercept ATO attempts, and prevent payment scams — all while maintaining the instant, seamless nature of P2P payments that customers want. In other words, they need to find the balance of having just enough friction to stop fraudsters, but not too much friction that frustrates legitimate customers.
Biometrics, 3DS, AI & the push for seamless account security
To address these challenges, banks are investing heavily in authentication modernization. Biometric payment options — such as face and fingerprint recognition — are being introduced to provide robust identity verification that feels effortless for customers.
At the same time, developer-led 3D Secure (3DS) APIs are empowering product and merchant teams with more control over risk-based authentication, making it possible to tailor security measures to specific contexts to increase transaction approval rates, reduce false declines, and improve checkout UX.
The goal is to combine strong identity proofing and device binding with flexible, context-aware authentication that reduces false positives to provide a smooth user experience, without compromising on account security.
AI and machine learning are also playing an increasingly significant role in banking fraud prevention strategies, with 71% of financial institutions using the technologies in their fight against fraud — but one key challenge remains: Fraud and security teams need real-time, low-latency, high-quality signals to continuously train their models to stay ahead as fraud tactics evolve.
Using Fingerprint’s device intelligence to stop banking fraud
The Fingerprint team works closely with leading banks, and we see these challenges firsthand. We’ve learned that device intelligence doesn’t need to replace existing fraud prevention systems — instead, it can help enhance their effectiveness.
Many banks already rely on established fraud prevention tools such as ThreatMetrix. By running Fingerprint alongside existing fraud prevention tools, banks gain increased fraud detection accuracy and reduce latency. This is especially critical in high-velocity P2P payment environments where milliseconds can make the difference between stopping or missing fraudulent transactions.
Fingerprint uses more than 100 signals to generate a unique, persistent identifier for each visitor — what we call the visitor ID. This identifier remains stable even when users clear cookies, switch networks, or use private browsers to attempt to mask their identity. By persistently linking devices to true usage patterns, our solution helps banks identify shared devices, compromised accounts, and suspicious activity that might otherwise go unnoticed.
Key takeaways: Balancing fraud reduction, risk management, and customer experience
Every large financial institution juggles multiple challenges: how to satisfy regulators, protect customers from the latest scams, and modernize authentication — all while preserving a frictionless banking experience for legitimate customers.
With today’s increasingly sophisticated fraud threats, legacy point solutions and blanket multi-factor authentication requirements are no longer enough to protect customer accounts. To stop Zelle scams and ATO fraud, deliver the best authentication experiences, and power effective AI-driven fraud detection, banks need high-quality device intelligence as a foundational layer.
With persistent device identification and real-time contextual signals, Fingerprint enables banks to confidently meet compliance demands, harden P2P and payment rails, and support advanced authentication flows, all while keeping customer experience at the center.
If you’re ready to take the next step in fraud prevention, our team is available to chat about your bank’s needs or help you explore our platform with a free trial.
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