How should banks reduce P2P scam losses without slowing down legitimate payments?

Why This Matters for Security Teams

P2P scam loss reduction is a balancing act between customer protection, fraud operations, and payment speed. If controls are too rigid, false positives create avoidable friction, drop-off, and support costs. If controls are too loose, authorised push payment scams and account takeover activity can move funds before anyone intervenes. NIST Cybersecurity Framework 2.0NIST Cybersecurity Framework 2.0 is useful here because it frames risk treatment as an ongoing operational discipline, not a one-time rule set.

The practical challenge is that scammers often exploit normal-looking behaviour, use mule accounts, and pressure victims into authorising transfers themselves. That means banks cannot rely only on static rules such as amount thresholds or beneficiary whitelists. They need layered decisions that combine identity signals, device trust, behavioural anomalies, and payment context. This is also where identity governance intersects with fraud operations: stronger verification is useful only when it appears at the right moment and for the right customer.

In practice, many security teams encounter the fraud pattern only after the payment has already settled, rather than through intentional pre-authorisation controls.

How It Works in Practice

The best-performing models typically use adaptive decisioning. A bank scores the payment in real time using signals such as device reputation, login age, account history, beneficiary novelty, transaction velocity, geolocation drift, and prior scam indicators. Low-risk payments flow through with little or no extra friction. Higher-risk payments can trigger step-up verification, delayed release, call-back checks, in-app warnings, or short holds for manual review.

This works best when fraud and authentication signals are combined rather than treated as separate workflows. For example, a trusted device with a long-lived session may justify lower friction for a familiar payee, while a new device, a fresh payee, and unusual timing may justify intervention even if the customer is fully authenticated. Current guidance suggests that banks should also validate the customer journey itself, because scam losses often rise when the interface creates urgency, confusion, or weak confirmation.

• Use risk-based routing so only anomalous transfers trigger stronger checks.
• Correlate identity confidence with transaction behaviour instead of using one signal alone.
• Log decision inputs for audit, dispute handling, and model tuning.
• Continuously monitor false positives, customer abandonment, and fraud escape rates.

Operationally, banks should align payment controls with fraud detection, case management, and customer communication so alerts become timely interventions rather than delayed after-the-fact reviews. For control design, CISA guidance on layered defense is directionally useful even though the use case here is financial fraud rather than ransomware. These controls tend to break down when real-time scoring is forced onto legacy payment rails that cannot support low-latency enrichment, explainable decisions, and rapid step-up actions.

Common Variations and Edge Cases

Tighter scam controls often increase friction and operational overhead, requiring organisations to balance loss reduction against customer experience and payment completion rates. That tradeoff becomes sharper in instant payment environments, where there is little time to reverse funds once the transfer is authorised. Best practice is evolving here, and there is no universal standard for how much friction is acceptable for every customer segment.

Some banks favour pre-payment controls, while others lean on post-payment recovery, beneficiary confirmation, or scam-specific warning prompts. Each approach has different strengths. Pre-payment controls reduce loss but can inconvenience legitimate users. Post-payment recovery preserves speed but is often too late to prevent harm. The most practical design is usually segmented: high-trust customers and routine transfers move quickly, while new payees, first-time devices, and unusual behavioural patterns get stronger review.

This is also where identity assurance matters. If a bank cannot distinguish a genuine customer from a coerced or compromised one, the payment layer alone will not solve the problem. For organisations handling higher-risk transfers, NIST digital identity guidance can help structure assurance and authentication decisions, while OWASP guidance on adversarial interaction patterns is relevant where AI-assisted fraud triage is used. The edge case is vulnerable-customer flows, where a legitimate payment may look risky because the customer’s behaviour changes under pressure, so banks need human review paths that are fast, consistent, and well-documented.

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