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12/07/2026 12:26 pm
TL;DR: Fraud losses continue to rise, with American businesses losing more than $12.5 billion in 2024 and global losses nearing $50 billion annually, while roughly 80% of that loss came from attempted payment fraud, according to Sift. The real control question is no longer whether to buy a fraud platform, but how to govern identity signals, decision transparency, and friction without weakening customer trust.
NHIMG editorial — based on content published by Sift: 7 Best Fraud Prevention Software Platforms for Protecting Your Business
By the numbers:
- In 2024, American businesses lost more than $12.5 billion to fraud.
- Global fraud losses hit nearly $50 billion per year, with around 80% lost to attempted payment fraud.
- Nearly 60% of companies report that fraud losses continue to increase in 2025.
Questions worth separating out
Q: How should fraud teams use behavioural signals without adding too much customer friction?
A: Use behavioural signals to adjust friction dynamically, not to block every anomaly.
Q: Why do account takeover controls and fraud prevention need to be connected?
A: Because attackers often move from identity compromise to transaction abuse in one chain.
Q: What do security teams get wrong about fraud scoring?
A: They often treat scoring as a black box that either approves or blocks a transaction.
Practitioner guidance
- Map fraud signals into identity assurance workflows Feed device reputation, behavioural anomalies, and transaction context into account protection and adaptive authentication so fraud risk can influence access decisions in real time.
- Define explainable risk thresholds by business segment Set different decision thresholds for high-value, low-volume transactions versus high-volume, low-value flows, and document why each threshold exists for audit and customer support.
- Share telemetry between fraud and IAM teams Unify login, recovery, and purchase signals so account takeover patterns are visible across the full trust journey instead of only at checkout or only at sign-in.
What's in the full article
Sift's full article covers the operational detail this post intentionally leaves for the source:
- Platform-by-platform feature breakdowns for real-time decisioning, device intelligence, and risk scoring.
- Commercial positioning and pricing-style distinctions across the seven fraud prevention vendors.
- Vendor-specific claims about support for account protection, chargeback reduction, and API integration.
- The article's full list of practical differentiators for teams comparing fraud tools at procurement stage.
👉 Read Sift's evaluation guide for fraud prevention software platforms →
Fraud prevention software and identity signals: what teams should weigh?
Explore further
12/07/2026 12:40 pm
Fraud prevention is now an identity governance problem, not only a payments problem. The article describes a market where device signals, behavioural telemetry, and adaptive scoring decide whether a user is trusted. That means fraud teams are effectively making identity assurance decisions at runtime, often before IAM or support workflows have a chance to intervene. Practitioners should treat fraud decisioning as part of the trust architecture.
A question worth separating out:
Q: How do organisations know whether fraud prevention training is working?
A: Look for better case quality, faster escalation, fewer repeated review mistakes, and stronger correlation between verification, access, and transaction signals. If training is effective, teams should make more consistent decisions with the same evidence and spend less time re-litigating the same fraud pattern in separate functions.
👉 Read our full editorial: Fraud prevention software now hinges on identity signals and scale
