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11/07/2026 8:25 pm
TL;DR: AI-driven fraud is accelerating through deepfakes, voice cloning, hyper-personalised phishing and fake platforms, with global scam losses reaching $1 trillion in 2024 and Sift reporting a 50% rise in blocked scams in Q1 2025. The governance problem is no longer detection alone, but whether trust decisions can keep pace with synthetic identity manipulation and behavioural deception.
NHIMG editorial — based on content published by Sift: AI How AI Is Fueling Online Fraud in 2025 and What Businesses Can Do About It
By the numbers:
- Global scam losses hit $1 trillion in 2024.
- Sift’s Global Data Network revealed a 50% spike in blocked scams in Q1 2025 compared to the same time last year.
- 74% of consumers surveyed said they’ve seen a noticeable uptick in scam and spam communications.
Questions worth separating out
Q: How should organisations handle AI-generated scams that mimic trusted people or brands?
A: Organisations should treat AI-generated scams as a trust orchestration problem, not only a detection problem.
Q: Why do GenAI scams create more risk than traditional phishing?
A: GenAI scams are more dangerous because they are faster to produce, easier to personalise, and more convincing across voice, text, and image channels.
Q: What do fraud teams get wrong about consumer confidence in spotting scams?
A: Fraud teams often assume awareness translates into safe behaviour, but the article shows a large gap between confidence and outcomes.
Practitioner guidance
- Strengthen step-up verification at high-risk decision points Require stronger verification when account recovery, payment changes, device changes, or unusual session behaviour appears.
- Correlate behavioural and identity signals in one risk view Combine device reputation, session history, location anomalies, payment patterns, and verification outcomes into a single decision layer.
- Review fraud thresholds for synthetic content attacks Test whether current thresholds still catch cloned voices, deepfake media, and personalised phishing that uses context from public data or prior breaches.
What's in the full article
Sift's full blog covers the operational detail this post intentionally leaves for the source:
- The Q2 2025 Digital Trust Index findings behind the fraud and consumer-behaviour trends.
- Activity IQ investigation workflows and how GenAI-assisted analysis shortens account takeover triage.
- The specific indicators Sift uses to connect fragmented signals into a single trust decision.
- The webinar context behind the consumer and fraud-team observations discussed in the post.
👉 Read Sift’s analysis of how AI is fuelling online fraud in 2025 →
AI-fueled scams and digital trust: what fraud teams need now?
Explore further
11/07/2026 8:55 pm
AI fraud is becoming an identity and trust governance problem, not just a fraud operations problem. The article shows that attackers are using GenAI to impersonate people, automate persuasion, and scale deception faster than human review can keep up. That pushes fraud prevention into the same governance territory as IAM and identity verification, because the question is now who or what can be trusted at the point of decision. Practitioners should treat trust policy as part of identity control, not an afterthought.
A question worth separating out:
Q: How do IAM and fraud teams work together on digital trust decisions?
A: IAM and fraud teams should share risk signals, escalation rules, and ownership for step-up decisions across onboarding, login, recovery, and payment flows. That coordination prevents attackers from exploiting gaps between identity proofing and transaction authorisation, where one team may assume the other has already validated trust.
👉 Read our full editorial: AI-driven fraud is widening the digital trust gap in 2025
