Topic starter
11/07/2026 1:58 pm
TL;DR: Fraud controls that rely on fixed rules can create friction and false declines, according to Signifyd. Merchants need adaptive decisioning that keeps legitimate customers moving while still separating fraud from normal shopping behaviour.
Editorial analysis by NHI Mgmt Group, based on content published by Signifyd: “CX Fraud Prevention: Balancing Risk and Customer Experience”.
By the numbers:
- According to Resolve, seven out of 10 customers abandon their carts without placing an order.
Key questions
Q: Why do AI and machine learning improve customer experience in fraud prevention?
A: AI and machine learning improve CX because they evaluate more signals in real time and can separate legitimate variation from suspicious behaviour more accurately than fixed rules.
Q: How can security and fraud teams tell whether their controls are failing?
A: Look for two signals at once: rising false positives that push real customers away and rising fraud leakage that appears only after chargeback analysis.
Q: What breaks when fraud controls are too strict in ecommerce?
A: Retailers start blocking good customers, increasing support load, reducing repeat purchase rates and damaging lifetime value.
Practitioner guidance
- Define fraud controls against conversion outcomes Measure false declines, checkout abandonment and review latency alongside fraud loss so security decisions are judged against customer impact as well as loss reduction.
- Replace brittle rule stacks with multi-signal scoring Use behavioural, device, transaction and historical signals together so the decision engine can distinguish suspicious activity from legitimate customer variation.
- Use step-up authentication only where risk warrants it Apply MFA or biometric-style verification selectively, because extra checks can protect trust in high-risk flows but can also create avoidable friction.
Bottom line: CX fraud prevention works best when fraud teams measure security and conversion together, not as opposing goals.
Glossary terms: False decline, Account Takeover, Risk Scoring Model, Behavioural Signal
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This topic was modified 4 hours ago by NHI Mgmt Group
11/07/2026 2:07 pm
CX fraud prevention is an identity governance problem as much as a fraud problem. The article makes clear that merchants are not only trying to stop bad transactions, they are trying to preserve trust in legitimate customer access. That means identity proofing, authentication strength and risk scoring should be judged against the customer journey, not in isolation. Practitioners should treat conversion impact as a control outcome, not a side effect.
A few things that frame the scale:
- Nearly 60% of companies reported that fraud losses were still increasing in 2025.
A question worth separating out:
Q: When should organisations add step-up authentication during a session?
A: They should add step-up authentication when the action is higher risk than the original login, such as privileged resource access, unusual device changes, or a suspicious location shift. That approach limits unnecessary friction while still reacting when the session no longer looks trustworthy.
👉 Read our full editorial: CX fraud prevention shows why security and conversion are linked