Topic starter
28/07/2026 7:40 pm
TL;DR: Kogan says it exceeded 98% approval rates and identified $1.5 million in annual savings after improving fraud and policy abuse management with Riskified, while keeping chargeback rates below AusPayNet thresholds. The case shows that ecommerce fraud controls now have to balance loss prevention, identity signals, and customer experience rather than optimise for fraud blocking alone.
Editorial analysis by NHI Mgmt Group, based on content published by Riskified: “Kogan Increases Approval Rates and Improves Customer Experience with Riskified”.
By the numbers:
- Kogan achieved approval rates exceeding 98% after improving fraud and policy abuse management, according to Riskified.
Key questions
Q: What fails when ecommerce approval systems focus only on fraud blocking?
A: When approval systems focus only on fraud blocking, they create false positives that reject legitimate customers and hide policy abuse behind a generic risk score.
Q: Why do behavioural and device signals matter more as fraud becomes more automated?
A: Behavioural and device signals matter because automated attacks can mimic static credentials but struggle to reproduce real interaction patterns, device characteristics, and network behaviour consistently.
Q: How can teams tell whether exec approvals are actually working?
A: They should test the exact execution path used in production, not the configuration label.
Practitioner guidance
- Tighten approval thresholds around false positives Review where good customers are being blocked because rules over-weight stolen-card patterns and under-weight behavioural context.
- Separate policy abuse from payment fraud Track promo misuse, serial returns, and subscription chargebacks as distinct abuse categories rather than folding them into a single fraud metric.
- Use richer identity and device intelligence Feed device, behavioural, and customer-level signals into the decision engine so the platform can distinguish legitimate intent from repeated abuse patterns.
Bottom line: Ecommerce fraud control is moving beyond simple blocking, because merchants now need to distinguish legitimate customers from abusive behaviour without damaging conversion.
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30/07/2026 10:56 pm
Approval optimisation is now an identity governance problem, not just a fraud problem. The article shows that merchants are no longer trying to maximise block rates alone. They are trying to make accurate trust decisions across customers, devices, behaviours, and policy-abuse patterns. That pushes ecommerce risk management closer to identity verification governance, where the quality of the signal matters as much as the severity of the loss. Practitioners should read this as a shift from static fraud blocking to intent-aware approval governance.
A question worth separating out:
Q: What is the difference between fraud prevention and policy abuse management?
A: Fraud prevention is aimed at stopping illegitimate transactions, often using payment and behavioural risk signals. Policy abuse management targets customers who exploit returns, promotions, subscriptions, or guarantees in ways that may be allowed by the payment rail but still damage margin and operations.
👉 Read our full editorial: Fraud and policy abuse controls lift ecommerce approval rates