Fraud and policy abuse controls: what improves approval rates?

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.

NHIMG editorial — 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% and identified $1.5 million in annual savings through improved management of fraud and policy abuse.

Questions worth separating out

Q: How should ecommerce teams balance fraud prevention with approval rates?

A: Treat fraud prevention as a decision-quality problem, not a blocking problem.

Q: Why do policy abuse and fraud need different controls?

A: Because they create different kinds of loss and are often revealed through different patterns.

Q: What signals indicate that fraud controls are over-blocking good customers?

A: Watch for declining approval rates in specific segments, rising manual review volume, and strong chargeback suppression that comes with conversion loss.

Practitioner guidance

• Correlate customer intent across sessions Link device fingerprints, behavioural patterns, and account history so repeat policy abusers and promo misusers are visible as a single risk pattern rather than isolated transactions.
• Separate fraud from policy abuse in control design Define distinct handling paths for fraudulent orders, serial promo misuse, and subscription chargebacks so review thresholds reflect the actual loss type and do not over-block legitimate customers.
• Measure approval quality alongside loss rate Track approval rate, false positives, chargeback outcomes, and repeat abuse together so the team can see whether controls are improving revenue capture or merely shifting risk.

What's in the full analysis

Riskified's full case study covers the operational detail this post intentionally leaves for the source:

• Decision model details for Kogan's higher-value and first-time customer categories
• How Identity Engine and Identity Explore were used to separate policy abuse from legitimate repeat behaviour
• The automated dispute workflow and chargeback handling model behind the reported savings
• The merchant-facing operational changes that helped keep chargeback rates below AusPayNet thresholds

👉 Read Riskified's case study on Kogan's approval rate and fraud controls →

Fraud and policy abuse controls: what improves approval rates?

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