Riskified, a provider of e-commerce fraud and risk intelligence solutions, has announced the results of its partnership with Kogan.com, one of Australia's largest online retailers, demonstrating how artificial intelligence is reshaping fraud prevention and customer approval strategies for enterprise e-commerce businesses.

According to the companies, Kogan achieved approval rates exceeding 98% after deploying Riskified's AI-powered fraud detection platform while also identifying approximately $1.5 million in annual savings through better management of fraud, promotional abuse, and policy misuse.

For digital retailers, fraud prevention has become significantly more complex as online transaction volumes increase and fraud tactics become increasingly sophisticated. At the same time, overly restrictive fraud controls can unintentionally decline legitimate purchases, creating revenue loss and customer dissatisfaction.

Kogan, which serves more than 3.5 million customers across Australia and New Zealand through brands including Kogan.com, Dick Smith, Mighty Ape, Matt Blatt, and Brosa, operates entirely online. Every purchase is processed as a card-not-present transaction—a category traditionally associated with higher fraud risk than in-store payments.

Rather than focusing solely on blocking fraudulent transactions, the retailer sought a solution capable of accurately distinguishing legitimate shoppers from fraudulent actors while maintaining a seamless purchasing experience.

AI Moves Fraud Prevention Beyond Chargebacks

Riskified was selected for its Chargeback Guarantee model and AI-driven fraud decision engine, alongside identity intelligence capabilities designed to analyze customer behavior across multiple transactions.

The platform combines machine learning, behavioral analytics, device intelligence, and identity-based signals to evaluate transaction risk in real time. This approach enables retailers to identify fraudulent activity with greater precision while minimizing false positives that prevent genuine customers from completing purchases.

The partnership also addressed policy abuse—an increasingly significant challenge for enterprise retailers. Unlike traditional payment fraud, policy abuse includes activities such as promotional misuse, serial return fraud, subscription chargebacks, and repeated exploitation of retailer policies.

These behaviors often remain undetected by conventional fraud tools despite creating measurable financial losses.

By implementing Riskified's Identity Engine and Identity Explore capabilities, Kogan gained deeper visibility into repeat offenders and customer-level purchasing behavior. According to the company, the additional intelligence enabled teams to identify serial policy abusers more effectively while approving a greater number of legitimate transactions.

Automation Reduces Operational Complexity

Beyond fraud detection, the collaboration also focused on improving operational efficiency.

Managing chargebacks manually requires substantial resources for evidence collection, dispute handling, and case management. Riskified's automated dispute management capabilities reduced manual review requirements while helping Kogan maintain chargeback rates below AusPayNet thresholds.

Machine learning models continuously trained using Kogan's transaction data also enabled more accurate decisions involving first-time buyers and higher-value purchases—two categories that often present greater fraud uncertainty for e-commerce merchants.

For enterprise retailers managing millions of transactions annually, reducing unnecessary manual reviews not only lowers operational costs but also accelerates order fulfillment and enhances customer satisfaction.

Why Identity Intelligence Matters for Modern Ecommerce

Identity intelligence is emerging as one of the fastest-growing areas within e-commerce risk management. Instead of evaluating transactions independently, identity-based systems analyze long-term behavioral patterns across customers, devices, payment methods, and purchasing histories.

This broader context allows AI models to recognize trusted customers more accurately while identifying sophisticated fraud networks that may appear legitimate during isolated transactions.

According to Juniper Research, global e-commerce fraud losses are projected to continue rising as digital commerce expands, driving increased investment in AI-powered fraud detection technologies. Meanwhile, Gartner has identified AI and machine learning as critical technologies enabling enterprises to improve fraud detection accuracy while reducing friction in digital customer experiences.

For retailers, this represents an important shift in fraud management strategy. Success is no longer measured only by preventing fraudulent transactions but also by maximizing legitimate approvals, protecting customer loyalty, and improving lifetime customer value.

Enterprise Impact

The Kogan-Riskified partnership illustrates how fraud prevention is evolving into a broader customer intelligence capability.

As e-commerce businesses expand across digital channels, balancing fraud protection with frictionless customer experiences has become a competitive differentiator. AI-powered identity intelligence enables retailers to make faster, more accurate decisions while reducing revenue losses associated with false declines, policy abuse, and operational inefficiencies.

For enterprise marketing and e-commerce teams, richer customer intelligence also supports personalization, customer retention, and profitability analysis by providing deeper visibility into customer behavior across the buying journey.

As digital commerce continues to grow, integrated fraud intelligence platforms are expected to become a foundational component of modern ecommerce infrastructure, helping retailers protect revenue while delivering seamless shopping experiences.

Market Landscape

AI-powered fraud prevention has become a strategic investment for enterprise e-commerce organizations as online transaction volumes continue to grow. Retailers are increasingly adopting machine learning, behavioral analytics, identity intelligence, and automation to reduce fraud while improving customer experience. At the same time, policy abuse—including return fraud, promotional misuse, and account exploitation—is emerging as a major profitability challenge beyond traditional payment fraud.

Industry analysts expect continued investment in intelligent fraud prevention platforms that integrate real-time risk analysis with customer identity intelligence, enabling retailers to improve approval rates, reduce operational costs, and strengthen long-term customer relationships.

Top Insights

• Kogan achieved approval rates above 98% after implementing Riskified's AI-powered fraud detection and identity intelligence platform across its e-commerce operations.
• The partnership identified approximately $1.5 million in annual savings by reducing fraud, promotional misuse, and serial policy abuse without increasing operational complexity.
• AI-driven identity intelligence enabled Kogan to approve more legitimate customers while reducing false declines and maintaining low chargeback rates.
• Automated dispute management reduced manual chargeback workloads, allowing internal teams to focus on higher-value operational priorities.
• Enterprise retailers are increasingly combining fraud prevention with customer intelligence to improve profitability, customer experience, and long-term digital commerce performance.

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