_____________________________________________________________________________________________________ Retail Fraud
refund claims and return abuse? The path forward lies in turning data and transparency into a dual advantage to both protect business margins while improving customer trust.
Leading merchants are now rethinking the trade-off between speed, trust, and security. With the right intelligence and tools, brands and retailers can automatically flag problematic returns, adjusting policies to match risk levels associated with specific regions and consumers. This reduces risk on the merchant’ s side while keeping the returns experience seamless for honest customers. To address the evolving risks, retailers should focus on leveraging both behavioral and network data to personalize their policies. This could look like offering greater flexibility to reliable customers with a history of legitimate transactions, while applying increased friction to individuals or accounts flagged for suspicious activity. By tailoring the customer experience, merchants can reward loyalty, while deterring repeat offenders who exploit return and refund policies. Additionally, transparency around return costs should be used as a behavioral lever to provide clear and upfront policy language at checkout. This informs customers whilst also encouraging more thoughtful purchasing and discouraging dishonest returns.
From blanket restrictions to nuanced policy design
As AI-generated return claims become more prevalent, it’ s essential for service teams to be trained to recognize these patterns, and for merchants to monitor for this type of activity proactively. Rather than applying blanket and outdated restrictions, retailers can apply friction selectively based on risk, using tiered approaches that match the level of scrutiny to the risk profile of each transaction. Finally, it is important to calibrate return policies by market, taking into account regional differences in return culture and fraud risk. This nuanced approach would ensure that policies remain effective and fair, regardless of geographic or demographic variations, and help retailers maintain both operational efficiency and customer trust in an increasingly complex environment.
The days of fraud being about stolen cards are fading fast. It’ s already about stolen identities, automated systems, and exploiting trust at scale. As AI agents begin to shop and interact on behalf of customers, the challenge is distinguishing between a helpful assistant and an automated fraud attack. What’ s more, in some new AI-driven checkout flows, up to a third of the data used to detect fraud is missing, making identity intelligence even more essential.
Retailers that invest in real-time, networkscale identity intelligence now will be best positioned to deliver seamless experiences for their best customers going forward while continuing to keep fraud and abuse in check. ■
For a practical example, see the Rue Gilt Groupe case study at https:// www. riskified. com / resources / video / rue-gilt-groupe
Zahava Dalin-Kaptzan www. riskified. com
Zahava Dalin-Kaptzan is Senior Product Marketing Manager at Riskified. Riskified empowers businesses to unleash ecommerce growth by outsmarting risk. Many of the world’ s biggest brands and publicly traded companies selling online rely on Riskified for guaranteed protection against chargebacks, to fight fraud and policy abuse at scale, and to improve customer retention. Developed and managed by the largest team of ecommerce risk analysts, data scientists, and researchers, Riskified’ s AI-powered fraud and risk intelligence platform analyzes the individual behind each interaction to provide real-time decisions and robust identity-based insights.
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