Generative AI-Enabled Refund Fraud in Chinese E-Commerce: Investigation on Merchants and Platform Workers

2026-06-02

AI Summary

This study addresses the misuse of generative artificial intelligence to fabricate highly realistic evidence of product defects, thereby undermining the authenticity of digital evidence central to e-commerce dispute resolution mechanisms. Through semi-structured interviews with 17 merchants and 13 platform staff, the research systematically identifies four categories of novel AI-driven fraudulent tactics emerging across transaction, dispute, logistics, and communication phases. It presents the first threat taxonomy for generative AI–enabled refund fraud in e-commerce, revealing how the technology’s capacity for scalable fabrication disrupts existing trust infrastructures and exposing critical limitations in current defensive strategies at both technical and structural levels. Grounded in empirical analysis, the work proposes forward-looking design directions—including cross-platform anti-fraud databases and verifiable material anchors—to inform the development of privacy-preserving mechanisms for fraud traceability.

Abstract

E-commerce dispute resolution typically relies on the security assumption that digital evidence truthfully reflects physical reality. Generative AI (GenAI) invalidates this threat model, enabling attackers to fabricate hyper-realistic evidence of product defects at negligible cost. Through semi-structured interviews with merchants (N=17) and platform workers (N=13) in the Chinese e-commerce market, we characterize this shift toward GenAI-enabled scalable fabrication. We outline a taxonomy of four GenAI-enabled threat vectors across the transaction, dispute, logistics and communication phases, highlighting how attackers exploit GenAI to synthesize physically plausible product defects at scale. To mitigate these threats, platforms and merchants are adapting verification strategies, relying on AI tools for automated screening and adversarial interrogation (e.g., requesting multi-angle videos) to increase attack complexity. However, we find several challenges that hinder the adoption of these defenses, including implementation hurdles like structural platform constraints and fundamental limitations regarding the technical sophistication of GenAI. We conclude by outlining design implications for privacy-preserving cross-platform fraud databases, and traceability mechanisms such as embedding verifiable material anchors into the product.

Problem

• Generative AI
• Refund Fraud
• E-Commerce
• Digital Evidence Fabrication
• Security Threat

Innovation

• Generative AI
• refund fraud
• e-commerce security
• synthetic evidence
• adversarial verification