The rise of AI-generated damage claims: What retailers need to know

The retail industry faces a growing challenge: artificial intelligence (AI) is now being used to create highly realistic, fraudulent damage claims.

Generative AI tools are readily available online, allowing anyone to manipulate images of products and receipts with minimal effort. According to some studies, more than one in 10 returns may be fraudulent even without AI assistance, and the use of AI is only increasing this figure, threatening commercial viability.

How AI-generated damage claims work

One common example includes a situation where fraudsters purchase goods, use AI to fabricate damage in photos, and submit these as evidence to claim refunds or replacements. The technology can create convincing images from any angle and with consistent defects, making manual detection exceptionally difficult.

This problem affects all areas of retail, including online marketplaces, accommodation services, and traditional retailers. Second-hand marketplaces are especially vulnerable to this kind of fraud given that goods are already used by default. 

Why retailers are vulnerable

Generous return and refund policies, intended to boost customer loyalty, have inadvertently made it easier for fraudsters to exploit retailers.

Automated refund systems often issue refunds before goods are inspected, leaving businesses exposed. Platforms generally trust buyer-submitted images, with sellers given few opportunities to contest suspicious claims.

Some buyers may not even consider that AI-assisted fraud can amount to a criminal offence. 

Actionable steps for retailers

There are some actions retailers can take to reduce their exposure to fraudulent, AI-generated damage claims. However, none of these measures alone is likely to address the underlying issues; a multi-tiered vetting mechanism may be required.

Moreover, retailers should consider the trade-off between increased certainty and customer perception. Generous return policies are common and customers are more likely to shop around if returns policies become too burdensome. 

Here are some examples of possible detection and prevention methods:

• Require customers to submit evidence of the damage from multiple angles, or to record a video of the damage. 

• Implement multi-layered fraud detection systems, combining automated AI analysis with human review before any refund is issued.

• Train staff to recognise AI-generated content and escalate suspicious claims appropriately. 

• Update return and refund policies to include safeguards without alienating genuine customers (e.g., requiring goods to be returned before issuing refunds).

• Invest in technology that verifies image authenticity, checks metadata, and detects deepfakes.

• Monitor regulatory developments, particularly the EU AI Act where applicable, as well as standards for AI-generated evidence in legal proceedings.

Conclusion

The convergence of AI-enabled fraud and customer-friendly policies has created a perfect storm for the retail sector. Proactive, risk-based strategies are essential to protect against evolving threats, maintain regulatory compliance and preserve customer trust. The cost of inaction is high – retailers must act fast to safeguard their businesses.

Our retail sector specialists can support with tailored advice on fraud prevention, policy updates, or navigating the legal landscape surrounding AI-generated claims.