Editorial
Key Takeaways
- Retailers lost $103 billion to fraudulent returns in 2024, accounting for 15% of total returns worth $685 billion.
- AI helps combat returns fraud through predictive analysis of transaction patterns and generative AI tools that connect organized retail crime cases.
- Clean data and human oversight are essential for retailers to successfully implement AI-powered fraud detection in their returns processes.
Predictive AI — also known as machine learning — and generative AI capabilities are influencing practically every part of a retail organization, but a growing interest is how these technologies can improve returns processes and prevent fraud.
In an economy challenged by tighter consumer spending, global tariffs and product inventory potentially stuck at sea, retailers are seeking ways to save money in places they can control. Combatting returns fraud, improving returns logistics and reducing shrink are top of mind.
And while AI experts like Sam Altman trumpet the power of AI agents to join company workforces, the truth is many companies aren’t that far along with AI. A McKinsey study found that just 1% of companies investing in AI feel that they are AI mature. Many consumer-facing organizations continue to see the potential of AI, but the next step for all companies is to turn AI into action. For retailers, that action could start internally with returns, which is a huge area of concern.
Returns Fraud Hobbles Retailers
In 2024, retailers lost $103 billion due to fraudulent returns and claims, according to retailer transaction data from Appriss Retail and Deloitte, accounting for roughly 15% of an overall $685 billion in returns last year. Adding to the dilemma, online returns are on the rise.
The study showed that Buy Online Return In-Store (BORIS) activity accounted for 14.58% of total returns and Buy Online Return Online (BORO) resulted in 9.94% of total returns. Combined the online returns resulted in 24.52% of online sales, while in-store returns represented 8.72% of brick-and-mortar sales. But not all is lost.
AI can assist retailers by providing a more efficient and faster way of detecting potential incidents of fraudulent returns. For example, AI embedded into sales and returns systems can monitor transactions as they occur online and in-store, flagging suspicious activity for retail staff to address. Incidents can vary, too, so AI helps retailers watch for all types of unusual behavior. Typical types of returns fraud and abuse include:
- Wardrobing: Buying an item to use it once and then return it, also known as renting.
- Bracketing: Ordering multiple styles of a product and returning most or all of them after use.
- Receipt fraud: Using stolen, forged, or reused receipts to return goods never purchased.
- Price switching: Swapping price tags, so a high-priced item fraudulently gets sold for less.
- Return of stolen goods: Shoplifting items and returning them for store credit or cash.
- Fake defect claims: Claiming items are defective, when they aren’t, and getting a refund.
Unfortunately, consumers and organized retail crime (ORC) groups continue to get smarter when it comes to taking advantage of a retailer’s returns process, so there are even more scenarios of fraud and abuse likely to come. However, retailers can be prepared for what’s next with the help of predictive AI and generative AI.
Related Article: 5 AI Case Studies in Retail
How AI Helps Detect Fraud
To fight fraud and theft, AI can support retailers and loss prevention teams in different ways. Specifically, there are differences in how generative AI and predictive AI work to detect fraud.
Predictive AI is essential in helping retailers analyze transactions, such as reviewing a shopper’s historical data and looking for anomalies in a transaction, such as a purchase with multiple addresses or repeated returns of all purchased items from an order. The predictive AI approach can carefully analyze and recommend to a retail agent if a return should be processed or not.
Generative AI, on the other hand, works best to help loss prevention teams develop ideas and automate certain tasks. For instance, loss prevention investigators can leverage generative AI to scan a database of internal incident reports and connect cases that could lead to cornering ORC groups processing fraudulent returns. The generative AI can spot if similar weapons were used in different incidents, or if there are coincidences in locations and items stolen between cases, and more.
Generative AI can also empower natural language queries for loss prevention teams to use the technology as a co-pilot, removing any technical expertise and making it easier for loss prevention specialists to extract insights from their transaction data. The AI also helps teams auto-generate structured query language requests, saves searches, filters data and helps teams conduct case work faster.
Combined, predictive and generative AI technologies provide critical omnichannel support, as loss prevention and logistical teams work to uncover cases of returns fraud and reduce returns overall.
Related Article: 10 Top AI Retail Products
Retailers Ramp Up Fraud Detection With AI
Of course, for retailers to get the most out of generative AI and predictive AI, they need to ensure clean, unified data is flowing throughout their retail systems. They also need to ensure managers are in place overseeing how the AI manages each returns transaction.
As AI touches all aspects of a retailer’s business, and companies continue testing the technology to improve business outcomes, there are clear benefits to starting with returns.
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Having a streamlined returns system in place, working in concert with unified data, predictive AI and generative AI can optimize how retailers manage returns. They can prevent cases of fraud and abuse, and ultimately, save money during a tumultuous time where being fiscally responsible matters more than ever. AI is getting stronger. As a result, so are retailers.
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