Retailers expect their customers to return some merchandise. In fact, the National Retail Federation has found that an average of 16.5 percent of retail sales in the United States are returned.
What Is Return Fraud?
Return fraud refers to customers returning merchandise to get a refund, store credit, replacement or other form of reimbursement, usually using misleading and deceptive means.
Types of return fraud include:
- Deliberate fraud: Purchasing an item with an ill-gotten credit card, then returning the item for a refund distributed in cash or to another card.
- Empty box fraud: Falsely claiming that the purchased item was not inside the packaging and asking for a refund.
- Cross-retail arbitrage: Returning an item that was purchased at a lower cost to profit off the price difference.
- Receipt fraud: Falsifying or reusing receipts to get a refund.
- Price-switching: Replacing the real price tag on an item with a higher-priced tag with intent to return the item at the higher price.
- Bricking: Buying an item, typically electronics, stripping it of valuable parts (usually to be sold later) and returning the now-defective item.
- Wardrobing: Buying an item, using it once and returning it within the retailer’s return window.
- Opportunism: Returning an item not because of a defect but because customers changed their mind or found the item at a lower price elsewhere. Opportunistic return fraud can also happen when customers list the wrong reason for a return on a return form.
Some return fraud is not performed with deceptive intent — say, returning an item purchased in person after finding it online from a different retailer at a better price, or purchasing an item on sale and unwittingly being refunded the full cost of the item upon return.
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Artificial Intelligence Can Help Detect and Prevent Return Fraud
Predictive analytics can be retailers’ secret weapon against return fraud. Artificial intelligence tools can analyze customer behavior, establishing standard, nonfraudulent patterns of use as well as fraudulent use patterns so retailers can craft policies that minimize the risk of return fraud without burdening good-faith customers. Once a behavior is identified as potentially fraudulent, retailers can direct customers who display that behavior to payment methods that minimize opportunities for return fraud.
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Analytics software can detect anomalies, including those indicative of fraud. One nonfraud example illustrates the capabilities of real-time analytics software: When a grocery store cashier accidentally entered the PLU code for avocados (4046) as the quantity of avocados, the cashier initiated a return to correct the mistake. Analytics software caught the issue, saving the retailer more than $8,000.
The amount of tax revenue loss retailers can prevent by deploying AI-tech solutions
Source: NRF, “How Artificial Intelligence Will Change Retail,” June 2023
Once AI has learned patterns of potentially fraudulent behavior, it can alert store employees in real time, enabling them to prevent abuses of customer power before they happen. In the same way, AI chatbots can be deployed for online purchases, asking questions of customers who display fraud-related behavior to detect intent or deter the sale. AI can also support employee training and education on their role in preventing return fraud.
Tech-enabled solutions have long been a part of how retailers detect return fraud, but AI automates much of that labor with great accuracy, allowing retailers to direct their personnel resources to where they will have the greatest impact.
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Tap the Long Reach of AI and Machine Learning Solutions
While predictive analytics may form the core of AI’s possibilities for reducing return fraud, AI and machine learning form a holistic solution beyond data.
Internet of Things devices can play a role in helping detect return fraud. For example, comprehensive video surveillance can help catch in-store swaps. Monitoring systems for seamless checkout setups help prevent price-switching at checkout. And connected devices designed for employee use can empower staff members to handle agile checkouts, giving them the flexibility to deploy their own knowledge about the possibilities of return fraud.
Supported by AI, retailers can help put a stop to not only their own losses but also an estimated tax revenue loss of nearly $5 billion a year, according to NRF. Investing in tech solutions supports retailers’ bottom line and the community as a whole.