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Why returns fraud should be a top priority within loss prevention

Artificial intelligence can help retailers stop returns fraud.

Organized retail crime (ORC) is on the rise across all channels.

According to the 2022 National Retail Security Survey conducted by NRF and the Loss Prevention Research Council and sponsored by Appriss Retail, 68% of surveyed retail respondents experienced higher rates of in-store fraud in recent years, while 61% and 54% indicated a rise in e-commerce fraud and omnichannel fraud, respectively.

As a result, retailers are searching for the best ways to reduce their risk. The same report found that 45% of retailers are investing in new loss prevention tools but only 10.4% have changed return policies to address ORC.

This discrepancy between those investing in loss prevention and those adjusting returns policies highlights a lack of knowledge around the potential solutions to different kinds of fraud. After all, with fraud taking many different forms, retailers often struggle with knowing where to start their counterattack.

By understanding ORC and its various forms, retailers can invest in the right tools to protect themselves. The returns process is one touchpoint that can be optimized to reduce fraudulent behavior.

Understanding trends in retail crime

When dealing with organized retail crime, retailers are up against a lot. ORC can include cybercrimes, internal theft, gift card and loyalty program fraud, returns fraud and payments fraud. And what’s more, in the 2022 National Retail Security Report, retailers detailed an increase in risk and threat priorities for all of these types of fraud over the past five years.

Despite the growing problems, the NRF report found that 92% of retailers have not adjusted their employee screening tactics, 87% have not changed point-of-sale strategies and 81% have not altered returns policies to combat fraud.

This inaction is only contributing to the rise in retail crime. Retailers must not become paralyzed by the number of ways to stop ORC, and instead choose somewhere to begin. For many, reducing fraudulent returns is an effective first step at lessening the impact of fraud, without impacting the customer journey for loyal shoppers.

Defining returns fraud

Just like organized retail crime, returns fraud can take many forms. For example, some shoppers intending to commit fraud might shoplift and then return the item for a full refund or purchase an item, use it, and return it claiming it was new. Others may falsify a receipt or place a lower price tag on the more expensive item.

Whatever the method, fraud at the point-of-sale and point-of-return can be costly, especially as return rates are growing faster than revenue rates for 91% of retailers. 

Stopping returns fraud

With so much at stake in the battle against returns fraud, it might be tempting to implement strict return policies across the board. However, most returns are legitimate and come from loyal customers. As a result, retailers should rely on artificial intelligence (AI)-driven data to predict the risk of fraud and take action before the sale occurs or at the point-of-return.

AI can review thousands of data points and automatically alert retail store associates to sales or returns that are at risk for fraud. These incidents might be tied to elaborate retail crime patterns that the store employee is not privy to. Once alerted to the risk, the store associate can be shown different types of recommendations including alternative return policies, such as marking a sale as “final” or limiting the return window during the sales motion.

During the return process, AI can also be used to make return approval, warning or denial recommendations based on historical transactions with that consumer. Overall, AI allows retailers to mitigate returns fraud without impacting the purchase or return experience for loyal shoppers.

The future of fraud detection

Organized retail crime doesn’t have to be a cost of doing business. The point-of-sale and point-of-return are just two areas where retailers can greatly benefit from data-driven tools designed to mitigate fraud. Moving forward, more retailers will rely on artificial intelligence and machine learning to guide them towards a more efficient and effective retail experience.

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