AI is Retailers’ Secret Weapon in the Battle Against Escalating FraudAI is Retailers’ Secret Weapon in the Battle Against Escalating Fraud

AI-driven solutions can safeguard retailers’ bottom line while still maintaining customer trust

Hilla Peled, senior vice president of data science at Riskified

October 9, 2024

4 Min Read
A keyboard with "shopping" written on the backspace key and "buy" on the enter key
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Online retailers continue to face hurdles that are impacting their profitability, including a prolonged economic slowdown, saturated markets and narrow margins. On top of these factors, bad actors are continuing to commit retail fraud and abuse at a massive scale with the help of artificial intelligence (AI). 

It isn’t news that AI has disrupted nearly every industry and aspect of business and fraud is no exception. With the rise of generative AI, we’ve seen fraud become more sophisticated and more accessible for fraudsters to scam retailers and consumers alike through the creation of deepfakes and tools such as WormGPT. 

Despite its challenges, AI has great potential to help online merchants take back control. By leveraging AI, merchants can bolster their protection against threats including policy abuse, where a user misuses merchant policies for personal gain, or friendly fraud, where a user falsely requests a chargeback for an online purchase.  

Understanding the Hurdles E-commerce Merchants Face  

To unlock the full potential AI has to offer for retail, merchants must first grasp the full spectrum of threats they face. For example, policy abuse can vary widely. Compare returning clothes after wearing them once or creating a fake email to take advantage of a promotion with criminal activities like using invisible ink on tracking IDs to fake returns. Many retailers may wave off policy abuse as a cost of doing business, but what they aren’t recognizing is that it has escalated to a billion-dollar problem, affecting businesses across the world. 

Related:Enterprise CIOs Must Avoid Legal Quicksand Around AI

The rising rate of global chargeback volume has also soared in the last few years, yet investment in chargeback management has been meager in comparison. The surge in chargebacks and complexity associated with managing disputes are costing businesses far more than just the price of reversing sales. Riskified research shows that 60% of merchants leave at least 40% of claims undisputed, while three in four recover less than half of all chargebacks. Merchants are aware that many of these are fraudulent claims, but they lack the resources and tools needed to dispute them, causing them to lose revenue. 

How Fraudsters Use AI to Accelerate These Challenges 

While policy abuse and chargebacks remain serious issues when committed by the average consumer, they are exacerbated by professional or serial fraudsters using AI, bots and other sophisticated technology to make e-commerce fraud a profitable and more mainstream business. For instance, AI-powered bots can automate the purchase of highly valued items in bulk, with the sole purpose of reselling them at a higher price on a third-party platform. Platforms like WormGPT and the dark web are making the process far more known about, simpler and quicker by offering guides and tools on how to use technology to take advantage of specific merchants and specific ways that work better from merchant to merchant, for example.

Related:How AI Enforces Consumer and Business Safety

Harnessing AI to Get Ahead of Fraudsters 

In the same way as fraudsters are using AI to target vulnerabilities, retailers should use AI to fight back as the traditional methods retailers currently have in place are no longer sufficient. For example, relying on a single data point, like an email, can be easily manipulated. Instead, leveraging advanced AI algorithms to analyze several data points enables merchants to uncover the true identities behind each transaction quickly and accurately. This way, both casual abusers who may be using multiple emails to double up on discounts and sophisticated fraud rings that are claiming multiple high-value refunds are caught.  

This holistic view also allows merchants to customize the shopping experience and determine policy decisions for each customer. That is, granting a loyal customer easier, more flexible return policies, while placing friction, like a fee, on the return of a suspicious customer.  

AI-driven solutions can also enhance the chargeback process, from all aspects of prevention to dispute handling, ultimately improving win rates and reducing inefficiencies. AI can automate the identification and categorization of claims, making it easier to keep track of the evidence merchants need to win a claim. With AI, retailers can shift from a reactive chargeback approach to a proactive strategy that safeguards their bottom line while still maintaining customer trust. 

It can be daunting to think that fraud, policy abuse and chargebacks will always pose risks as long as they exist and that fraudsters will continue to hone their craft and bypass protective measures as new technologies arise. What we must remember is that retailers have the tools they need to effectively fight back. Merchants will come out on top when they focus on using AI to fight AI. 

About the Author

Hilla Peled

senior vice president of data science at Riskified, Riskified

In the last couple of years, Hilla has held leading roles in partnering with business stakeholders across large enterprises such as Ahold-Delhaize and Citi, inspiring them to leverage technology, AI, data science and machine learning to drive their commercial success.

She has an M.Sc in distributed algorithms and reinforcement learning, years of leading teams consisting of data scientists and engineers and technology-driven lecturer experience,

Hilla is known for her ability to transform business needs/opportunities into advanced data products that demonstrate large-scale impact, supported by innovative machine learning technology. Her visionary perspective has earned her the opportunity to work for multinational companies, building long-term data strategy and driving game-changing data science projects.

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