How Can AI-Driven Reverse Logistics Minimize Fraud and Personalize Returns Management
Running a retail business has never been as easy (and challenging) as it is today. While e-commerce has enabled small businesses and growing brands to reach well beyond their geographic market, intensifying online competition and the sheer number of SKU variants that need stocking up to cater to increasing consumer tastes are immense. And with rising sales comes another problem — product returns.
Though returns were always a concern, they were easier to manage before the advent of omnichannel retailing. This led to orders returning to the business in various ways, creating logistical and financial nightmares. In response, companies have attempted to meticulously measure return patterns, tracking reasons, frequency, and operational impact. However, making that data actionable has been challenging, as businesses lacked effective ways to leverage it beyond surface-level insights.
The challenge lies in two key areas. First, while patterns could be unearthed from data streams, addressing them remained largely reactive. Second, the sheer number of variables in the mix made it difficult for single-purpose systems like TMS, WMS, or ERP to adapt and optimize returns management seamlessly.
AI-powered returns management systems (RMS) are changing the game today. AI models’ ability to analyze large volumes of data—whether inventory, customer insights, or returns data—can help retailers identify specific patterns, enabling a more proactive approach to mitigating the returns problem.
Tailoring returns strategies to suit customer profiles
While online retail is adept at product recommendations based on individual consumer tastes, such customized treatment was not commonplace with the reverse logistics process. AI can be a game-changer, as AI models can now be trained to sift through consumer profiles and their return patterns and match them to their impact on business.
AI can spot all frequent customers with little to no returns, identifying them as high-value customers and giving them preferential treatment with returns, compared to habitual returners who borderline abuse return policies. AI systems can adjust policies dynamically rather than waiting for traditional analysis to cycle through systems.
Providing frequent buyers with a seamless, hassle-free returns experience will ensure they stick with the brand and help extend the customer lifetime value (CLV). Conversely, adding hurdles to buyers with high return rates can help desist unnecessary returns. This could be as simple as requiring more information before processing a return or delaying refunds until the item is verified and returned to inventory.
Besides looking at internal data, AI models can be trained to check on market competitors, automatically flagging any updates or tweaks they make to their return policies. This allows businesses to stay competitive by quickly adapting their own policies in response. The same applies to pricing strategies—if you’re in a highly competitive space, AI can track competitor price adjustments and surface insights in real-time, helping you react faster.
Inhibiting returns fraud with AI-driven detection
Product fraud is an inseparable part of returns. The National Retail Federation (NRF) estimates that over $100 billion in merchandise value was lost to fraudulent returns in 2023, accounting for 13.6% of the overall value of product returns that year. AI-based anomaly detection models can help weed out fraud at origin by identifying suspicious return patterns. For instance, AI can use historical data to flag returns from customers who have returned worn-out products.
When the model flags a potentially fraudulent return, the product must be categorized differently, requiring additional verification by warehouse inspectors. This process helps reduce the overall load on inspectors and ensures fewer fraudulent items slip through.
AI also enhances fraud detection by leveraging image recognition. Computer vision plays a key role here, helping compare customer-uploaded photos of returned items against authentic product images to identify counterfeit or tampered goods. This can happen before the return is accepted or during returns processing, giving warehouse staff extra tools to verify legitimacy.
AI offers two key benefits here: efficiency and effectiveness. Quick verification and automated processing of refunds to high-value customers eliminate unnecessary manual checks, while technology like computer vision enables an effective reviewing process.
Dynamic returns routing across inventory networks
For a retailer running nationwide operations, stocking the ‘right amount’ of inventory across every warehouse is an ever-complex balancing act. Orders returning to inventory make this equation more complicated.
AI models help clear up the haze, routing returns based on factors like pricing, sell-through rates, and market demand in different regions. AI models can also manage inventory by identifying products needed to fulfill incoming orders. For example, a retailer selling running shoes might reroute inventory to a city hosting a marathon, where demand is expected to surge.
In situations where the return is not economical, such as when a $10 T-shirt is in for return, AI can identify that it’s more cost-effective to credit the customer and have them keep or donate the item rather than going through the entire return process. This is also known as a beyond-economic return.
In the future, AI can also be expected to go beyond making linear decisions and predict future outcomes based on historical patterns, such as estimating when a returned product will be resold after being added to the inventory. This will help clarify restocking and product routing.
For example, if a retailer launches a new product that sees strong initial sales but is later met with a high rate of returns, AI can recognize this trend early and flag it as a potential misstep. Instead of restocking aggressively based on early sales figures, the system can recommend adjusting inventory levels or investigating the reasons behind the returns — a product defect, misleading marketing, or a mismatch with customer expectations, preventing overstocking and optimizing purchasing decisions.
Considering we are still in the nascent stages of AI, this technology will be leveraged more as it’s proven and tested. As AI evolves and proves its value, it will streamline returns, optimize inventory, and free up human efforts for more strategic, high-impact tasks — turning returns into a competitive advantage.
