Top Challenges in Returns AI Solves

Returns are a growing problem for e-commerce businesses, costing billions annually. AI is transforming how retailers manage returns by tackling inefficiencies, fraud, and poor logistics.

Here’s what you need to know:

• Rising Costs: Returns reached $849.9 billion in 2025, with businesses losing $0.85 for every $1 of returned merchandise.
• High Return Rates: Online return rates (16.9%-19.3%) nearly double those of physical stores (9%-10%), with apparel returns exceeding 30%.
• Fraud Issues: Fraudulent returns accounted for $103 billion in 2024, or 11%-15% of all returns.
• Operational Inefficiencies: Manual processes delay refunds, increase costs, and cause inventory mismanagement.
• Customer Expectations: 82% of shoppers demand free returns, but poor return experiences push 71% to avoid buying again.

AI Solutions:

• Better Forecasting: AI predicts return patterns, improving inventory allocation and reducing stockouts or overstock.
• Faster Processing: Automates inspections, policy enforcement, and routing, cutting processing times by up to 75%.
• Fraud Detection: Identifies suspicious claims and patterns, reducing fraud-related losses by 20% or more.
• Smarter Routing: Optimizes return paths to recover maximum product value, lowering transportation costs by up to 30%.
• Increased Visibility: Real-time tracking and integration with warehouse systems improve transparency and customer satisfaction.

AI-powered tools are helping retailers save money, improve efficiency, and deliver better customer experiences while addressing the financial and logistical challenges of returns.

How retailers are using AI to stop return fraud

Challenge 1: Poor Return Forecasting and Inventory Control

Inaccurate return forecasting throws inventory systems into disarray. Traditional methods focus primarily on outbound sales, ignoring the steady flow of returned products. This oversight creates a gap where replenishment decisions are made without considering inventory stuck in the reverse pipeline. The consequences? Some locations face stockouts while others drown in excess inventory.

In 2024, returns in the U.S. surged past $890 billion, accounting for 16.9% of retail sales. During the holiday season alone, 16% of sales - around $160 billion - returned to the reverse pipeline, further complicating inventory management. Without precise forecasting, retailers often misallocate stock, over-purchase new inventory, and fail to prepare warehouse space for return surges. They also lose chances to redirect returned products to locations where they could sell faster.

"Allocation and replenishment decisions fail to account for the influx of returned items, causing stockouts for some stores and overstock situations for others." - Wendy Mackenzie, invent.ai

This lack of visibility often forces retailers to rely on outdated, one-size-fits-all approaches, such as routing all returns to a single central warehouse. These static rules create bottlenecks, inflate handling costs, and delay the process of getting products back on shelves. Every day a returned item sits in a backlog means lost revenue. By the time these products re-enter inventory, they’re often past their prime selling season, leading to steep markdowns or liquidation at a fraction of their original price.

This forecasting issue is just one of many inefficiencies plaguing modern e-commerce returns. Solving it is essential for improving the overall returns process. AI-driven analytics offer a path forward by bridging these forecasting gaps with real-time predictive insights.

How AI Uses Data Analytics for Better Forecasting

AI revolutionizes return forecasting by uncovering patterns hidden in vast datasets - patterns that are beyond human detection. Machine learning models analyze factors like purchase history, seasonal trends, localized demand, weather conditions, and promotional activity to assign SKU-level probability scores. This approach transforms returns from chaotic unpredictability into manageable patterns, identifying which products, customers, and timeframes are most likely to result in returns.

This level of forecasting operates with incredible precision, breaking down data to granular levels like SKU-Store-Week or SKU-DC-Week. With these insights, retailers can strategically position inventory where it’s most likely to sell. When a return is processed, AI determines in real time whether the item should be sent to a high-demand store, a regional distribution center, or straight to liquidation, all based on current demand signals. Studies show that companies using predictive analytics for returns see a 20% decrease in overall return rates. Additionally, AI-powered forecasting can reduce forecasting errors by up to 50%.

"Machine learning algorithms eliminate these inefficiencies by predicting return patterns, automating workflows and optimizing disposition strategies with surgical precision." - Wendy Mackenzie, invent.ai

Challenge 2: Slow Returns Processing with Frequent Errors

Handling returns manually creates a perfect storm of inefficiencies, costing businesses time, money, and customer trust. Warehouse staff often spend too much time evaluating returns, and their assessments can vary wildly - one associate might grade an item as resellable, while another deems it unsalvageable. This inconsistency comes at a steep price: about 23% of consumers requesting returns end up with incorrect replacements, and the cost to process these returns can climb to 66% of the product's original price.

The financial toll doesn't stop there. Processing an e-commerce return costs anywhere from $20.75 to $45.25 when you factor in transportation and handling. With up to 30% of online purchases ending up in the returns pipeline, these costs add up fast. Manual systems simply can't keep up with this volume, leaving returned items to pile up in backlogs where they lose value by the day.

Errors in enforcing return policies - like misinterpreting warranty terms or approving fraudulent claims - only make matters worse. Mistakes such as these led to a staggering $100 billion in fraudulent returns in 2023, accounting for 13.6% of all returns. These inefficiencies drag down profit margins and create operational headaches.

"AI can determine the best course of action for each return scenario and cut returns processing time by 75%." - Erhan Musaoglu, CEO, Logiwa

Delays in processing returns also disrupt inventory restocking. To compensate, businesses often overorder, which can lead to excess inventory and wasted resources. Add to this the errors caused by multiple handoffs and fatigued workers rushing inspections during peak seasons, and it’s clear that manual processes are falling short. Gaps in integration between warehouse systems, shipping carriers, and customer service platforms only compound the problem, creating data errors that require manual fixes.

AI-Powered Automation for Returns Processing

AI offers a lifeline by automating the returns process, addressing inefficiencies at every step. For instance, computer vision systems analyze high-resolution photos of returned items to spot scratches, dents, packaging issues, or signs of misuse like "wardrobing". These systems compare the returned items to original standards, generating objective condition scores that eliminate the inconsistencies of human judgment. This shift to automated assessments speeds up processing by 20%–40% within just 90 days.

AI-driven disposition engines take things further by calculating the best course of action for each return in mere milliseconds. These algorithms evaluate factors like product condition, resale value, processing costs, and transportation fees to decide whether an item should be resold, refurbished, recycled, or liquidated. This approach removes guesswork, ensuring businesses recover the maximum value from returns. Companies using these engines report a 10% to 25% reduction in cost-per-return by minimizing manual intervention.

"AI agents change the game by automating decisions and workflows across the returns lifecycle... The result: faster cycle times, lower cost-per-return, higher recovery on resellable goods, and a better customer experience." - Digiqt

Robotic sortation systems also play a crucial role by automating the physical handling of returned items. Using scanners and robotics, these systems sort products into categories like resell, repair, refurbish, or recycle, boosting throughput by up to 3X and cutting sorting labor by as much as 70%. This lets warehouse teams focus on more complex tasks, like handling exceptions and ensuring quality.

AI also ensures consistent and instant policy enforcement. It can verify return eligibility, warranty coverage, and return windows in seconds, eliminating the delays caused by manual reviews. AI tools also flag suspicious returns - like empty-box claims, counterfeit items, or repeat offenders - before they can be processed. Additionally, these systems provide step-by-step digital instructions to warehouse staff, ensuring uniform grading across shifts and locations. Integration with existing systems is seamless, often achieved through APIs or lightweight robotic process automation, avoiding the need for costly system overhauls.

Challenge 3: Fraudulent Returns and False Claims

Fraudulent returns are taking a hefty toll on retailers, with losses expected to hit $103 billion in 2024 alone. These returns make up roughly 11–15% of all returns, meaning one in nine returns processed by major logistics companies turns out to be fraudulent.

The methods used for return fraud are becoming more sophisticated. Common tactics include wardrobing - buying items for temporary use and returning them - and bracketing, where customers order multiple versions of a product (sizes, colors, etc.) and return most of them. More advanced schemes, like bricking, involve removing valuable components from electronics and returning the empty shell, while empty box fraud sees customers claiming they never received an item or returning boxes filled with heavy, unrelated objects to mimic the product's weight. Other scams, such as price-switching, receipt fraud, and cross-retailer arbitrage, add to the complexity of the issue.

The problem doesn't stop at direct monetary losses. Manual inspections, extra shipping costs, and the destruction of unsellable items - particularly cosmetics - often result in a 100% loss of product value. To combat fraud, retailers are forced to implement stricter return policies, like shorter windows or added fees. Unfortunately, these measures can alienate honest customers and hurt sales. Fraudulent returns contributed to over $112 billion in total retail shrinkage in 2023, according to the National Retail Federation.

"We believe that around 15% of returns are actually fraudulent or represent abuse of the system."
– Doriel Abrahams, Head of Risk, U.S., Forter

And the problem is only growing. Fraudulent returns are expected to increase by 9% over the next four years. Organized retail crime (ORC) groups are also getting more advanced, using fake documents and social engineering to exploit busy shopping seasons.

AI Algorithms for Detecting Fraud

AI is stepping up to tackle these challenges with tools that can spot patterns invisible to human inspectors. For example, Happy Returns launched its "Return Vision" system in 2024 under CEO David Sobie. This system assigns fraud risk scores by analyzing customer behavior, addresses, and the time between delivery and return requests. About 1% of returns are flagged as high-risk, and 13.5% of those flagged turn out to be fraudulent upon further inspection.

"If there's ever been fraud associated with your email address or your physical address, that can trigger a high-risk score."
– David Sobie, CEO, Happy Returns

AI-powered computer vision adds another layer of precision. In one case, AI detected that a pair of returned designer jeans was actually a cheaper knockoff by spotting subtle differences in the waistline - a detail invisible to the naked eye. This prevented a $298 fraudulent refund. In another instance, a returned orange sweater was flagged because its knit pattern and color didn’t match the original $200 item, instead aligning with a $36.99 version.

AI doesn’t just catch fraud - it can help prevent it, too. Predictive analytics identify serial returners and flag customers whose return timing seems suspiciously close to delivery. Retailers can then tailor their policies dynamically: trustworthy customers with clean histories might get instant refunds, while those flagged as high-risk may face extra verification steps, like delayed refunds or manual inspections. This approach helps safeguard profits without alienating loyal shoppers.

A real-world example of this strategy is 100% PURE, a cosmetics brand led by CEO Richard Kostick. By implementing a more hands-on approach for suspicious accounts - requiring flagged customers to cover return shipping costs - the company managed to keep its return rate at just 2% in 2024.

"There are people out there whose entire purpose in life is to find loopholes and little cracks in business processes and make them into full-blown holes."
– Doriel Abrahams, Head of Risk, U.S., Forter

Advanced tools like X-ray imaging and multi-spectrum lighting are also being used to inspect high-value electronics, allowing retailers to catch bricking attempts before issuing refunds. AI systems further improve fraud prevention by sharing data across departments like finance, fraud prevention, and customer service. This cross-channel collaboration helps identify patterns of abuse and even connects cases across different store locations to uncover organized retail crime.

These technologies don’t just reduce fraud - they also streamline the entire returns process, making it more efficient for retailers and customers alike.

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Challenge 4: Poor Routing and Recovery of Returned Products

Managing returned products is about more than just preventing fraud - it’s also about recovering value efficiently. Once a return is authorized, the journey of that product back into the supply chain becomes a major challenge. Without proper visibility into the reverse logistics network, products often take unnecessarily long and costly routes. Imagine a sweater traveling from a customer’s home to a central warehouse, then to a regional hub, and finally to a liquidation facility. Every extra step adds labor, fuel, and storage costs, delaying resale opportunities and reducing the product's worth.

This inefficiency hits low-value items particularly hard. For instance, the cost of shipping and processing a $10 T-shirt can easily surpass its resale value, turning the return into a financial loss. On top of that, manual decisions about where to send returned goods often fail to consider factors like current resale prices or regional demand. A pair of running shoes, for example, could sit idle in a warehouse for weeks instead of being sent to a city hosting a marathon, where demand is high. Meanwhile, the clock is ticking: products lose value over time. In fact, items in "brand new" condition can fetch up to 10 times more in primary or secondary markets compared to those that are damaged or dirty. The longer a product lingers in the system, the more money is lost - or worse, these perfectly usable goods might end up in landfills, contributing to waste.

This highlights the need for smarter, tech-driven solutions that can streamline reverse logistics and protect the value of returned items.

AI-Powered Decisions for Product Disposition

AI is changing the game when it comes to handling returns. It enables swift, data-driven decisions to determine the best course of action for each product. AI-powered disposition engines analyze real-time data across millions of items, taking into account factors like product condition, resale value, transportation costs, and storage fees. Based on this analysis, the system decides whether to restock the item for full-price resale, refurbish it to "like-new" condition, sell it on secondary marketplaces, harvest parts, or recycle it responsibly.

"AI disposition engines are one of the most crucial tools retailers can utilize in returns management... help workers make lightning-fast returns allocation decisions based on real-time data across millions of products."
– Yuri Yushkov, CTO, goTRG

AI also automates the inspection process using tools like computer vision and image recognition. These technologies can instantly assess an item’s condition and detect counterfeit or tampered goods without human input. Additionally, Natural Language Processing (NLP) can analyze customer feedback and return reasons to evaluate product quality even before the item arrives at the warehouse. For example, a global consumer electronics brand that adopted an AI-powered reverse logistics solution in 2025 saw a 27% reduction in return processing time and a 38% boost in recovered product value within just six months. Real-time inventory updates also led to a 15% drop in customer complaints about return delays.

AI's advanced routing algorithms further optimize the process by calculating the most cost-effective return paths - whether that’s directly to a refurbisher, back to a vendor, or to a warehouse. These algorithms consider real-time variables like carrier rates and fuel costs. For items where the cost of processing exceeds resale value, AI can trigger "customer keep" options, where customers receive an immediate refund and keep or donate the item. This approach cuts transportation costs by up to 15% and significantly reduces carbon emissions. Retailers using AI for logistics routing have reported up to a 30% reduction in transportation costs, while AI-driven disposition can slash returns processing times by 75%.

Challenge 5: Limited Visibility in Reverse Logistics

When a customer drops off a return, it often vanishes from the tracking system. Unlike the transparency of forward shipping - where you can monitor a package from the warehouse to your doorstep - reverse logistics operates in a murky space. The issue stems from network complexity: outbound shipments follow a straightforward path from a single warehouse to multiple customers, but returns flow in the opposite direction, starting with countless individual consumers and navigating through a fragmented web of carriers, processing centers, and warehouses. Each transfer increases the risk of delays, misrouting, or even lost packages.

This lack of transparency creates operational blind spots that ripple through every part of the supply chain. Without real-time tracking, businesses struggle to predict return volumes, which can lead to warehouse congestion when unexpected spikes occur. To make matters worse, traditional systems like TMS, WMS, and ERP often fail to integrate return data effectively, creating data silos. Customers face similar frustrations - they're left wondering where their return is, when they'll receive a refund, or even if their package was received. It's no surprise that 72% of customers say an easy return process is critical to their satisfaction and likelihood of making future purchases.

The financial implications are hard to ignore. Poor visibility inflates transportation costs as packages take inefficient routes, increases labor expenses, and leaves businesses vulnerable to return fraud - which made up 15.1% of all returns in 2024, costing around $104 billion. With logistics labor costs surging 30% over the past five years, relying on manual processes is no longer feasible. Fortunately, technologies like AI and IoT are stepping in to address these challenges.

Real-Time Tracking with AI and IoT

AI and IoT are revolutionizing reverse logistics by closing visibility gaps. IoT sensors and RFID technology, combined with AI systems, deliver instant updates on package location, condition, and even environmental factors like temperature and humidity during transit. For example, RFID tags and GPS tracking not only confirm when a package is received but also monitor its journey, reducing processing times by up to 90% and minimizing the chances of loss or tampering.

AI transforms raw tracking data into practical insights. Computer vision systems speed up returns processing, inspecting items up to 5 times faster than manual methods by comparing product images to standard references. AI-powered chatbots and customer portals keep customers informed with real-time updates, return authorizations, and refund timelines, cutting down on support inquiries and improving the overall experience. Additionally, AI systems automatically sync return data with WMS and ERP platforms the moment a package is scanned, giving warehouse managers instant visibility for restocking decisions.

Dynamic routing algorithms powered by AI further streamline the process by optimizing transportation routes based on live data, such as traffic conditions, fuel costs, and demand in specific regions. This ensures returned products take the fastest and most cost-effective path to their next destination. Together, these technologies are transforming reverse logistics from a black hole into a well-lit, efficient process.

Using AI with 3PL Providers like JIT Transportation

Working with an AI-driven 3PL provider can transform returns into a competitive advantage. JIT Transportation combines its nationwide network and scalable infrastructure with AI systems that manage the entire returns process - from the moment a customer initiates a return to its final resolution. These AI agents handle tasks like generating return merchandise authorizations (RMAs), creating shipping labels, and updating inventory statuses in real time across JIT's ERP and WMS platforms. This seamless integration eliminates the fragmented data issues common in traditional reverse logistics, ensuring every returned package is trackable from start to finish.

AI-powered routing takes full advantage of JIT's network, directing returns to locations where they can achieve the highest recovery value. Decisions are based on real-time factors like market demand, inventory levels, and transportation costs. For instance, a returned winter coat could be rerouted from a Florida warehouse to Minnesota, where demand for such items is higher, increasing the likelihood of a full-price resale. This approach addresses common challenges like inefficient routing and delays, highlighting AI's role in streamlining returns.

"AI can determine the best course of action for each return scenario and cut returns processing time by 75%." - Erhan Musaoglu, CEO, Logiwa

During peak seasons, AI scalability becomes crucial. Tasks like return authorizations and customer communications are handled efficiently, even during volume surges, without the need for additional staff. At receiving stations, computer vision systems automatically assess product condition and detect tampering, while AI engines decide in real time whether items should be restocked, refurbished, or liquidated based on factors like resale value and processing costs. This ensures products flow through the reverse supply chain at optimal speed.

AI also plays a critical role in fraud prevention, identifying patterns like recurring high-value returns or mismatched records. With fraudulent returns accounting for 13.6% of total return value in 2023 - over $100 billion in losses - this proactive monitoring protects both JIT and its clients from schemes like "wardrobing" and counterfeit returns.

Returns Metrics Before and After AI Implementation

The results speak for themselves. Businesses using AI-powered 3PL providers like JIT Transportation see measurable improvements across key performance indicators:

Metric	Pre-AI	Post-AI	Improvement
Processing Time	Days to weeks	Minutes to hours	40%–75% faster
Operational Costs	High labor/manual touches	Automated workflows	30% reduction
Recovery Rate	50%–60% of value	Optimized disposition	38% increase
Return Cycle Time	Multi-day/week	Automated triage/routing	20%–40% faster
Fraud Losses	13.6% of return value	Anomaly detection	20%+ reduction
Customer Satisfaction	Inconsistent/slow	Instant/transparent	25%–40% increase

These improvements have a direct financial impact. With the average e-commerce return costing between $20.75 and $45.25, a 10%–25% reduction in cost-per-return can save businesses significant amounts, especially when handling thousands of returns each month. One global consumer electronics company, for example, saw a 27% reduction in return processing time and a 38% increase in recovered product value within just a few months of implementing AI-powered reverse logistics.

Conclusion

Returns management doesn’t have to be a profit-draining headache or a source of frustration for customers. AI is stepping in to resolve five key challenges in reverse logistics: inaccurate forecasting that disrupts inventory alignment, sluggish processing that ties up resources, fraudulent returns that cost over $100 billion annually, inefficient routing that diminishes recovery value, and a lack of visibility that clouds the reverse supply chain.

The results speak for themselves - companies leveraging AI have reported processing times that are 40%–75% faster, a 30% drop in operational costs, and a 38% boost in recovered product value. On average, for every $1 in returns, businesses lose $0.85 to labor, shipping, and restocking. AI helps reduce these losses by automating tasks like return eligibility checks, optimizing product disposition, and detecting fraud.

And here’s the good news: implementing AI doesn’t mean a massive system overhaul. By partnering with JIT Transportation, you can tap into AI-powered tools right away. With a nationwide network and scalable solutions, JIT integrates seamlessly with AI systems that handle everything from generating RMAs to syncing real-time inventory updates across WMS and ERP platforms. Computer vision assesses product conditions instantly, while AI engines determine the best recovery routes - whether that’s restocking or refurbishment.

This approach not only slashes costs but also boosts operational flexibility. AI is transforming returns management from a reactive process to a proactive strategy. Retailers are now predicting return patterns at the SKU level, spotting fraudulent activity before it spirals, and turning reverse logistics into a competitive edge. With fraudulent returns accounting for 13.6% of total return value and average e-commerce returns costing between $20.75 and $45.25, the financial case for AI couldn’t be clearer.

Getting started is easier than you think. Begin small - pilot AI in one product category or facility to establish KPIs - and then scale your efforts. AI-driven returns management not only reduces costs but also strengthens customer loyalty with faster refunds, tailored exchanges, and transparent tracking. In a world where 14.5% of all retail purchases are returned, mastering reverse logistics could be your ticket to standing out.

FAQs

What data do I need to use AI for return forecasting?

To make the most of AI for return forecasting, you'll need access to critical data points like customer behavior, product specifics, purchase history, seasonal trends, market conditions, and local influences. By feeding this data into predictive analytics, you can uncover return patterns and fine-tune inventory management and disposition strategies for better efficiency.

How does AI decide whether to restock, refurbish, or liquidate a return?

AI reviews returns by assessing the item's condition, verifying its genuineness, and checking for any evidence of tampering. Based on this evaluation, it decides the next steps - whether to restock the item, send it for repairs, or move it to liquidation. This approach ensures returned products are managed efficiently and correctly.

How can AI reduce return fraud without hurting honest customers?

AI helps reduce return fraud by examining customer behavior and product details to generate fraud risk scores. This process flags potentially suspicious returns while ensuring that genuine customers aren’t negatively impacted. By identifying patterns and spotting irregularities, AI provides accurate fraud detection without interfering with the shopping experience for honest buyers.

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