Data-driven insights revealing why post-purchase automation is now essential for combating the multi-billion-dollar returns fraud crisis

 

Returns fraud has become one of the most pressing challenges facing ecommerce brands today. With total U.S. retail returns estimated at $849.9 billion for 2025 and fraudulent returns accounting for billions in losses, brands are scrambling to find solutions that balance fraud prevention with customer experience. One structural constraint is write access: many AI CX platforms can take actions exposed by connected commerce APIs but hit limits when the underlying system does not provide the write endpoints needed to complete a workflow. This is where agentic post-purchase platforms that combine intelligence with transaction rails become critical for closing the gap between fraud detection and fraud prevention.

 

Key Takeaways

• Return fraud estimates vary by year and methodology. 9% of returns were estimated to be fraudulent in 2025, while a separate 2024 analysis estimated return and claims fraud and abuse at 15.14% of returns, or about $103 billion
• Abusive returns surged 64% in 16 months. The rapid acceleration of policy abuse between 2024 and 2025 signals an urgent need for automated enforcement
• 85% of retailers use AI, but only 45% find it effective. The gap between AI adoption and effectiveness points to fundamental limitations in detection-only approaches. Kodif’s returns and exchanges automation connects AI intelligence with transaction execution for eligible post-purchase workflows
• 71% of consumers are less likely to shop with a retailer again after a bad return experience. Brands must balance fraud prevention with customer retention priorities, making intelligent automation essential
• Online return rates can be 3x higher than in-store. One analysis found a 15.2% online return rate versus 5% in-store, creating greater returns exposure for ecommerce brands

 

Understanding Return Fraud: The Scale of the Ecommerce Crisis

The returns landscape has fundamentally shifted. What was once a manageable operational cost has become a strategic threat to ecommerce profitability. These statistics reveal the true scope of the challenge.

 

1. U.S. retail returns were estimated at $849.9 billion for 2025

The sheer volume of merchandise flowing back to retailers represents a 15.8% overall return rate. This creates enormous operational overhead and opens countless opportunities for fraudulent actors to exploit gaps in return processes.

 

2. Return fraud accounted for approximately $76 billion in losses in 2025

Fraudulent returns were estimated at 9% of return volume in 2025, extracting billions from retailer margins. The source cautions that this estimate is not directly comparable with the higher 2024 figure because the methodology and return base changed.

 

3. Return fraud and claims reached $103 billion in 2024

A separate 2024 analysis estimated fraudulent and abusive returns and claims at 15.14% of total returns. Because the 2024 and 2025 estimates use different data and methodologies, they should not be treated as a direct year-over-year comparison.

 

4. Ecommerce return rates average 19.3% versus 15.8% all-channel

Online purchases were estimated to have a 19.3% return rate, compared with a 15.8% all-channel return rate. This disparity creates greater returns exposure for ecommerce-heavy brands.

 

5. Online returns run roughly 21% higher than overall return rates

Ecommerce-specific return rates run roughly 21% higher than a retailer’s overall return rate, increasing the operational burden for digital-first brands.

 

6. Omnichannel return fraud represents a $19 billion profitability leak

The complexity of buy-online-return-in-store and similar omnichannel workflows creates massive exploitation opportunities that fraudsters actively target. Without unified policy enforcement across channels, brands hemorrhage value.

 

The Acceleration of Fraud: Why Traditional Approaches Are Failing

Fraud is not just growing; it is accelerating. These statistics demonstrate why reactive, detection-only approaches cannot keep pace with evolving tactics.

 

7. Abusive returns surged 64% between January 2024 and May 2025

The rapid escalation of abuse in just 16 months signals a fundamental shift in consumer behavior. What was once occasional opportunism has become systematic exploitation.

 

8. 71% of retailers reported an increase in overstated-quantity returns

Customers increasingly claim they received fewer items than shipped, forcing retailers to choose between taking the loss or friction with legitimate customers. Automated verification can address this at scale.

 

9. 65% of retailers reported a rise in empty-box or “box of rocks” returns

The empty box scam has become disturbingly common, with customers returning packages containing worthless substitutes while claiming full refunds. Manual inspection at scale is economically unfeasible.

 

10. 64% of retailers saw a rise in decoy and counterfeit returns

Fraudsters now return counterfeit versions of products while keeping authentic items, requiring sophisticated verification that most return processes cannot provide.

 

11. Empty boxes accounted for 31% of return fraud indicators in 2024

Nearly one-third of identified fraud incidents involved completely empty return shipments, highlighting the brazenness of modern return abuse.

 

12. Card testing fraud increased 65% between Q2 2024 and Q2 2025

Beyond returns, card testing attacks that precede larger fraud schemes accelerated dramatically, signaling broader criminal interest in ecommerce exploitation.

 

13. Overall fraud pressure increased 13% by value in 2025

The aggregate fraud burden on retailers grew substantially, with criminals extracting higher-value items and using more sophisticated techniques.

 

The AI Effectiveness Gap: Why Detection Alone Falls Short

Most retailers have adopted AI fraud tools, but effectiveness remains elusive. The data reveals a critical gap between identifying fraud and preventing it.

 

14. In 2025, 85% of retailers said they used AI to detect or prevent return fraud

AI adoption for fraud management has become nearly universal among retailers, yet fraud continues to grow. Kodif’s view is that the problem is not simply AI quality but AI architecture.

 

15. In 2025, only 45% of retailers said AI fraud detection tools were effective

Despite widespread adoption, fewer than half reported meaningful results from their AI investments. Detection without execution leaves too many gaps for fraud to slip through.

 

16. In 2024, 84% of retail executives said their companies had changed return policies in the prior year

The Appriss Retail analysis found that 84% had changed policies to combat fraud. The same research found that restrictive return policies can affect customer purchasing behavior, highlighting the need to balance fraud controls with customer experience.

 

17. In 2025, 64% of merchants said updating their returns process within six months was a priority

In the 2025 NRF and Happy Returns survey, 64% of merchants said updating their returns process in the next six months was a priority.

 

This is precisely where Kodif’s approach differs. Kodif frames roughly 35-40% as the market-average automation ceiling for API-layer AI CX platforms, with write access as a key architectural constraint on end-to-end post-purchase automation. 

 

Kodif’s returns and exchanges automation combines the intelligence layer with transaction rails, enabling the AI to actually enforce policies within the customer conversation rather than simply detecting violations.

 

18. Nearly three-quarters of retailers charged for at least some returns in 2025

In the 2025 data, 72% of retailers charged for at least some returns, up from 66% the previous year. The increase shows how more retailers were introducing return fees as they reassessed the cost of reverse logistics and return processing.

 

Consumer Behavior: The Human Side of Return Fraud

Understanding why customers commit fraud reveals opportunities for prevention through better experience design and automated policy enforcement.

 

19. 82% of shoppers cite free returns as a major factor in purchase decisions

The customer expectation for free returns creates a strategic dilemma: absorb fraud losses or risk conversion. Intelligent automation offers a path to protect margins while preserving experience.

 

20. 45% of consumers believe “bending the truth” is acceptable when making returns

The survey found that 45% of consumers believe “bending the truth” can be acceptable when making returns. This measures attitudes toward questionable return behavior, not whether consumers consider return fraud itself socially acceptable.

 

21. Gen Z consumers averaged 7.7 online returns over a 12-month period

The highest-returning generation treats returns as a standard part of shopping, creating both legitimate high-volume returns and ample cover for fraudulent behavior.

 

22. 51% of Gen Z shoppers say they bracket purchases regularly

More than half of younger consumers intentionally over-order with plans to return most items. While not technically fraud, bracketing strains operations and obscures actual fraudulent activity.

 

23. In 2024, 60% of retail executives identified wardrobing as a significant type of return fraud

The practice of purchasing items, wearing them, and returning them remains a major concern for apparel brands. Automated policy enforcement can identify patterns that suggest wardrobing behavior.

 

24. 30% of shoppers who admit to wardrobing do so weekly

Among those who engage in wear-and-return behavior, nearly a third do it regularly, treating retailers as free closet rentals. This serial behavior is detectable through pattern analysis.

 

25. Nearly 4 in 10 online shoppers said they or someone they know engaged in returns abuse or fraud in the past year

Nearly 4 in 10 online shoppers said they or someone they know engaged in returns abuse or fraud in the past year. This includes both personal and secondhand reporting, so it should not be treated as a 40% self-admission rate.

 

26. 81% of consumers review return policies before purchasing

Customers actively evaluate return terms as a buying criterion, making policy clarity essential. Plain-English policies that AI can consistently enforce help set appropriate expectations.

 

Customer Experience Impact: The High Cost of Getting It Wrong

Fraud prevention cannot come at the expense of customer loyalty. These statistics reveal the stakes of balancing protection with experience.

 

27. 71% of consumers say a negative return experience would discourage repeat purchases

The loyalty impact of poor returns is severe. Brands cannot afford heavy-handed fraud prevention that alienates legitimate customers.

 

28. 65% would stop buying from a merchant based on a bad return experience

A separate study found similar loyalty destruction from negative return interactions, reinforcing the critical need to balance fraud prevention with customer experience.

 

29. 62% said they would buy more from a brand based on a good return experience

Conversely, positive return experiences drive revenue. Brands that automate returns effectively can turn a cost center into a competitive advantage.

 

30. Four out of five consumers would share a negative return experience with friends and family

The 2025 NRF and Happy Returns research found that four out of five consumers would share a negative return experience, extending the impact of poor returns beyond a single transaction.

 

31. In 2024, 55% of consumers said restrictive return policies had stopped them from buying from a retailer

Overly aggressive fraud prevention actively costs sales. The right approach uses intelligent automation to enforce policies precisely, not restrictive rules that punish everyone.

 

32. Among retailers that introduced return fees, 47% reported more customer complaints

The fee-based approach to fraud reduction created significant backlash, with nearly half seeing complaint volume increase alongside lost customers and reduced order values.

 

33. 71% of shoppers would stop buying online from retailers charging return shipping fees

Customer resistance to return fees is substantial. Rather than charging for returns, brands should invest in automation that identifies and addresses fraud while keeping legitimate returns friction-free.

 

This is where AI-powered customer support transforms the equation. By automating eligible returns and exchanges directly within the customer conversation, Kodif’s Resolution Agent can evaluate requests against policy and execute supported post-purchase actions without requiring a separate portal or manual transaction step. Approximately 97% of shipping protection claims are approved, meaning most claims involve workflows around outcomes unlikely to be disputed. Separate claims portals often create unnecessary friction.

 

The Cost of Returns Processing: Operational Realities

Beyond fraud losses, the basic economics of return processing create significant margin pressure that automation can address.

 

34. 40% of retailers cite rising return-processing costs as a reason for charging return fees

The 2025 NRF survey found that 40% of retailers cited increased return-processing costs as a reason for charging for returns, alongside higher carrier shipping costs.

 

35. Handling a return costs about $20–30 on average

Handling a single return costs about US$20–30 on average once shipping, inspection, restocking, and support are included. Automating the support component can reduce part of this operational burden.

 

36. 35% of surveyed retailers had implemented real-time return technology

The 2024 Appriss Retail and Deloitte report found that 35% of retailers had implemented real-time technology to approve, warn, or deny a return or claim based on behavior patterns.

 

37. Close to two-thirds of consumers admit to at least one costly returns behavior

The 2025 NRF research found that close to two-thirds of consumers admitted to at least one costly returns behavior, including wardrobing, bracketing, sending back different items, or empty boxes. These behaviors should not all be treated as equivalent to fraud.

 

38. Claims and appeasement fraud and abuse were estimated at $21 billion in 2024

Beyond return fraud itself, claims fraud in which customers falsely report missing or damaged items represents another major category. Kodif’s delivery claims automation can resolve eligible claims within the conversation while identifying patterns that suggest abuse.

 

Category-Specific Fraud Exposure: Where the Risk Concentrates

Different product categories face varying fraud exposure, helping brands prioritize automation investments.

 

39. Online apparel returns average 22% versus 6.2% in-store

ICSC data summarized by WhiteBox found that 22% of apparel bought online was returned versus 6.2% in-store. High legitimate return volumes can make abusive behavior harder to distinguish from ordinary returns.

 

40. Poor fit is cited in 50% of online returns

Research summarized by WhiteBox found that 50% of consumers cited fit among the most common reasons for returning online purchases, alongside damaged items, unmet expectations, and incorrect items.

 

41. 87% of apparel shoppers who buy online intentionally overbuy to try items at home

The bracket shopping phenomenon is deeply embedded in fashion ecommerce behavior, creating a baseline of high-return customers that complicates fraud identification.

 

42. Buy Online Return In-Store and Buy Online Return Online combined accounted for over 52% of all returns in 2024

The omnichannel return mix creates complexity that fraudsters exploit. Unified policy enforcement across all channels requires automation that can execute transactions regardless of origin point.

 

Why the Automation Ceiling Matters for Fraud Prevention

The statistics show a gap between AI adoption and reported effectiveness. Kodif’s view is that one contributor is architectural: end-to-end automation depends on having the write access required to execute post-purchase actions.

 

• API limitations can restrict automation: Many AI CX platforms sit above third-party commerce systems and can execute the actions those systems expose through APIs, but they can hit limits when the underlying platform does not provide the write endpoints needed to complete a workflow.
• Missing write access can force human handoffs: When an action cannot be executed through the connected system, the workflow may require a human handoff. This creates delays that frustrate legitimate customers and gaps that sophisticated fraudsters exploit.
• Kodif views the automation ceiling as architectural: Kodif’s position is that this automation ceiling is architectural, not simply a model-quality problem. The market average for API-layer AI CX platforms is roughly 35-40% end-to-end automation.
• Kodif combines intelligence with transaction access: Kodif’s post-purchase-native architecture, which combines the intelligence layer with transaction rails and write access, has achieved 60%+ end-to-end email automation for brands focused on post-purchase workflows.
• Fraud prevention requires action, not just detection: An AI that can identify a suspicious return pattern and immediately apply the appropriate policy response within the same conversation closes the loop that detection-only systems leave open.
• Policies can be applied directly within workflows: The plain-English policy builder allows CX teams to define how the AI should apply post-purchase policies for returns, exchanges, claims, and other workflows. These policies can be tested against historical conversations before deployment, and the AI Manager can suggest refinements based on patterns observed in live interactions.

 

How Kodif Transforms Returns Fraud Prevention

The returns fraud statistics reveal a clear pattern: brands need solutions that combine intelligence with execution capability. Kodif’s approach addresses this through three core capabilities:

 

• Write access to post-purchase systems: Unlike API-layer platforms that hit automation ceilings when underlying systems lack write endpoints, Kodif’s Resolution Agent can execute returns, exchanges, refunds, and claims directly within customer conversations. This eliminates the gap between fraud detection and prevention.
• Policy-driven automation: The plain-English policy builder lets CX teams define exactly how post-purchase workflows should be handled. Policies can be tested against historical conversations before deployment, with the AI Manager suggesting refinements based on observed patterns.
• 60%+ automation for post-purchase workflows: By combining the intelligence layer with transaction rails, Kodif achieves automation rates that exceed the market average ceiling. This allows brands to protect margins through consistent policy enforcement while maintaining the friction-free experience legitimate customers expect.

 

The data shows that 97% of protection claims are approved, meaning most post-purchase requests involve straightforward workflows. Requiring customers to navigate separate portals for these routine transactions creates unnecessary friction that detection-only AI cannot eliminate.

 

As return fraud continues to accelerate and customer experience expectations rise, the architectural advantage of platforms that can both detect and execute becomes decisive. The statistics make clear that the future of fraud prevention lies not in better detection algorithms alone, but in systems that can turn detection into automated, policy-compliant action.

 

Frequently Asked Questions

What are the most common types of return fraud in ecommerce?

The most prevalent forms include wardrobing (wearing items and returning them), empty box returns, counterfeit substitution where fraudsters return fake versions of products, overstated quantity claims, and receipt fraud using stolen or forged documentation. Organized retail crime rings increasingly target high-value items using coordinated tactics across multiple accounts.

How does “friendly fraud” differ from chargeback fraud?

Friendly fraud, also called first-party misuse or friendly chargeback fraud, occurs when a cardholder disputes a legitimate transaction through the issuer, either intentionally or because of confusion. It is a form of chargeback behavior rather than a separate category from chargeback fraud. Return-policy abuse can overlap with friendly fraud when customers dispute legitimate purchases after receiving or keeping the goods.

Can AI truly prevent return fraud, or just detect it?

AI can support fraud prevention when it can apply policy rules and execute supported actions, but capabilities vary by platform and connected system. Kodif is designed to combine the AI intelligence layer with post-purchase transaction access so eligible returns, exchanges, and claims can be resolved within the customer conversation.

What is the “automation ceiling” in post-purchase CX and how does it relate to fraud prevention?

The automation ceiling refers to the limit AI platforms hit when they lack write access to execute transactions. Kodif uses roughly 35-40% as its market framing for the automation ceiling of API-layer AI CX platforms, arguing that workflows requiring unavailable write access can force human handoffs. Platforms with deeper transaction access can automate more of those end-to-end post-purchase workflows.

How can businesses balance strict return fraud prevention with a positive customer experience?

The key is precise policy enforcement rather than blanket restrictions. AI that can evaluate each return against policy criteria and execute eligible requests while leaving exceptions for review can reduce friction without applying the same restrictions to every customer. This requires transaction access for the actions being automated, not just the ability to answer questions.