Returns Fraud and Abuse: Diagnosing the Problem, Targeting the Response
Return fraud and abuse isn’t one problem — it’s two. This research separates opportunistic misuse from deliberate deception, then maps the tactics spreading online. It closes with practical guidance on governance, joined-up data and frontline checks that slow repeat offenders without punishing honest customers in the process.
Foreword
Retailers today face a sharpened challenge: how to manage product returns in a way that is fair to customers, yet resilient to abuse and fraud. Returns have grown dramatically in both volume and complexity. And with that growth has come a clear signal from the shop floor to the boardroom; manipulation of the returns process is not just increasing, it is evolving.
Over the past year, many of our members have voiced concerns about the misuse of return policies, the rise of refund-related scams, and the growing difficulty of separating legitimate customers from those exploiting the system. The problem is not just the number of returns, it’s the growing share that now involve deliberate deception or repeated opportunism.
This Returns Fraud and Abuse Report responds directly to those concerns. Commissioned to provide our sector with evidence-based insight, it draws on large-scale consumer research, observational analysis of online fraud forums, and step-by-step mapping of how return fraud occurs. It is, without exaggeration, the most comprehensive study of return fraud and abuse undertaken in our region.
The findings make one point clear: not all return misconduct is equal and neither should be the response. What we often call “abuse” is opportunistic and rationalised. Customers view it as bending the rules, not breaking them. By contrast, return fraud is intentional, repeatable, and frequently enabled by third parties. Treating both as the same invites failure; overly lenient policies invite gaming, while blanket crackdowns alienate honest customers.
What does that mean in practice? Monitor service and risk together and make success mean meeting both. Work off a single view from purchase to refund and keep the downstream costs of a sale in sight. Give colleagues the standards, simple checks and confidence to pause and ask for a second set of eyes where it matters.
On behalf of our members, I extend my thanks to the authors, Professor Michael Townsley, Dr Andrew Childs, and Professor Joseph Clare, for their rigorous and practical work. I also thank the many retailers who contributed their time and insights to this project. Your input has shaped a report that is not only timely but actionable.
As with all the research undertaken on behalf of ECR Retail Loss, it would not be possible without the active support and involvement of the retail community who generously gave their time to participate in the most meaningful of ways. Thank you for taking the time to share your thoughts and experiences–by working together we are much more likely to Sell More and Lose Less!
Finally, I encourage you to not only read and share this study, but also take part in the work of ECR Retail Loss. Further details can be found at: ecrloss.com.
Executive Summary
This report presents the findings of a mixed-methods research project examining a wide variety of return misconduct. We conducted:
- A survey of 5,900 consumers across Australia, the U.S., U.K., and Canada;
- Digital ethnography of refunding communities on the dark web, messaging services, and
social media; and - Crime scripting analysis of major fraud and abuse types, from FTID to switch item fraud.
Key findings
- Behavioural pattern: Most customers do not offend; a small minority account for a disproportionate share of incidents. “Grey-area” abuse (e.g., wardrobing) is rationalised as acceptable and low risk.
- Organised actors: Techniques are shared and refined online, with paid refunding services available; account ageing, address manipulation and identity artefacts link returns fraud to wider ecosystem of cyber-harms.
- Measurement and capability: Performance depends less on policy length and more on enterprise discipline: shared accountability, one version of the truth from order to outcome, and confident, consistent practice at the frontline.
Cross-cutting enablers (build these first)
1. Shared accountability and guardrails (governance) Measure customer service and risk side by side; treat success as meeting both. Govern local relaxations as time- limited pilots with approvals, audit trails and sunset dates. Adopt Total Retail Loss (TRL) as the common frame for definitions, ownership and reporting.
2. Joined-up data and true economics (data) Consolidate identifiers (order, payment, return authorisation, pseudonymised customer ID) to create a single order ledger. Incorporate expected loss (returns, chargebacks, write-downs) into unit economics and roll up to customer level to target effort where it pays. Apply privacy-by- design and role-based access.
3. Frontline equipped and backed (people) Provide decision aids and conversation scripts for high-risk scenarios; mandate evidence capture (e.g., ID/original-tender match, SKU/serial, condition photos) on defined thresholds; authorise a second set of eyes where it matters; monitor overrides and apply procedurally just investigations.
Targeted menus (apply once enablers are in place)
Abuse (opportunistic misuse; proportionate, transparent friction)
- Risk-tiered return fees or credits; shorten windows for high-risk categories; clarify “not-returnable if used/tampered” rules.
- Adaptive friction triggered by patterns (e.g., high return frequency, specific SKUs); automatic waivers for low-risk customers/items.
- Behavioural nudges at purchase/return (defaults, salient norms, sustainability messaging) that reduce casual misuse without penalising the compliant majority.
Fraud (deliberate deception; investigative track)
-
- Stronger verification for high-risk returns (no receipt, alternate tender, high value): receipt/original tender/ID alignment; serial/RFID checks where feasible.
- Risk scoring across channels to route transactions for second-person review; targeted investigator contact for top risk bands.
- Escalating sanctions for repeat deception: warnings, followed by restrictions or account flags, and ultimately bans. All actions are recorded and reviewed against clear criteria.
Alongside this report, ur podcast on self-returns in retail and removing friction looks at how to balance customer convenience with the risk of returns fraud.
Context and Research Objectives
Product returns have become a defining feature of modern retail. Liberal return policies, introduced to reassure online shoppers, helped e-commerce flourish but also tripled global return volumes between 2015 and 2022. In 2024, U.S. merchants alone handled US $685 billion in returns, representing about 13 percent of all retail sales. A sizeable but hard-to-measure share of those returns involves fraud and abuse: customers claiming refunds while keeping the goods, wardrobing items for one-off use, or disputing orders had been delivered.
- Operational cost: Handling a return now consumes approximately 27 percent of the item’s sale price, up from 18 percent in 2018 (3)
- Financial-services cost: “Friendly-fraud” payment disputes cost merchants about USD $117 billion a year, much of it tied to illegitimate refund claims (4)
- Environmental cost: Landfill disposal of unsaleable returns almost doubled from 11 million metric tonnes in 2019 to 21 million metric tonnes in 2022 (5,6)
- Infrastructure cost: The reverse-logistics industry that handles returned goods is worth USD \$680–840 billion and is forecast to exceed USD \$1 trillion within the decade (7)
Viewed together, these indicate that problematic returns are growing in frequency, sophistication and cost, but they do not reveal which behaviours drive the losses, how prevalent each one is, or where interventions will be most effective. This study sought to address these gaps. Three complementary methods were employed:
- A consumer survey of return behaviours and attitudes:
- Scanning online fraud communities on the dark web, messaging services, and social media; and
- Crime scripting methodology to identify how return frauds are enacted and where they may be disrupted.
Problematic Returns: What We Know
The practice of product returns has roots in early retail environments where customer satisfaction was essential. Department stores in the late 19th and early 20th centuries introduced liberal return policies as a competitive advantage, emphasising the mantra that “the customer is always right” to build trust and loyalty. These policies were straightforward, tailored to brick-and-mortar stores, and manageable within the limits of physical retailing.
The issue of return fraud has been an ongoing challenge for retailers, with its roots documented as early as the 1970s. Noel B. Zabriskie conducted an influential study examining refund requests (selected at random) over a six-month period at a department store(8). Of the 134 refund requests analysed, approximately 12 percent were determined to be fraudulent, either as suspected or provable cases. The provable frauds were further categorised into two distinct types: petty household cheating and criminal fraud.
Zabriskie described petty household cheating as being perpetrated by consumers who, while generally honest, may attempt to deceive the store opportunistically if presented with the chance. These individuals often demonstrate a history of legitimate transactions with the retailer. Conversely, criminal fraud involves consumers who engage in repeated and premeditated acts of deception with the intent of monetary gain. These individuals may have prior criminal records or have come to the attention of security personnel at the retailer or other local stores. Crucially, there are often indications of intent to defraud present even before the transaction occurs. The findings revealed that eight out of ten cases of provable fraud fell into the category of petty household cheating, highlighting the predominantly opportunistic nature of fraudulent behaviours. In contrast, only two of the ten cases were classified as criminal fraud, representing a more deliberate and systematic effort to deceive.
Zhang and colleagues(9) compiled a typology of return frauds, detailing how different fraud types occur in online returns, in-store channels, or across both. Their analysis also identified the key factors that facilitate fraud, such as weak item tracking, underdeveloped returns portals, and inadequate fraud detection systems, and mapped these vulnerabilities within the retailer’s organisational structure. Furthermore, they aligned fraud prevention strategies with specific organisational functions, emphasising the roles of operations, loss prevention, and data analytics teams in mitigating risk. While technological advancements have improved fraud detection, Zhang et al. argue that many modern retailers continue to struggle with balancing customer convenience and fraud prevention.
Merlano and colleagues(10) expanded the study of return fraud and abuse by examining consumer perspectives on anti-fraud interventions within omnichannel fashion retail. Their research moves beyond retailer-focused analyses to assess how customers perceive, respond to, and evaluate the effectiveness of various counter-fraud measures. This consumer-centric approach helps identify how the effectiveness of fraud prevention strategies is not uniform across consumer segments, based on attitudes toward return policies, fraud deterrence, and ethical considerations. Their study also categorises interventions based on perceived effectiveness, providing insights into which measures are likely to be accepted by consumers versus those that may deter legitimate shoppers.
Before progressing further, it is important to clarify the scope of this research and the problem we are seeking to explore. Within the context of retail fraud, the terms “returns” and “refunds” are frequently used interchangeably, potentially causing ambiguity and limiting the extent of analysis. For our purposes, the term “return fraud” is adopted, as it more accurately reflects the focus of our analysis. “Returns” refer to a logistical process within retail operations, while “refunds” denote one of several possible outcomes of this process. The fraudulent activities examined herein involve the intentional manipulation of the returns process by malicious actors with the intent to secure refunds, exchanges, or other benefits (the outcome).
In addition, sharp-eyed readers will have noted our use of the somewhat cumbersome term “return fraud and abuse”. In many circumstances, these terms are used interchangeably, a practice we argue is mistaken and ultimately undermines the effectiveness of detection and prevention strategies. For the purposes of this research, we distinguish between the two concepts as follows:
Return fraud refers to criminal deceit in the returns process, deliberate acts designed to obtain a financial or material benefit through deception. Common examples include returning stolen merchandise, fabricating receipts, or falsely claiming that an item never arrived. These behaviours are typically premeditated, deceptive, and prosecutable under fraud or theft statutes. In jurisdictions such as the U.K., this conduct falls squarely under the Fraud Act 2006(11), which defines fraud as dishonest representation or omission intended to secure a gain or cause a loss.
Return abuse, by contrast, involves opportunistic exploitation of return policies. While not necessarily illegal, such behaviours often contravene the intended spirit of the policy. Examples include habitual bracketing (ordering multiple items with the intention of returning most), wardrobing (buying an item to use briefly before returning), or staging (purchasing an item for photographs, often for social media, then returning it unworn). These acts are typically motivated by convenience, perceived entitlement, or rationalisation, rather than criminal intent to deceive
In short, return fraud involves deception with the intent to secure a financial or material benefit, and is widely recognised as a criminal offence under legislation. Return abuse, on the other hand, represents manipulative but often lawful behaviour that exploits policy ambiguity rather than breaking the law. While both can result in significant losses for retailers, they differ in intent, legality, and the appropriate mechanisms for prevention and redress. This distinction parallels the classic differentiation between petty household cheating and criminal fraud first observed by Zabriskie. Table 1 presents several return behaviours across the abuse/fraud spectrum.
Table 1: Examples of Return Fraud and Abuse
| Behaviour | Description | Category |
|---|---|---|
| Wardrobing (Deshopping, “Renting”) |
Purchases item with intention to use briefly and return for full refund, e.g. wearing a dress to an event then returning it. No factual lie told to retailer (item is simply “unsatisfactory”), but return policy is exploited. |
Abuse (dishonest but often within policy) |
| Bracketing | Buying multiple versions of a product (sizes, colours) intending to keep one and return the rest. Legitimate for fit-testing, but excessive bracketing (returning the bulk of orders repeatedly) is considered misuse of free returns. |
Abuse (when done beyond normal trial) |
| Staging (Social Media Wardrobing) |
Customer purchases an item (usually clothing) solely to take photos for social media, then returns it unused. The item is technically unworn in the conventional sense (tags may remain on), but the intent was never to keep it. |
Abuse (intention misaligned with policy) |
| False Service Complaint |
Customer fabricates or exaggerates a negative service experience (e.g., poor delivery, rude staff) to obtain a refund or to pressure the retailer into accepting a return outside normal policy conditions. While no factual product issue exists, the return is granted to avoid escalation or preserve goodwill. |
Abuse (manipulative exploitation of service goodwill) |
| Did Not Arrive | Customer claims an online order never arrived (or was missing items) to get a refund or replacement, despite having received everything. Variations include claiming a high-value item was missing from the box. |
Fraud (premeditated lie; form of theft by deception) |
| Returning Stolen Merchandise |
Shoplift an item, then return it without a receipt for store credit. Or use a found/bought receipt to “return” a stolen identical item for cash. In some cases, returners re-enter the store with a previously purchased receipt and locate items on shelf to return. All cases involve returning goods not legitimately acquired. |
Fraud (criminal scheme, often linked to organised crime) |
| Price Switch / Price Arbitrage |
Manipulating price to profit from returns: switching tags, returning discounted items for higher refunds, or substituting knock-offs. May involve cross-retailer returns or product substitution for fraudulent refund claims. |
Fraud (deliberate deception and/ or product tampering) |
| Counterfeit Return | Returning a fake item in place of a genuine one for a refund. Retail staff might not detect the swap if the counterfeit looks similar. |
Fraud (deception with illicit goods) |
| Empty Box (Brickin- Box) Return |
Returning nothing or worthless items while pretending to return a product. |
Fraud (blatant deceit) |
| FTID / Lost in Transit Fraud |
Customer manipulates the return shipping process to make it appear that the returned item was sent back, when in fact it was not. Techniques include modifying shipping labels, using dummy parcels (e.g., empty boxes), or exploiting postal loopholes to evade tracking and secure a refund. Also known as ‘Fake Tracking ID’ (FTID) fraud, this is a deliberate and often sophisticated form of refund scam. |
Fraud (premeditated deception involving logistics tampering) |
It is worth noting that behaviours like wardrobing generate debate: consumers often perceive them as minor or even acceptable, whereas retailers view them as damaging misconduct. For example, a recent study(12) asked consumers and retail executives to rate various return scenarios on a scale from acceptable to unacceptable. Both groups agreed that clearly fraudulent acts (like returning a shoplifted item) were unacceptable, and legitimate returns (e.g. returning a defective product) were fine. However, for “gray area” behaviours in the middle, such as wearing an item once and returning it or using a product then claiming it did not meet their needs, young consumers were far more forgiving than retail professionals (Figure 1 visualises the results).
Many customers justify wardrobing or similar abuse by telling themselves “the retailer can resell it” or “I needed it just this once”, effectively neutralising the wrongdoing. In their minds, they are not stealing but operating within the policy. Retailers, by contrast, see even these borderline behaviours as unethical and costly, since used returns often must be sold at a discount or thrown out. Furthermore, because enforcement is rare perception gaps persist around return abuse: a “culture of entitlement” toward easy returns has normalised some abusive behaviours, complicating efforts to draw a clear line between acceptable returns and fraud.
Methodology
This research employed a three-pronged analytic strategy: a customer survey focusing on problematic return behaviours, an ethnographic scan of online fraud communities, and a step-by-step analysis of how fraud and abuse of returns are enacted, identifying the typical stages, decisions, and tactics involved. Together, these approaches captured both opportunistic and organised forms of return misconduct, guiding the development of actionable prevention strategies.
Customer Survey
To examine customer engagement in problematic return behaviours, we conducted an online survey (data collected August 2024) via Prolific: a platform dedicated to academic survey panels. Ethics approval was granted by the Griffith University Human Research Ethics Committee (GU Ref Num: 2024/398). Filters were applied to recruit respondents aged 18–45 across Australia, the U.S., Canada, and the U.K., all of whom shopped online at least occasionally. The final sample comprised 5,900 respondents, with the largest share from the United States (N = 3,496), followed by the United Kingdom (N = 1,202), Canada (N = 1,001), and Australia (N = 203).
Participants were randomly assigned four of nine return behaviour scenarios (e.g. wardrobing, empty-box return, FTID) and asked a series of questions regarding awareness, social norms, and personal engagement. Those who admitted to a behaviour completed additional items about their motivations, methods, outcomes, and future intentions. The survey concluded with general attitude items and open commentary.
Dark Web Ethnography
To examine organised return fraud, we conducted a rapid digital ethnography across dark web marketplaces, messaging service channels, and popular short-form video sharing services. This work was approved by Griffith University (GU Ref Num: 2024/091). Data collection spanned March 2023–March 2024 and included:
- Cryptomarkets and forums: 199 product listings and 710 comments relating to return fraud techniques;
- Messaging services: 17 channels and 9 groups analysed over 12 months;
- Popular short-form video sharing services: 100 top-performing videos tagged with refund fraud content.
Thematic analysis was used to identify how RaaS (Refund-as-a-Service: outsourcing a fraudulent refund to a specialist) operates, how techniques are refined and shared, and how bad actors balance risk, reward, and perceived legitimacy.
Crime Script Development
To understand how return fraud and abuse is enacted and identify points of intervention, we used crime scripting, a technique that partitions crimes into step-by-step actions and decisions made by offenders. This method is well suited to complex multi-stage crimes like fraud.
We began by reviewing academic literature, offender tutorials from dark web forums, and findings from the consumer survey. From this, we compiled a comprehensive list of known return fraud and abuse types, such as wardrobing, FTID scams, receipt-based returns, etc. We then focused on the most encountered forms of return fraud and abuse based on academic literature, industry workshops, and fraud community observations. We also sought to identify a concise set of distinct fraud and abuse types, ensuring our classification captured meaningful distinctions clearly and efficiently. Based on prevalence and relevance to modern retail environments, our final set included:
Wardrobing: Customers purchase clothing or accessories, wear or use the items, and return them for a refund.
- DNA (Did Not Arrive): Customers claim that their online order never arrived, despite receiving the product.
- Receipt-Based Return: Bad actors use counterfeit, altered or stolen/found receipts to return items they never purchased.
- FTID (Fake Tracking ID): A bad actor manipulates the return tracking ID to falsely indicate that a return package has been shipped or received by the retailer.
- Switch Item Return: A bad actor returns a different, less valuable item in place of the original, more expensive product.
- Team Member False Returns: Team members collaborate with external actors to process fraudulent returns, typically for high-value items.
An example script (with corresponding prevention strategies) is provided later in the report. Full details of the process for developing these scripts and the evaluation of the script outcomes are presented in the technical report[i]
[i] Technical Report: Return Fraud and Abuse Crime Scripts and Prevention Strategies available at ecrloss.com.
Key Findings
Customer Insights
Sample Details
Results presented here are aggregated for the full sample (N = 5,900, collapsed across countries). There was a slightly greater proportion of female respondents (51%), almost two-thirds (62%) were aged 34 years and younger, respondents typically held either a high school diploma or a Bachelor’s degree (82%, combined), on an at-least-weekly basis 41 percent purchased new products in physical retail outlets and 37 percent purchased new products online, and 27 percent of respondents returned goods by mail/courier often/very often with 26 percent returning goods to physical retail outlets often/very often.
Relative Rates of Different Types of Return Fraud and Abuse
Figure 2 displays the relative frequencies at which participants engaged in nine return-misconduct types in the previous 12 months. Ranked by decreasing frequency: wardrobing (buying, briefly using/wearing, then returning) was disclosed by 12 percent of respondents; DNA (Did Not Arrive) claims (falsely asserting non-delivery for goods received) by 6 percent; switch-item returns (returning a different, lower-value or used item in place of the original) by 5 percent, and so on. Error bars show 95% confidence intervals for each estimated proportion.
Problematic Return Participation and Activity Rate
The survey design ensured that all respondents were given the opportunity to report on four types of return fraud, randomly selected from a broader list of nine fraud types. In line with longstanding criminological findings regarding the non-random nature of offending behaviour, the data showed that the majority of respondents (83%) reported no engagement in fraudulent returns. The next most common group (15%) admitted to one type of fraud, while a small subset exhibited more prolific offending patterns: 1.5 percent had engaged in two types, 0.2 percent in three types, and 0.1 percent (seven individuals) in four types. Figure 3 depicts these results graphically.
Among those who admitted to fraudulent return behaviour, most indicated they had done so only once, though certain fraud types stood out for higher repetition rates. Specifically, 30 percent of respondents who engaged in “Refund as a Service” (RaaS) had done so only once, as did 44 percent for receipt-based fraud and 46 percent for empty box fraud.
Repeat offending was evident across all fraud categories. For example, the most active 10 percent of “wardrobers” accounted for 35 percent of self-reported incidents. In the case of FTID fraud, the most active 30 percent of respondents were responsible for 73 percent of incidents. Similarly, 57 percent of team member fraud was attributed to just 14 percent of respondents who reported engaging in this behaviour.
These findings underscore several important implications for prevention. First, the overwhelming majority of customers do not engage in return fraud. This should encourage retailers to prioritise interventions that maintain a seamless experience for legitimate shoppers, while deterring dishonest behaviour. Measures such as light-touch identity verification or randomised return inspections can signal oversight without creating undue burden.
Second, the group who admitted to a single type of fraud (typically only once) likely represents opportunistic offenders. Their conduct may be deterred through timely and minimally invasive interventions, such as well-positioned reminders of return policies, brief verbal or visual cues during the return process, or small procedural hurdles that make fraudulent returns more effortful than legitimate ones.
Finally, the high concentration of offending within each fraud type suggests a clear role for targeted, method-specific prevention strategies. Rather than imposing broad restrictions across all customers, retailers would benefit from deploying proportionate, intelligence-led interventions that focus resources on the behaviours and tactics driving the greatest harm.
Target Selection and Decision Making
For each of the nine return behaviours, respondents who had engaged with that behaviour at least once were asked to focus on the most recent occasion and provide insight into a range of target selection and process decisions. The varying percentages of return behaviours corresponding to different high-level target categories are displayed in Figure 4.
The results suggest that (a) clothing returns are disproportionately affected by wardrobing and receipt-based frauds, aligning with product characteristics that allow wear-and-return or receipt substitution; (b) electronics are strongly targeted by more technically sophisticated frauds such as bricking, empty box, switch item, and FTID, consistent with their high value and ease of resale; (c) DNA fraud is prevalent in the food delivery sector (nearly 50%), likely reflecting low barriers to falsely reporting non-receipt in app-based refund systems; and (d) household items display a relatively even spread across several fraud types, though receipt-based, switch item, and DNA frauds are most prominent. These findings reinforce the notion that fraud types are not randomly distributed across retail sectors but are instead shaped by the interaction between product characteristics, return policies, and offender opportunity structures.
For all behaviour types, respondents who had engaged in at least one occasion were asked where they got the idea the most recent time. The responses to this question indicated that most return behaviours result from personal experience of successful false returns (avg. 39% across all behaviours), with individuals discovering tactics through trial and error. Further to this, friends and family play a key role in spreading knowledge (avg. 29%), particularly for simpler behaviours like wardrobing (31%) and switch item fraud (37%). In contrast, more complex frauds, such as RaaS (combined 40% all of the time), FTID (combined 35% all of the time), and bricking (combined 27%), are often learned from online forums and social media, where communities share communities share strategies.
Respondents who disclosed they had engaged in one of the problematic return behaviours were also asked a range of questions about why they targeted specific businesses, the purchase-refund method they used, whether there were any issues with the return, what they did with the goods, and whether they would repeat the behaviour. Figure 5 displays the average for their most recent return for all respondents.
Figure 5 suggests a pattern of opportunistic, low-risk abuse rather than calculated or professional fraud. Most respondents kept the goods rather than reselling them, suggesting the motivation lies in personal gain through casual exploitation rather than commercial profit. This, combined with the finding that nearly 90% encountered no difficulty completing their return, implies the behaviour often emerges from individual experience, where offenders stumble upon a procedural vulnerability rather than actively seek one out. Retailers were typically chosen for their perceived leniency (47%) or because the individual was already familiar with the store’s policies (36%); external cues such as online reviews were far less influential (18%).
With approximately 70 percent of respondents indicating they would “definitely” or “maybe” do it again, these return behaviours collectively appear to offer a reliable reward with minimal perceived risk. For prevention, this suggests the value of modest procedural frictions at the point of return (particularly measures that apply consistently across channels) to deter casual repeat abuse without unduly burdening legitimate customers.
Within each behaviour question set, respondents were asked to provide free-text responses indicating the strategies they had taken to increase the likelihood of success. The successful strategies broadly grouped into three categories: effective communication tactics, calculated use of store policies, and careful handling of product conditions. It was perceived that a combination of these approaches increased the chances of securing a refund while minimising the risk of conflict and/or rejection. Some examples to demonstrate these types of strategies include:
Prevention and Disruption
Respondents were also asked questions relating to the perceived effectiveness of various interventions that retailers could take to discourage each of the return behaviours being examined. The relative rankings of each intervention were calculated by aggregating ‘very effective’ and ‘extremely effective’ responses in each case. Figure 6 displays the results, disaggregated by whether respondents were assessing the intervention in relation to abuse or fraud.
The two highest rated strategies overall were being charged a return fee and being contacted by a company investigator for review. For fraud, being charged a return fee was viewed as the most effective deterrent, with nearly 60 percent rating it as very or extremely effective. For abuse, this intervention was similarly ranked highest, though slightly less emphatically. Contact by a company investigator was the second highest rated intervention for both fraud and abuse contexts.
Notably, interventions based on customer education or appeals to collective cost were rated much lower, especially in the context of abuse. For example, only around 15–20 percent of respondents viewed statements about the financial harm to the company or the broader social cost of abuse as highly effective deterrents. These findings suggest a clear hierarchy in perceived deterrence: direct, individualised consequences (e.g., fees, investigations) are seen as more effective than informational or moral appeals.
Return Fraud Offenders and Communities
We examined how digital environments, including the dark web, social media, and encrypted messaging applications, facilitate offender pathways into return fraud and help shape its growth against retailers. By understanding the organisation of these fraud actors, along with how they distribute knowledge and provide services to execute return fraud, this phase provided the foundation for understanding return fraud actors and potential retailer countermeasures. In these communities, “methods” (step-by-step refunding playbooks) and RaaS (Refund-as-a-Service, where customers engage a third party to carry out a fraudulent refund) are central organising ideas.
The findings presented are informed from a period of rapid digital ethnography where we triangulated insights from forum and chat messages in return fraud communities, return fraud product listings, and tutorials/guides shared in these spaces. The following key themes were developed:
- How fraud communities share and refine refunding methods.
- The rise of organised return fraud operations and RaaS.
- Motivations and risk management strategies of bad actors.
- Overlaps between return fraud and other fraudulent practices.
Developing and Sharing Methods
Return fraud actors share detailed instructions for fraudulent returns through collaborative development and dissemination of methods. In return fraud communities, actors use the term method to refer to the specific systematic techniques and strategies that, if followed correctly, can successfully exploit retailer return policies or manipulate retailers to receive a refund for a product. We found evidence of a wide variety of refunding methods discussed in these communities, including the empty box (and its variant the partial empty box), DNA, FTID, lost in transit, and leak methods. Detailed instructions for executing these methods, which retailers to target with methods, and the financial limits of the different refunding methods were shared through dedicated user guides and handbooks well-known to community members or via lengthy forum posts from actors outlining the specific steps for executing the frauds. Refunding methods were discussed in community interactions where return fraud communities recounted their own positive and negative experiences of refunding to enhance the legitimacy of common methods and to refine refunding practices.
The development of methods through community discussions was also useful in teaching return fraud actors some of the fundamental skills required to accomplish successful returns. One prominent example of this was observed in how return fraud communities would share information on social engineering (psychological manipulation of staff to obtain compliance) techniques, such as effectively pressuring customer service employees into issuing refunds by making them feel stressed or otherwise compelled to provide a refund.
In addition to sharing established return fraud methods, these communities encourage collaboration and serve as platforms facilitating the development of improved return fraud strategies that try to identify new vulnerabilities at the delivery process to exploit. For example, actors brainstormed ways to overcome new packaging processes (e.g. tamper-proof packaging, shipping multiple items separately) and delivery adaptations (e.g. delivery photos of shipment at doorsteps). These communities would receive feedback and assess the viability of adaptations of refund techniques, and there was evidence that this was particularly useful for newcomers into the return fraud domain to seek guidance and clarification from experienced return fraud actors. Moreover, because of the way that messaging service communities facilitate rapid communication on a mobile app compared to dark web forums, this allowed return fraud actors to provide step-by-step updates during their refund attempts to solicit real-time feedback from other members.
[ii] Leak methods involve actors claiming a product (e.g. battery, perfume, perishable food item) leaked during transit, damaging the shipment order and rendering other ordered products unusable
Refund-as-a-Service
In addition to the sharing of return fraud strategies in dark web and messaging service communities, return fraud expertise is increasingly commercialised. As part of our exploration of these communities, we examined the online illicit markets emerging from this activity. We firstly examined dark web cryptomarkets, large online illicit marketplaces hosted in the dark web, to evaluate the sale of return fraud guides and services. Somewhat surprisingly, our findings from analysing these product offerings suggest that, while these illicit markets did contain numerous suppliers of refunding guides, these products are not popular and the reviews for these products are minimal. Considering this in light of the community discussions, we suggest that this is the case because many of these are low-quality products that primarily compile information already freely available found within these communities. In fact, many of these guides sold on dark web cryptomarkets appeared to offer similar generic refunding advice where vendors would replicate listings but alter the retailer (see Figure 7).
In contrast to dark web cryptomarkets, we did find there to be a demand for return fraud services on messaging service platforms. These were services where actors could purchase a refunding professional to organise a refund on their behalf in situations where individuals either lacked required skills to execute the fraud (e.g. photoshopping shipment labels for FTID and lost in transit) or had rejected refunds from retailers from their own attempts at common refunding methods. These RaaS vendors usually operated on structured payment models, typically featuring tiered commissions depending on the size of the refunds or as flat-fee pricing, with vendors usually accepting payment in cryptocurrencies or PayPal transactions. Messaging service vendors would integrate bots into their channels to assist with enquiries from prospective customers, and would also simultaneously run Discord communities and other digital infrastructures (e.g. surface websites, Google Forms and Sheets) to organise their services. RaaS vendors achieved recognition and popularity by promoting themselves in communities or from customer vouches. Establishing trust is a key challenge in online illicit markets due to the lack of quality assurances and the prevalence of scams. To address this, messaging service vendors strive to present themselves as trustworthy by showcasing customer vouches through screenshots of refunded order confirmation and promoting their success in other communities. Community members also play a role in verifying vendor legitimacy through peer recommendations and this social validation was often used in return fraud communities to verify who the trustworthy vendors are.
INSERT: Figure 7: Dark web cryptomarket listings of duplicate refund fraud methods by the same vendor
Motivations and Risk Management
Digital environments are actively used by return fraud actors to share motivations, celebrate successful refunding attempts, and collectively manage the perceived risks of engaging in this form of fraud. Financial gain was frequently cited as a primary motivator, with participants ranging from those pursuing one-off opportunities to others seeking to scale their refunding efforts for larger rewards. These platforms also served as spaces where actors showcased the tangible benefits of return fraud to interested peers, reinforcing participation.
Many were drawn to these communities by the perception that return fraud carries lower risks compared to other fraudulent practices. Nevertheless, certain risks were commonly discussed. These included concerns about law enforcement involvement, the possibility of having personal accounts or delivery addresses blocked by retailers, and fears that products, particularly electronics, could be tracked or remotely disabled via serial numbers. Such risks were often downplayed or managed through shared folklore and practical advice aimed at avoiding detection. Community narratives further neutralised concerns, with members employing humour and rhetoric to portray return fraud as a harmless game played against impersonal corporate systems.
Interconnections of Return Fraud
Interconnections of Return FraudOur analysis of return fraud communities found that return fraud is associated with broader cybersecurity threats within retailing organisations. Exploring these interconnections of return fraud with other security problems can be helpful in considering the broader environmental conditions and enablers that are sustaining and shaping the prevalence of return fraud behaviours.
Customer accounts that are aged (previously active accounts) play a significant role in facilitating return fraud among actors as it was believed that they were less likely to trigger detection from retailers. As a consequence, return fraud becomes interconnected with forms of identity fraud because buying pre-aged customer account details is seen as a quicker and more efficient way to acquire aged customer accounts than using the offenders’ own account or taking the time to craft a trustworthy customer account. We found evidence that the same communities discussing return fraud strategies are also distributing information and soliciting requests for customer accounts.
In addition, this practice also extended beyond just buying and selling customer accounts but to other forms of sensitive customer data. Return fraud community members would also request full [iii] or engage with synthetic identity providers to facilitate return fraud with fake bank accounts. We found many other examples of fraudulent practices enabling return fraud actors to circumvent retailer security measures, such as address jigging (bad actors making slight alterations to their own addresses), the use of drop addresses by taking advantage of Airbnb and real estate listings, and the use of fraudulent police reports to falsely verify packages being stolen to validate a need for a refund.
Once bad actors successfully obtained products through return fraud, we also found clear evidence of systematic approaches to sell refunded goods onto other consumers. Offenders appeared to opt for online second-hand markets such as classifieds, auction sites, and social media, to fence refunded goods into stolen goods markets. These platforms are ideal outlets for return fraud actors due to their minimal verification processes and the normalisation of resale culture for electronics and fashion products.
Fraud Script and Prevention Strategy Findings
Crime scripting, step-by-step mapping of offender actions and decisions, is used here to identify where return fraud can be disrupted or deterred without unduly burdening legitimate customers. To develop crime scripts and evidence based countermeasures, we convened a one-day industry workshop on 15 August 2024 at Bunnings National Training Centre in Melbourne, Australia. Twenty-one retail professionals representing diverse sectors, including apparel, hardware, pharmacy, department stores and online marketplaces, participated.
After a brief explanation of the crime script concept, participants were divided into groups, and each group was provided with a set of return fraud types. For each fraud type, a vignette describing the fraud scenario was presented, followed by the corresponding crime script that detailed the step-by-step actions carried out by the bad actor. The scripts outlined key vulnerabilities in the refund process, allowing participants to focus on devising strategies that would disrupt, dissuade, or detect the fraudulent activity. To illustrate, the online wardrobing vignette, script and prevention strategy are recreated below.
[iii] Slang terminology for the full information of victims involved in data breaches such as names, addresses, and credit card information.
Online Wardrobing Vignette
The bad actor initiates the process by comparing multiple retailers that sell the targeted items. The bad actor evaluates the respective return policies, opting for the retailer with the most lenient return policy, all other factors being equal. To strengthen their customer profile, especially if it is the first purchase, the bad actor makes one or more small purchases to establish a “good customer” profile. The bad actor then proceeds to make a legitimate purchase of the targeted items, completing the transaction with all required details.
Following the dispatch of the items by the retailer, the bad actor receives the delivery, legitimately accepting the shipment. After using the items, the bad actor decides to return them. The bad actor carefully reattaches the tags to the items and repackages them to resemble their original condition, disguising any signs of usage.
When initiating the return, the bad actor provides a fabricated reason for the return, inventing a plausible but false explanation for dissatisfaction. The bad actor employs specific language that aligns with the store’s return policy, intentionally identifying and exploiting any policy loopholes or ambiguities to ensure compliance with the stated terms. The bad actor then seeks return authorisation. Using social engineering tactics, the bad actor applies pressure on customer service agents, acting frustrated but friendly to gain approval for the return.
The bad actor then packages and ships the items back to the retailer, completing this step legitimately. To minimise scrutiny, the bad actor times the return to coincide with busy trading periods, thereby reducing the likelihood of thorough inspection by the retailer.
During the refund or exchange processing stage, the bad actor engages in further social engineering. The bad actor pressures customer service agents into accepting the return or making an exception, potentially escalating to threats of complaints or poor reviews. In the event of a refund denial, the bad actor initiates a chargeback with their credit card company, disputing the transaction to obtain the refund. Finally, to improve their customer profile for future transactions, the bad actor may make one or more small purchases, thereby reinforcing their status as a “good customer”.
Table 2: Online Wardrobing Crime Script
| Legitimate Step | Bad Actor Action | Prevention Strategy |
|---|---|---|
| Select Retailer | Compare return policies and choose the most lenient. | Regularly review and tighten return policies to minimise exploitation |
| Establish “Good Customer” Credentials | Makes small purchases to establish a trustworthy profile. | Use loyalty programs and customer data to differentiate legitimate vs. high-risk behaviour. |
| Purchase | Completes legitimate purchase. | Inform customers about return conditions at purchase and flag suspicious orders. |
| Dispatch | No fraudulent action at this stage. | Maintain accurate dispatch records to support future return scrutiny. |
| Delivery | Legitimately receives items. | Integrate delivery tracking data to assist with downstream fraud detection. |
| Return Initiation | Reattaches tags, disguises usage, repackages items, provides fabricated reason. | Use packaging and tags that deter reuse or concealment of wear. |
| Reason for Return | Invents a plausible but false reason, exploits policy loopholes. | Train staff to identify suspect reasons and flag questionable patterns. |
| Return Approval | Social engineering: pressures agents to accept return or make an exception. | Require elevated review for high-risk returns and automate checks where possible. |
| Packaging and Shipping | Legitimately completes this step. | Set clear refund conditions and verify them before processing. |
| Item Inspection | Returns items during busy periods to avoid thorough inspection. | Prioritise inspection resources during peak periods and verify returns against purchase data. |
| Refund/Exchange Processing |
Social engineering to push for approval, threats of complaints, refuses store credit or exchange. | Support staff in enforcing return policies calmly and consistently. |
| Refund Denial | Threatens a chargeback if denied. | Monitor for chargeback threats and maintain audit trails to dispute them. |
| Transaction Processing | Legitimately completes this step. | Review flagged transactions before refund and use bank alerts where available. |
| Repair Customer Profile | Makes small purchases to maintain ‘good customer’ status for future fraud. | Track behavioural patterns over time and escalate repeat abuse for manual review. |
Each workshop group was tasked with identifying intervention points and designing strategies to disrupt or deter the return behaviour. Each script was examined by multiple groups to reduce the reliance on any single group’s perspective. This approach allowed for triangulation of ideas and minimised the risk that an idiosyncratic view would overly influence the final set of strategies. Observations showed a high degree of convergence in the strategies developed, particularly for in-store frauds. However, for online frauds such as DNA and FTID, strategic approaches diverged, with some groups proposing AI-driven solutions (tech-focused) and others favouring manual verification steps (manual focused).
All workshop strategies were documented in real time using a shared Google Sheet, ensuring clarity and traceability for subsequent evaluation.
The proposed strategies were independently evaluated by a nine-member expert panel drawn from major retailers across North America, Europe, and Asia-Pacific. Panelists held senior positions in loss prevention, fraud risk, and risk roles. Recruitment was conducted via LinkedIn and the ECR Retail Loss Working Group, with ethics approval granted by the Griffith University Human Research Ethics Committee (GU Ref Num: 2024/510).
Experts received a pack containing detailed crime scripts, vignettes, and the proposed interventions. They were asked to evaluate each strategy using a structured rubric comprising four criteria:
- Cost to Business–financial and operational feasibility;
- Manageability–ease of integration into existing systems;
- Impact on Customer Experience–unintended consequences for legitimate customers; and
- Effectiveness–expected impact in preventing or reducing return fraud.
Detailed prevention strategies were developed for each fraud and abuse script and this process demonstrated value and validity to adopting a crime scripting approach to understand returns fraud and abuse within retail environments. Due to space limitations, these detailed scripts and evaluations are presented in a separate technical report.[iv]
[iv] Technical Report: Return Fraud and Abuse Crime Scripts and Prevention Strategies available at ecrloss.com.
Bringing It All Together
Although detailed, script-specific strategies provide targeted interventions, many retailers concurrently face multiple fraud or abuse types. Given that offenders collaborate to develop new methods and rapidly shift to new exploits when older ones fail, targeting individual fraud or abuse scripts in isolation may amount to a game of whack-a-mole. Drawing from our detailed analysis of individual script and their corresponding prevention strategies, we developed a generalisable five-point prevention framework to guide retailer responses. These levers, applied individually or in concert, address system-wide vulnerabilities and enable tactical interventions.
- Policy Reinforcement and Friction: Clarifying policies, standardising checks, and tightening eligibility criteria reduce procedural exploitation opportunities.
- Escalation and Second-Person Reviews: Requiring additional approvals at key decision points introduces friction.
- Digital Trace Evidence: Enhancing transaction verification (e.g., robust digital evidence requirements) can help identify fraudulent claims.
- Behavioural Profiling and Pattern Detection: Using risk profiles based on customer return patterns helps tailor adaptive policies.
- Staff Awareness and Consistent Enforcement: Training employees to identify scripted behaviours reduces vulnerability from inconsistent policy applications.
An illustrative example of these principles emerged when comparing online and in-store wardrobing strategies (see technical report[v] for full details). Despite comprising similar fraud countermeasures, the online return process was considered to be more resistant to abuse compared to the in-store process, even thought they were largely equivalent. The key difference is that online process comprised natural delays and multiple verification points. The in-store returns process is comparatively compressed and thus more attractive to offenders.
The empirical patterns above point to three enabling conditions that determine whether specific countermeasures take root: (i) aligned incentives and governance so sales targets do not overwhelm risk controls; (ii) joined-up data that links purchase, shipment, return and dispute touchpoints into a single auditable view; and (iii) team members positioned as a critical control, supported and fairly assured. The recommendations that follow are organised around these enablers and then set out strategy menus for abuse and fraud that become feasible once the enablers are in place.
[v] Technical Report: Return Fraud and Abuse Crime Scripts and Prevention Strategies available at ecrloss.com.
Recommendations to Retailers
Total retail loss (TRL) is a whole-of-enterprise framework for defining, measuring and managing all forms of retail loss (malicious and non-malicious, known and unknown) using a common taxonomy, consistent measurement rules and clear ownership. Its advantages are that it (i) creates shared definitions so numbers are comparable; (ii) consolidates data across channels and functions so loss can be traced end-to-end; and (iii) enables evidence-based prioritisation by materiality and controllability. The returns channel is especially well suited to a TRL approach because loss can emerge at multiple stages and across multiple teams (purchase, fulfilment, return initiation, transport, inspection, refund/credit). Without a single frame, teams will optimise local KPIs and simply shift cost or risk elsewhere.
Cross-cutting enablers
1. Aligned incentives and governance (Operational capability)
Return policies and service standards shape behaviour. In many organisations, speed and “first-contact resolution” are prioritised to the detriment of review quality; elsewhere, local policy relaxations intended to “delight” customers quietly shift risk to loss prevention or operations. To align incentives, run a dual Customer Experience–Risk scorecard. Report service metrics (e.g., AHT, NPS) alongside control metrics (e.g., decision accuracy, audit exceptions) and treat performance as successful only when both sets meet targets. Require mandatory checks at high-risk returns (e.g., no receipt, high value, alternate tender): capture specified evidence and route to second-person approval. Govern change so any local relaxation operates as a formal pilot with management approval, audit trail and explicit sunset or renewal criteria.
2. Joined-up data (Data capability)
Retailers already capture rich signals, but they are fragmented. Treat returns as a strategic data asset and stitch systems together using stable identifiers (order, payment, return merchandise authorisation, pseudonymised customer ID). Then build forward-looking, unit-level economics at the order level: start with price and subtract traceable costs (product cost, pick/pack, delivery, service time) plus expected loss (a probability-weighted estimate of the costs likely to arise after the sale from returns, chargebacks, write-downs and fraud). Then sum to the customer level (less targeted marketing/service)[vi]. Expected loss can be estimated from historical patterns by category and risk tier, known risk flags (e.g., no-receipt, high value, alternate tender), and customer/store history, and it should update as policies and behaviours change.
Making expected loss explicit delivers a number of practical advantages. It enables proactive control design, directing verification and fees to high-expected loss segments while keeping low-expected loss journeys fast and low-friction. It yields truer economics by avoiding “profit today, loss tomorrow,” recognising likely returns and chargebacks at the point of sale and it supports better policy experiments, evaluating pilots on expected loss-adjusted outcomes (e.g., post-return loss per return, chargeback rate) rather than raw approval speed. It enables sharper pricing and promotions, ensuring offers remain value-positive once expected loss is priced in. With this visibility, retailers can pinpoint where losses concentrate, target friction where it pays and protect valuable customers.
[vi] This is the so-called pocket margin14 but other unit economic models exist, including customer profitability analysis or cost-to-serve. They typically incorporate activity-based costing at the various stages of the purchase–fulfilment–return chain15
This is the so-called pocket margin but other unit economic models exist, including customer profitability analysis or cost-to-serve. They typically incorporate activity-based costing at the various stages of the purchase–fulfilment–return chain.
Shared definitions (e.g., what counts as a goodwill credit) and sensible cost drivers (e.g., time to process a return) create one economic truth for every order and customer. Because data consolidation amplifies both value and privacy risk, apply privacy-by-design (data minimisation, pseudonymisation, role-based access, encryption, logging and retention limits) so the analytics can be safely used across teams.
3. Team members as a critical control—support, standards, and accountability (People capability)
Team members shape outcomes directly (e.g., false returns, collusion) and indirectly (e.g., pressure, misdirection, inconsistent scrutiny). Treat staff as an asset to develop and support: provide decision aids and conversation scripts in POS/CRM for high-risk scenarios, give explicit authority to pause a return for second-person review without stigma, and offer continuous development on current scam patterns and “red flags”. Balance this enablement with proportionate controls and accountability: require two-person approval for high-risk cases (e.g., high-value, no-receipt, alternate tender), mandate capture of specified evidence (ID/original-tender match, SKU/serial, condition photos), monitor overrides and refund-to-alternate-tender patterns, conduct random audits, and apply procedurally just investigations with proportionate consequences. The objective is confident and consistent practice: team members can explain and enforce requirements, are backed when they do so, and understand that deliberate breaches attract appropriate sanctions.
With these enablers in place, the following menus are far more effective. Without them, even well-designed tactics struggle to gain traction.
Strategies for Return Abuse
These measures are designed to reduce opportunistic misconduct, particularly behaviours like wardrobing, staging or bracketing, which exploit generous return policies without overt criminality.
- Refine and Clarify Return Policies. Retailers should ensure that return policies are precise and transparent, particularly for high-risk categories like apparel and electronics. Policies that clearly prohibit returns of visibly worn or tampered items help reduce wardrobing. Shortening return windows for specific items can also reduce abuse without compromising overall customer satisfaction.
- Introduce Adaptive Friction into the Returns Process. Friction can be selectively applied to deter casual abusers. For instance, charging a modest fee for online returns or restricting free returns for customers with high return frequency can signal boundaries without alienating honest customers. Our survey indicates that return fees are perceived as the strongest deterrent to abuse. Pilot a risk-tiered fee structure: automatically waived for low-risk customers and low-risk items; applied or higher for high-frequency returners and categories with elevated abuse. Be transparent about the rule set, offer store-credit offsets where appropriate, and evaluate impacts on legitimate customers before scaling.
- At the Point of Purchase Use Behavioural Nudges. Behavioural nudges should target the decision phase before or during purchase. Drawing on Wortley’s concept of situational precipitators [vii], retailers can influence behaviour by designing shopping environments or experiences that make opportunistic abuse less appealing. For example, displaying personalised return rate comparisons (“Your return rate is higher than 90% of other customers”) may prompt self-reflection. Other examples include checkout pop-ups offering incentives to waive return options, or defaulting to store credit. These subtle interventions shift the perceived cost-benefit calculus without overt enforcement. Situational precipitators refers to environmental cues or conditions that actively encourage or trigger offending, rather than simply enabling it.
- During a Return, Communicate Expectations and Norms Effectively. Retailers can improve compliance by using normative messaging and education once the return process is initiated. For example, confirmation screens or return receipts can highlight that excessive returns contribute to environmental waste or strain inventory systems. Rather than emphasising product pricing, an argument our survey suggests consumers find unconvincing, communications should highlight broader ethical impacts such as sustainability and fairness. Behavioural research indicates that norm-based messages can shift consumer behaviour, particularly when these norms align with emerging values like environmental consciousness.
[vii] Situational precipitators refers to environmental cues or conditions that actively encourage or trigger offending, rather than simply enabling it.16
Strategies for Return Fraud
These strategies are aimed at high-risk behaviours involving active deception, such as false claims, receipt forgery, or refund fraud conducted with stolen goods or falsified identities.
- Strengthen Verification Requirements. Retailers should require stronger verification for high-value or no-receipt returns. This includes requiring a matching receipt, original payment method, or customer ID. Technologies such as RFID or unique product codes can also authenticate returned goods.
- Apply Data Analytics and Risk Scoring. Machine learning and transaction analytics can be leveraged to detect suspicious return patterns in real time. By analysing factors such as return frequency, item value, and customer account behaviour, retailers have reported improved capabilities in flagging serial return abusers. Fraud scoring models can be used to assign risk levels to transactions, helping to prioritise cases for manual review or enhanced verification procedures. In a similar way, it would be possible to examine data for high-risk returned items, particularly where these patterns overlap with problematic customer account behaviour.
- Segment Returns Processes Based on Risk. Establish separate return handling tracks for high-risk items or customers. For example, high-value returns or those without receipts could be routed to specialist staff for inspection and approval.
- Introduce Escalating Sanctions for Repeat Offenders. Retailers should implement a tiered response system: a warning on first misuse, followed by investigations/visits, restrictions, account flagging, or bans for repeated fraudulent behaviour. This approach balances deterrence with fairness and is supported by research on behavioural escalation and compliance. For transactions that score as high-risk, proactive contact by an investigator (documented call or email) functions as a strong deterrent and signals auditability. Constrain its use to the top risk bands to avoid impacting legitimate customers, and record outcomes to refine thresholds over time.
Conclusion
Preventing return fraud and abuse is ultimately an organisational task, not a menu of disparate tactics. The evidence points to three enabling conditions. First, aligned incentives and governance: make customer experience and risk co-exist by running a dual scorecard, enforcing policy parity across channels, and treating local relaxations as time-limited, signed-off pilots within a TRL operating model. Second, joined-up data: create a single ledger for orders, returns and disputes, built on stable identifiers and privacy-by-design, and use forward-looking unit economics that include expected loss so debates become testable and friction can be targeted where it pays. Third, people-led controls: equip and back team members with decision aids, clear authority to escalate, specified evidence requirements and proportionate oversight, so practice is both confident and consistent. Taken together, these enablers make the specific strategies set out above—whether aimed at opportunistic abuse or deliberate fraud—practical and scalable.
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Disclaimer
The research for this report was supported by the ECR Retail Loss. This report is intended for general information only; it is based upon a review of the available literature together with primary research undertaken with retail organisations in Europe. Individuals or companies are advised to take professional advice regarding their specific needs and requirements prior to taking any actions resulting from anything contained in this report. Any such actions taken by individuals or companies are entirely at their own risk. Companies are also responsible for assuring themselves that they comply with all relevant laws and regulations, including those relating to intellectual property rights, data protection and competition laws or regulations. The images used in this document do not necessarily reflect the companies taking part in this research. © September 2025, all rights reserved.
About the Authors:
Professor Michael Townsley is a criminologist at Griffith University’s Griffith Criminology Institute, an internationally recognised centre for applied crime prevention. He specialises in retail crime risk, particularly returns fraud and abuse, organised retail crime, and e-commerce/card-not-present scams. Michael serves as Academic Lead to the Profit Protection Future Forum (ANZ) and works closely with national retail peak bodies to translate research into frontline practice and policy. Earlier research on the contagion of burglary risk helped shape modern spatial criminology and continues to inform place-based prevention. He is a recipient of the Adam Sutton Crime Prevention Award (ANZSOC) in 2023, recognising scholarship with real-world impact. He can be contacted at m.townsley@griffith.edu.au
Dr Andrew Childs is a Lecturer in Criminology at the School of Criminology and Criminal Justice, Griffith University. He is an expert in the human factors of cybercrime and has a research program focussed on the development of online illicit markets, the social organisation of cybercrime communities, and the criminogenic affordances of disruptive technologies (e.g. cryptocurrencies). His work often uses novel digital research methods to understand digital crimes, which has ranged from conducting qualitative interviews with active offenders via encrypted messaging applications to analysing native online data (e.g. forum posts and chat logs). Dr Childs has led pioneering research into evolving digital crimes, including the first published work on social media and encrypted messaging application drug supply, organised identity fraud during COVID-19, and the victimisation processes of cryptocurrency scams. His expertise in these topics is widely recognised, and he has provided expert commentary on cybercrime issues to national and international media outlets along with authoring submissions to Australian parliamentary inquiries. He can be contacted at a.childs@griffith.edu.au
Dr Joseph Clare is a Professor of Criminology at the University of Western Australia with 20 years of applied experience in a range of criminal justice and emergency first-responder areas. Joe is a quantitative social scientist with a strong commitment to the nexus between theory, applied research, and practice, and he is focused on using available data to help criminal justice agencies develop problem-specific solutions and data-driven prevention strategies. His current research interests focus on the application of crime prevention frameworks to loss prevention and academic integrity. He can be contacted at joe.clare@uwa.edu.au
About ECR Retail Loss: ECR Retail Loss is part of the ECR Community, a voluntary and collaborative retailer-manufacturer platform with a mission to fulfil consumer wishes better, faster and at less cost. Over the last 25 years, the group has acted as an independent think tank focused on creating imaginative new ways to better manage the problems of loss and on-shelf availability across the retail industry. Championing the idea of Sell More and Lose Less, the group is open to any retailer and manufacturer to join. Its work is supported by research funding provided by AXON, Checkpoint Systems, NCR, Retail Insight, RGIS and Vusion Group. For further information: ecrloss.com
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