Editorial
A retailer’s loss prevention department can be misconstrued as only chasing down shoplifters or investigating cases of internal and external theft and fraud. Yet, there are many non-fraud sources of loss that need to be accounted for, including food waste, supply chain errors, pricing mistakes, damaged products during stocking and more.
When retailers embark on reducing the many ways shrink happens, loss prevention can be an overwhelming — but vital — task. And just as retailers use AI and analytics to optimize other areas of the retail business, such as forecasting, inventory management and product attribution, the technology can help loss prevention teams gain a view into retail loss that a manual process cannot see. With AI, retailers can improve how they catch shrink and grow their bottom lines.
External Theft, Returns and Leading Drivers of Shrink
To be clear, theft from organized retail crime (ORC) groups and sticky-fingered shoppers is a significant concern for retailers. Specifically, fraudulent and abusive retail returns are increasingly impacting retailers.
In fact, Deloitte and Appriss Retail reported that returns in 2024 totaled $685 billion in merchandise, with $103 billion being tied to return and claims fraud. The returns study reviewed transaction data from 60 of the top 100 US retailers, uncovering that returns represent 13.21% of total retail sales, and 15% of all returns are fraudulent.
ORC syndicates are abusing online returns systems, where buy online return in-store (BORIS) and buy online return online (BORO) already combine for more than half of all returns, per the Deloitte and Appriss Retail study. The National Retail Federation also reported that more than three-fourths of its members deemed ORC as the fastest-growing type of theft impacting their businesses.
What’s more, the Department of Homeland Security estimated that ORC costs federal and state governments nearly $15 billion in lost tax revenue, roughly factoring into an estimated $500 in additional costs passed onto American families annually.
Retailers need to become more vigilant and sophisticated in reducing incidents of ORC, but they also need to become savvier in uncovering total retail shrink. In some cases, up to 80% of losses come from non-fraud shrink, creating a massive opportunity to regain wasted dollars.
Related Article: How AI Fights $103 Billion in Retail Returns Fraud: Predictive & Generative Solutions
Nontraditional Loss: Identifying Various Types of Shrink
AI-powered analytics and generative AI tools working inside a loss prevention strategy enable retailers to expand the view of where retail loss is occurring. While theft and crime are leading causes of shrink, additional types of loss include:
- Food waste or spoiled perishable goods
- Pricing errors on outdated signage or within the system
- Damaged products on-shelf
- Consumers abusing a coupon or discount
- Errors made during administrative tasks
Retailers that integrate AI into their returns and POS systems and use it to help analyze transactional data can gain a 360-degree view into the business and pinpoint where loss is occurring.
For example, with retail returns, AI can uncover the root cause of an issue for a brand team to dig deeper into. Consider a home retailer that’s selling a new crystal vase, a loss prevention team using AI can spot that a high number of the vases are being returned. Further analysis discovers that the vases are cracking during online shipments, so the company can investigate how to strengthen the product’s packaging, the product itself or how it’s being shipped. The insights immediately lead to saving money from damaged items and reduce shrink.
Retailers with total visibility into how their returns are being handled can uncover areas of loss that go beyond just claims and fraud abuse. However, to gain total visibility, and to enable AI-powered analytics to uncover where money’s going out the window, retailers require a unified, streamlined tech strategy.
Unified Data and Systems Support AI
Giving loss prevention teams eyes into the unseen parts of retail loss calls on data, systems and multiple points of contact to come together. It’s building a holistic approach to the business that enables AI and retail analytics to thrive.
It starts with creating streamlined data pipelines. End-to-end systems perform best when clean and enriched data is continually being fed into the solutions. This means information about sales, inventory, assortments and shopper transactions — both online and offline — should be integrated, enriched and funneled to a centralized data repository, enabling loss prevention teams to use AI to uncover losses and make faster business decisions.
AI can assist fraud and reverse logistics teams by analyzing transaction data, shopper history insights and returns data, developing strategic recommendations that identify issues of returns fraud, trends around damaged products, administrative errors and more. Generative AI, specifically, can also step in to help analysts identify and study new behaviors, which can be fed back into AI models, strengthening visibility into loss.
To enable a holistic loss prevention strategy, retailers should develop strategic partnerships that enhance visibility across their entire transaction and returns lifecycle. By integrating fraud signals from a Card Not Present (CNP) provider, a returns and claims authorization solution, a returns management solution and a reverse logistics company, retailers can create a unified, 360-degree view of their operations and strengthen their ability to prevent fraud, theft and abuse.
Related Article: 5 AI Case Studies in Retail
AI Provides Deeper Visibility Into Loss Prevention
AI models and integrated, streamlined solutions support loss prevention teams by uncovering where loss is happening online and in stores. It’s a data-driven defense against fraud and non-traditional types of shrink.
Retailers can optimize retail returns, deploying AI solutions to analyze return data across all channels that recommend whether to approve or decline a return. Loss prevention teams can also further leverage retail analytics to monitor incidents of fraud and abuse, using generative AI capabilities to connect cases that could lead to the discovery of ORC groups.
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Retailers have powerful tools available to them to protect profits and combat shrink. By combining data pipelines to integrate data sources and building focused AI models, retailers can move beyond reactive loss prevention and build proactive, 360-degree solutions that deliver real-time alerts and actions, reducing shrink and safeguarding profits.
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