The Rise of Pandemic-Driven Retail Fraud: How Brands Are Leveraging Prescriptive Analytics to Fight Back
With margins already tightened by the financial impact of the COVID-19 pandemic, the oft-undetected but constant siphon of funds caused by fraud is devastating for retailers. A recent study revealed the cost of fraud to U.S. retailers and e-commerce companies rose 7 percent in 2020, with every $1 of fraud costing companies $3.36 in losses.
By leveraging prescriptive analytics, retailers are relying on powerful data-driven software to identify instances of fraud and noncompliance, recommend corrective action, and prevent it from happening again both in stores and online.
Minimizing Brick-and-Mortar Fraud
To create a safe and healthy in-store shopping experience during the pandemic, many retailers updated their operations and processes to include social distancing measures. They enhanced self-checkout offerings, which minimized employee presence on the floor. While these measures were critical to supporting community health, they have unfortunately opened the door to fraud committed by employees and customers alike
Less oversight on the floor has allowed many cashiers to commit “sliding” fraud with minimal risk of detection. In this instance, the cashier passes an item over the scanner while secretly covering its barcode, either with a finger or with the barcode of a cheaper product, like a $1 pack of gum. To bring these internal crimes to light, prescriptive analytics “clusters” cashiers with similar characteristics to find the average performance data to calculate their hourly scan-rate benchmarks. Should an individual’s scan rate drop below this cluster benchmark, a manager is instantly alerted to this suspicious behavior with CCTV footage combined with the detailed receipt from the exact time and place the incident occurred, enabling a swift resolution.
Self-checkout has greatly reduced the potential for person-to-person virus transmission, but retailers must be careful; it’s easy for individuals to swap price tags on items to pay a substantially lower cost at self-checkout or just “miss” a scan by “mistake.” With fewer managers on the floor, it usually goes unnoticed. Prescriptive analytics automatically monitors inventory levels and cross references the data against actual sales, sometimes in combination with loyal customer information. Should a significant discrepancy be identified, the software alerts an appropriate stakeholder and advises next steps, ranging from retraining the employee in question to taking legal action against perpetrators.
Fighting Back Against E-Commerce Fraud
Just as consumer behavior trends resulted in the rise of self-checkout, they also caused an explosion in e-commerce shopping. In 2020, e-commerce spending reached an incredible $794.50 billion worldwide — $100 billion higher than originally forecasted.
However, the surge in online shopping, often overwhelming retailers’ shipping and customer service staff, has made catching fraud even more difficult, especially online return fraud. One common example involves customers filing a false complaint about a damaged or missing e-commerce item. They're then compensated with a refund, replacement item and/or gift card as an “appeasement,” ultimately costing the retailer money for a nonexistent issue. If left undetected, losses can hit staggering rates.
Prescriptive analytics utilizes employee performance data to establish benchmarks for customer service representatives’ (CSRs) order replacement frequency. Machine learning capabilities then monitor CSR activity to flag any anomalies in the rate of online returns, which allows management to intervene when needed. This empowers retailers to detect fraudulent return claims while still carrying out authentic returns to maximize customer satisfaction.
Every dollar counts for retailers fighting to bounce back from the pandemic. By implementing prescriptive analytics, retailers no longer need to guess where fraud is occurring or hope they're taking the right measures to push back. Instead, they're identifying exactly where and how fraud is hurting their business and leveraging the right combination of tools and data to stop it.
Related story: Delivering on Retail's Future...With the Help of Analytics
Guy Yehiav is responsible for setting the organic and nonorganic growth, leadership strategy, and customer success for the Zebra Analytics business unit.
As a leader in enterprise AI solutions at the edge, with 25+ years of experience driving profits with data and IoT in the retail and the supply chain industries, Guy oversees the corporate strategy, direction and success of Zebra Analytics at Zebra Technologies. He was previously the CEO of Profitect, where he guided the company through multiple years of significant growth, including 182% revenue growth and 137% headcount growth from 2018-2019, before being acquired by Zebra Technologies. This was his second exit for his shareholders, after growing the Demantra supply chain optimization software company and selling it to Oracle. He has lead companies that keep complexity at the back end and simplicity at the front end, cultivating machine learning and smart decision-making based on data to deliver stellar results for customers.