U.S. retailers lost a combined $61.7 billion to the various components of total retail loss (shrink) in 2019, an $11 billion increase from the previous year that reflected 1.62% of all sales. In this article we will focus on internal theft — a large component of the overall 42 categories of total retail loss.
The damaging financial impact of employee theft continues to grow by the year, and in retail’s ultra-competitive environment, where margins are already tight from pricing wars and COVID-19, every dollar counts toward the bottom line. More bothersome is the additional impact of the inventory distortion fraud creates. A customer who discovers an empty shelf where a product should be, even though systems indicate it should be available (a phenomenon referred to as “phantom inventory”), may never shop with that retailer again.
In a chain drug store, internal fraud can be even more serious, due to the variety of potentially dangerous and/or tightly controlled medications readily accessible to employees. Luckily, preventing it certainly isn’t impossible — with the right technology it can be a seamless part of any security strategy. Using advanced analytics, retailers can leverage already available data to identify suspicious employee activity, identifying and stamping out elusive internal fraud before it slips through the cracks undetected.
Three types of internal fraud you may be missing
One of the most easily missed types of internal fraud is cashier fraud, such as sweethearting. Usually occurring during checkout at the register, sweethearting-type fraud involves a store employee purposefully giving away merchandise to acquaintances, whether through price switching or simply not scanning the product at all. Sweethearting often goes completely undetected, as there are few obvious changes in behavior or activity to raise management’s suspicion.
As the COVID-19 pandemic and social distancing guidelines have resulted in a rise in popularity of self-checkout, especially at drug stores and big-box stores, self-checkout fraud is also becoming an increasing problem. This form of fraud requires tampering with the self-checkout process to steal product or apply an unauthorized discount. A common type of self-checkout fraud involves an employee allowing customers to price switch. Though not quite as simple as sweethearting, self-checkout fraud can still go undetected while costing stores in lost profits and widespread inventory inaccuracies.
Finally, e-commerce fraud takes advantage of retailers’ return and refund policies, both of which have likely been relaxed in the wake of COVID-19. Commonly, this type of fraud involves a customer service representative recruiting an acquaintance to submit false complaints, ranging from receiving the wrong item to not receiving the item at all. Many retailers will compensate the customer without question by offering them a refund, replacement product, gift card or all three. Without proper oversight, these fraudulent complaints can continue unchecked.
All the above examples of internal fraud — sweethearting, self-checkout, and e-commerce fraud — can avoid detection as they often result in minimal losses at a time. However, these acts of fraud can add up over time and seriously impact drug stores’ bottom lines. While they may be too subtle for managers to detect, they can be easily uncovered within a store’s retail data.
Combatting internal fraud with advanced data analytics
Many drug stores have extensive fraud policies in place, but lack visibility to their employees’ actions. The best defense against internal fraud is a drug store’s own retail data. Advanced analytics, such as prescriptive analytics solutions, empower chain drug stores to identify anomalies in data or operations, from multiple sources, that indicate cases of employee fraud. These artificial intelligence (AI)-powered solutions can analyze a variety of information — including inventory levels, revenue, employee activity, customer history and more — to give managers a comprehensive view of their operations, providing much needed answers without disrupting day-to-day store activities. As an added bonus, the best solutions also send action steps to the appropriate stakeholders, directing them how to respond to any potential problems.
As an example, a drug store chain used advanced analytics to uncover and eliminate a widespread internal fraud case. Within the retailer’s own data, the solution identified a disturbing correlation between high-value voids (from POS data) and low supervisor presence (from the HR system) on the sales floor. Essentially, this meant that cashiers were performing more voids when their supervisors were away from the floor. The solution alerted the chain’s asset protection (AP) team to this finding, also sending them CCTV footage of the exact times the suspicious voids were conducted.
The AP team quickly identified a group of cashiers participating in a sweethearting ring by voiding and passing off high-value merchandise to their friends and families. The ring was quickly broken up and restitution of more than $50,000 was paid.
Advanced analytics solutions are most impactful when incorporated into overall security strategies. This level of consistent and dynamic monitoring is key to dissuading employees from committing fraud in the first place. Advanced data analytics solutions can even detect internal fraud before management has a reason to suspect it. Rather than allowing the fraud to continue for months unnoticed, costing the store revenue, inventory and time, advanced data analytics can automatically identify indicators of internal fraud for a swift resolution.
Take a stand against internal fraud through smart analytics
Drug store owners and managers don’t want to believe their employees are purposefully committing illegal acts of fraud. Implementing the right data-driven technologies can take biases out of the situation, leaving only the facts. Leveraging this smart technology can be a critical part of any chain drug store’s fraud prevention strategy. Advanced data solutions give pharmacies and stores the granular insights they need to fight back against internal fraud, protecting their margins and their reputations.
Guy Yehiav is the general manager and vice president of Zebra Prescriptive Analytics.