In 2019, the number of employee theft cases increased by an average of 560 apprehensions per retailer surveyed (up from 323 last year).1 According to NRF, retail shrink totaled $61.7 billion in 2019 amid rising employee theft and shoplifting/ORC. Many types of employee fraud are invisible to traditional accounting methods. They can, however, be identified by analyzing deviations from key performance indicators and statistical norms. With the average dollar loss of employee theft averaging 4.2 times greater than shoplifting, retailers need an exception-based reporting tool to quickly identify suspicious trends, transactions, and other data anomalies.
Based on our customers’ experience and our analytics, what follows in our guidebook, Top 10 Internal Theft Offenses, is a list of the most common employee fraud risks and a look at how Oracle Retail XBRi Loss Prevention Cloud Service can address those risks.
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Check out the full guide, Top 10 Internal Theft Offenses
A highlight of working in retail loss prevention (LP) is the evolution of technologies as companies seek the next big thing that will help reduce shrink. Artificial Intelligence (AI) and Machine Learning (ML), unlike some past buzzwords, are living up to their hype. To understand how AI and ML change the LP analytics approach, it’s important to consider the current landscape.
Oracle has a dedicated team of data scientists who are developing the next generation of retail analytics that incorporates rules-based approaches along with the industry expertise of our retail community, all leveraging the analytical power of AI and ML. Machine learning here is not a replacement for the rules-based approaches but rather works in concert, and in fact, the rules are an integral part of training some of the machine-learning models.
1NRF: Retail shrink totaled $61.7 billion in 2019 amid rising employee theft and shoplifting/ORC