LP Magazine

SEP-OCT 2018

LP magazine publishes articles for loss prevention, asset protection, and retail professionals covering shrinkage, investigations, shoplifting, internal theft, fraud, technology, best practices, and career development.

Issue link: http://digital.lpportal.com/i/1030193

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Page 57 of 68

SOLUTIONS SHOWCASE APPRISS RETAIL Machine Learning and the Evolution of Exception Reporting R ecent years have seen an explosion of consumer products that incorporate artificial intelligence (AI). Self-driving cars, Alexa, Siri, chatbots, Google search, and smart traffic lights are examples of how artificial intelligence is impacting the world. While artificial intelligence has gained mainstream attention recently, AI applications have been "hidden" for many years in a variety of business applications, such as credit scores, credit card fraud detection, direct marketing, and more. Retail loss prevention has also seen applications of AI in technologies such as facial recognition and return fraud authorization. In this article, we extend the loss prevention professionals' toolkit and show the results of a machine-learning algorithm (a specific type of AI) developed to detect fraudulent and abusive employee activity. The ultimate objective for developing machine-learning models is to obtain collective intelligence to curb employee deviance. Appriss Retail serves more than ninety retailers worldwide, and this experience provides a solid foundation for such model development. From Rules and Queries to Predictive Models and Machine Learning Today, most loss prevention departments use traditional exception-based reporting (EBR) tools to monitor their employees by running queries on point-of-sale data that flags suspicious activities. EBR tools used by retailers are similar to the tools used by the credit card industry in the 1980s to detect fraud. In the early 1990s, the credit card industry mostly abandoned the EBR approach in favor of methods of detection based on predictive modeling and machine learning. A similar trend was seen with health-care fraud about fifteen years ago. Our prediction is that retail loss prevention will evolve in a manner comparable to credit card fraud detection. In a traditional EBR process, a query is run to look for anomalous behavior, and its output is provided to an analyst in the form of a list of transactions or employees to investigate. Then the analyst or investigator reviews the exceptions presented and determines, sometimes based on looking at additional information, if the transaction or employee in question warrants further review. Some of the exceptions will result in an action against the employee, such as arrest or termination of employment, which we label as "success" in the flow chart below. The goal of machine learning is to apply a complex mathematical model to the data and learn, using thousands of variables, the most useful elements in an employee's transactions for identifying cases that are most likely to result in an action. With a machine-learning model, the top-ranked exceptions will have a much higher rate of success than those that are generated from traditional EBR. Over time, an approach with a higher success rate will make loss prevention departments far more efficient and will decrease employee fraud rates. "In our testing, we saw more than a 25 percent increase in fraud cases identied and a 25 percent decrease in time required to track down the fraudulent activity within many of the cases." – Department Store Divisional VP of Loss Prevention Investigation/ Review of Exceptions Success Found Something to Correct/Termination Failure Found Nothing to Correct Machine learning exploits the differences between successes and failures EBR Query on POS Data Query Based Investigations Machine Learning Based Investigations 57 LP MAGAZINE | SEPTEMBER–OCTOBER 2018

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