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.

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SOLUTIONS SHOWCASE APPRISS RETAIL Overview of Articial Intelligence and Machine Learning Artificial intelligence can cover a wide range of tasks. The next diagram illustrates some frequently used components of AI. Predictive Models and Machine Learning. Predictive models and machine learning are shown in the diagram as almost completely overlapping circles and are very close in definition and purpose. The primary difference between the two is machine learning originated in computer science, whereas predictive modeling was developed in the field of statistics. In the past decade or more, the two fields have essentially merged and now share a highly overlapping definition. Both are concerned with using data to predict or model outcomes, and both are tools used in AI. Deep Learning. Deep learning is a process that uses highly complex model structures to learn patterns in the raw data without any human intervention, thus eliminating the need for derived variables. The difficulty in deep learning is that the more complex models are often more difficult to fit accurately. Dynamic Learning. There are two very different types of model categories: static models and dynamic models. A static model is fit to a static pattern in data. The predictions from a static model can change as the data it is presented with changes, but if the model is presented with the same exact data, it will produce the same exact answer. By contrast, a dynamic model's prediction can change from day to day even if it is presented with the same information. Dynamic learning is a process by which models are refit or updated dynamically as new data presents itself. This approach is useful when a system is continually bombarded with additional information and the relationships in the data are dynamic and the model itself needs to change to adapt. Collective Intelligence Machine-learning models allow developers to use data from many retailers to create a more powerful solution, and Appriss Retail's worldwide retailer base is a significant benefit. Each retailer is unique in the exception-based approaches and investigation techniques that result in employee terminations and prosecutions. Combining retailer strategies leads to better detection, including: Q Variables resulting from shared questions Q Models built on many successful strategies Using Dynamic Learning and Feedback Beyond the collective-intelligence model described in the prior section, a final objective of an AI-based employee-deviance detection system is a feedback loop from the investigations back into the models. During an investigation, the investigator will perform many actions that can be captured in a system and later used to improve system accuracy. For example, the investigator will click on related transactions and individuals, will open a case, will abandon a case or a transaction, and so on. The captured usage activity can be converted into information that a machine-learning model can utilize to improve the models. This type of feedback loop ensures that as new fraud schemes are detected at one retailer, that pattern is captured in a broader model that propagates across all retailers. The Ultimate Objective As stated above, the ultimate objective for developing machine learning models is to combine many learnings and obtain collective intelligence to curb suspicious employee behavior. In this application of machine learning, a department store chain saw at least a 25 percent increase in fraud-related terminations above what would normally be captured by EBR alone. Visit apprissretail.com to learnˆmore. Employee-Based Variables 3,000+ Cases to Investigate Feedback loop from investigations improves models and keeps up with new fraud Machine Learning / Predictive Models Known Successful Investigations Across Many Retailers + Artificial Intelligence Machine Learning Predictive Models Dynamic Learning Deep Learning Self Driving Cars, Voice Recognition, Text Recognition 58 SEPTEMBER–OCTOBER | LOSSPREVENTIONMEDIA.COM

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