Retail fraud can range from fraud in returns or abuse of customer service, or credit risk for larger purchases, based on, for example, uncovering fraud rings, social media activity of customers and detecting patterns. It can also be major security breaches putting private customer information at risk. Retailers need to protect their margins and their reputations by proactively detecting fraudulent activities. fraXses can help retailers identify anomalies and patterns by putting in place continuous monitoring tactics that look for unusual patterns in product and inventory movement. This can help indicate incidents of fraud such as shrink and store associate theft and look for exceptions. Over time, models can be built that utilise machine learning and provide more predictive capabilities that can trigger actions when exceptions are encountered.