Abstract: This application relates to apparatus and methods for identifying anomalies in item sales. The anomalies may be caused by, for example, wrongly priced items. In some examples, a computing device receives transaction data identifying the purchase of one or more items from a store or website, for example. The computing device applies a rule-based model to the transaction data to generate, for each item, a first value. The first value may be based on a number of rules violated. The computing device may also apply, for each item, a machine learning based model to the transaction data and to the first value to generate a second value. The second value may indicate a probability of an anomaly. The computing device may determine that an anomaly exists for an item based on the first value and the second value for the item, and may transmit an indication of the anomaly.