Abstract: Systems and methods for aggregating commercial transaction information from a plurality of merchants, and evaluating them utilizing machine learning and artificial intelligence algorithms are disclosed. The commercial transaction information is parsed, aggregated, and evaluated based on patterns. One or more fraud clusters are generated based on the recognized patterns. The fraud clusters are utilized to generate a predictive fraud score for a transaction initiated by a customer. Interpretation of the predictive fraud score by a merchant allows the merchant to determine whether to allow the transaction to be completed or not by the customer.

Abstract: Systems and methods for aggregating commercial transaction information from a plurality of transaction systems of merchants, and evaluating the aggregated information utilizing machine learning and artificial intelligence algorithms are disclosed. The commercial transaction information is parsed, aggregated, and evaluated based on patterns recognized by the artificially intelligent system. One or more fraud clusters are generated based on the recognized patterns. The fraud clusters are utilized to generate a predictive fraud score for a transaction initiated by a customer. Interpretation of the predictive fraud score by a transaction system of a merchant allows for a determination as to whether the transaction initiated by the customer is likely a legitimate transaction or a fraudulent transaction.