Abstract: The features relate to artificial intelligence directed detection of user behavior based on complex analysis of user event data including language modeling to generate distributed representations of user behavior. Further features are described for reducing the amount of data needed to represent relationships between events such as transaction events received from card readers or point of sale systems. Machine learning features for dynamically determining an optimal set of attributes to use as the language model as well as for comparing current event data to historical event data are also included.

Abstract: A transaction in a sequence of transactions is received and a transaction group with which the transaction is associated is determined, as well as transaction group signature data of the transaction group with which the received transaction is associated. A transaction score for the received transaction is generated, in response to determining that the received transaction is a last transaction in the transaction group with which the received transaction is associated, and a transaction group score is generated for the transaction group with which the received transaction is associated, such that the transaction group score is generated based on all transactions in the sequence of transactions that are associated with the transaction group.