Abstract: A machine learning system for processing data corresponding to an incoming transaction. The machine learning system comprises a categorical history module which includes a memory configured to store state data for a plurality of categories indexed by a respective category identifier, wherein the state data stored in the memory for each category identifier corresponds to a previous transaction associated with the entity and the respective category. A decay logic stage is configured to modify state data stored in the memory based on a time difference between a time of the incoming transaction and a time of a previous transaction. When the memory contains state data for the entity and category identifier pair associated with the incoming transaction, a decaying function dependent on the time difference is applied to the stored state data to generate a decayed version.

Abstract: A method of training a supervised machine learning system to detect anomalies within transaction data is described. The method includes obtaining a training set of data samples; assigning a label indicating an absence of an anomaly to unlabelled data samples in the training set; partitioning the data of the data samples in the training set into two feature sets, a first feature set representing observable features and a second feature set representing context features; generating synthetic data samples by combining features from the two feature sets that respectively relate to two different uniquely identifiable entities; assigning a label indicating a presence of an anomaly to the synthetic data samples; augmenting the training set with the synthetic data samples; and training a supervised machine learning system with the augmented training set and the assigned labels.

Abstract: A data item is searched for in first and/or second data sets of a data store. If it is found in the first data set and if it was updated in the first data set after the second data set became an overlay of the first data set, first data stored in association with the data item in the first data set is returned. If it is found in the first and second data sets and if the second data set became an overlay of the first data set after the data item was updated in the first data set, second data stored in association with the data item in the second data set is returned. The second data set is identified based on overlay metadata, indicative of the second data set being an overlay of the first data set.

Abstract: A transaction processing system includes a transaction processing module configured to receive first information associated with a first proposed transaction, retrieve second information associated with at least one prior transaction that is associated with the first proposed transaction, and calculate a time-decayed algorithm using the second information to generate third information. The transaction processing system also includes a weighting module communicably coupled to the transaction processing module, wherein the weighting module is configured to receive the third information from the neural-based processing module, apply a weighting factor to the third information to generate fourth information, and calculate at least one processing algorithm using the first information and the fourth information to generate an output. The output of the weighting module is used by an additional transaction processing module to determine whether the first proposed transaction is fraudulent.

Abstract: A method of training a supervised machine learning system to detect anomalies within transaction data is described. The method includes obtaining a training set of data samples; assigning a label indicating an absence of an anomaly to unlabelled data samples in the training set; partitioning the data of the data samples in the training set into two feature sets, a first feature set representing observable features and a second feature set representing context features; generating synthetic data samples by combining features from the two feature sets that respectively relate to two different uniquely identifiable entities; assigning a label indicating a presence of an anomaly to the synthetic data samples; augmenting the training set with the synthetic data samples; and training a supervised machine learning system with the augmented training set and the assigned labels.