Machine learning system

A method of processing data associated with a proposed transaction associated with an entity, the method comprising:

• processing, using a time-decay cell module, at least some of said data corresponding to the incoming transaction, the processing comprising:
• storing, in a first memory of the time-decay cell module, first state data corresponding to an immediately preceding transaction;
• modifying, using a first decay logic stage of the time-decay cell module, the first state data stored in the first memory based on a time difference between a time of the incoming transaction and a time of the immediately preceding transaction;
• determining, using a first neural network stage of the time-decay cell module, next first state data using previous first state data modified by the first decay logic stage and a first input tensor derived from the incoming transaction;
• storing, using the first neural network stage of the time-decay cell module, the next first state data in the first memory for a next transaction;
• and
• mapping the modified first state data to generate output data;
• processing, using a categorical history module, at least some of said data corresponding to the incoming transaction, the processing comprising:
• storing, in a second memory of the categorical history module, second state data for a plurality of categories indexed by a respective category identifier, wherein the second state data stored in the second memory for each category identifier corresponds to a previous transaction associated with the entity and the respective category;
• modifying, using a second decay logic stage of the categorical history module, second state data stored in the second memory based on a time difference between a time of the incoming transaction and a time of a previous transaction, wherein the step of modifying the second state data comprises:
• when the second memory contains second state data for the entity and category identifier pair associated with the incoming transaction, retrieving said second state data and applying a decaying function to the second state data to generate a decayed version of the second state data, wherein the decaying function is dependent on the time difference, and outputting the decayed version of the second state data;
• updating, using an update logic stage of the categorical history module, the second state data output using an second input tensor generated from the data corresponding to the incoming transaction to generate updated second state data;
• storing, using the update logic stage of the categorical history module, said updated second state data in the second memory for the entity and category identifier pair associated with the incoming transaction;
• and
• outputting, using the update logic stage of the categorical history module, an output tensor comprising the updated second state data;
• mapping the output data from the time-decay cell module and the output tensor from the categorical history module to a scalar value representative of a likelihood that the incoming transaction presents an anomaly within a sequence of actions;
• and
• using the scalar value to determine whether to approve or decline the incoming transaction.