Abstract: A process for handling interleaved sequences using RNNs includes receiving data of a first transaction, retrieving a first state (e.g., a default or a saved RNN state for an entity associated with the first transaction), and determining a new second state and a prediction result using the first state and an input data based on the first transaction. The process includes updating the saved RNN state for the entity to be the second state. The process includes receiving data of a second transaction, where the second transaction is associated with the same entity as the first transaction. The process unloops an RNN associated with the saved RNN state including by: retrieving the second state, determining a new third state and a prediction result using the second state and an input data based the second transaction, and updating the saved RNN state for the entity to be the third state.

Abstract: Computer memory management during real-time fraudulent transaction analysis is disclosed. In an embodiment, a method includes receiving a transaction entry and determining whether the transaction entry is potentially fraudulent, including by determining whether the transaction entry is correlated with another transaction entry in the computer memory or an already-identified suspicious pattern of other transaction entries. The method further includes determining whether to evict the transaction entry from the computer memory based on the determination of whether the transaction entry is potentially fraudulent. The method includes providing a result of fraudulent transaction analysis performed using the computer memory that has been optimized.

Abstract: Techniques described herein include determining, for each account of several accounts, an element blame score (EBS) for each potential point of compromise (POC) of a plurality of POCs. Determining the EBS includes: representing the determinations as a computer computational matrix operation, dividing a matrix of the operation into blocks, and distributing the blocks among nodes for distributed computation. The method further includes determining an overall compromise blame score (OCBS) for each potential POC based on the determined EBS and determining whether the OCBSs have converged. If the OCBSs have converged, select at least one of the potential POCs as a likely POC based at least on the corresponding OCBS of the selected likely POC. If the determined OCBSs have not converged, updating the OCBS for each potential POC and the EBSs for each potential POC.

Abstract: Techniques described herein include determining, for each account of several accounts, an element blame score (EBS) for each potential point of compromise (POC) of a plurality of POCs. Determining the EBS includes: representing the determinations as a computer computational matrix operation, dividing a matrix of the operation into blocks, and distributing the blocks among nodes for distributed computation. The method further includes determining an overall compromise blame score (OCBS) for each potential POC based on the determined EBS and determining whether the OCBSs have converged. If the OCBSs have converged, select at least one of the potential POCs as a likely POC based at least on the corresponding OCBS of the selected likely POC. If the determined OCBSs have not converged, updating the OCBS for each potential POC and the EBSs for each potential POC.