US 12,373,670 B2
Interleaved sequence recurrent neural networks for fraud detection
Bernardo José Amaral Nunes de Almeida Branco, Lisbon (PT); Pedro Caldeira Abreu, London (GB); Ana Sofia Leal Gomes, Lisbon (PT); Mariana S. C. Almeida, Lisbon (PT); João Tiago Barriga Negra Ascensão, Lisbon (PT); and Pedro Gustavo Santos Rodrigues Bizarro, Lisbon (PT)
Assigned to Feedzai—Consultadoria e Inovação Tecnológica, S.A., (PT)
Filed by Feedzai—Consultadoria e Inovação Tecnológica, S.A., Coimbra (PT)
Filed on Apr. 16, 2024, as Appl. No. 18/637,142.
Application 18/637,142 is a continuation of application No. 17/174,046, filed on Feb. 11, 2021, granted, now 11,989,643.
Claims priority of provisional application 62/975,614, filed on Feb. 12, 2020.
Prior Publication US 2024/0303468 A1, Sep. 12, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/12 (2023.01); G06F 7/08 (2006.01); G06F 16/23 (2019.01); G06F 16/27 (2019.01); G06N 3/042 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/063 (2023.01); G06N 3/08 (2023.01); G06Q 20/40 (2012.01)
CPC G06N 3/045 (2023.01) [G06F 7/08 (2013.01); G06F 16/2379 (2019.01); G06F 16/27 (2019.01); G06N 3/042 (2023.01); G06N 3/044 (2023.01); G06N 3/063 (2013.01); G06N 3/08 (2013.01); G06Q 20/4016 (2013.01); G06Q 20/409 (2013.01); G06Q 40/12 (2013.12)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving data of a first transaction;
retrieving a first state;
providing the retrieved first state and an input data based on the first transaction to a machine learning trained recurrent neural network model to determine a second state including by:
transforming an input vector into a new vector, and
representing the new vector in a space to feed at least one layer of the recurrent neural network model;
determining a prediction result associated with a fraud threat of the first transaction using at least (i) the first state and (ii) the input data based on the first transaction;
updating a saved recurrent neural network state for an entity associated with the first transaction to be the second state;
receiving data of a second transaction, wherein the second transaction is associated with the entity; and
unlooping a neural network associated with the saved recurrent neural network state including by:
retrieving the second state;
providing the second state and an input data based on the second transaction to the machine learning trained recurrent neural network model to determine a third state;
determining a prediction result associated with a fraud threat of the second transaction using at least (i) the second state and (ii) the input data based on the second transaction, wherein the second transaction is approved based at least in part on a determination that the prediction result associated with the fraud threat of the second transaction is below a threshold; and
updating the saved recurrent neural network state for the entity to be the third state.