Patent ID: 11875351
Assignee: CAPITAL ONE SERVICES, LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 6:
7. A computing implemented method, comprising:
receiving, by a mobile device, from a server, a customer identification to associate the mobile device to a pretrained model, the pretrained model is trained to identify fraudulent transactions using fraud detection logic circuitry of the mobile device;
receiving, from the server, a cluster identification to associate the mobile device to a cluster that comprises a plurality of customers, wherein a first customer associated with the mobile device is one of the plurality of customers;
receiving, by the mobile device comprising fraud detection logic circuitry, transaction data for an anonymized customer associated with the cluster the transaction data to describe a purchase made by the anonymized customer;
determining, by the fraud detection logic circuitry applying the model at the mobile device, a vote indicative of whether the purchase is fraudulent or that the purchase is non-fraudulent, wherein the model is pretrained to detect fraudulent transactions based on a purchase history of multiple customers in the plurality of customers in the cluster;
communicating, in response to a receipt of the transaction data, a response comprising the vote to indicate that the purchase is fraudulent or non-fraudulent based on application of the model by the mobile device to the transaction data, the response to identify the transaction with encoded data; and
updating the model, by the fraud detection logic circuitry, based on an indication from the server, wherein the server determines that the purchase is fraudulent or that the purchase is non-fraudulent based on a plurality of votes from a plurality of customer devices, wherein updating the model comprises training the model with the transaction data, wherein pretraining and training of the model include:
randomly selecting a plurality of transactions associated with the multiple customers, including receiving another transaction data associated with the plurality of transactions from a plurality of customer devices associated with the plurality of customers in the first cluster;
modifying and encrypting at least a portion of each received another transaction data associated with the plurality of transactions; and
executing at least one of supervised or unsupervised training of the model using the modified and encrypted at least a portion of each another transaction data associated with the plurality of transactions.