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

Claim 12:
13. A non-transitory machine-readable medium containing instructions, which when executed by a processor, cause the processor to perform operations, the operations to:
receive, via an interface of a mobile device, a customer identification to associate the mobile device to a pretrained model, the pretrained model is trained to identify fraudulent transactions;
receive, via the interface of the mobile device, a cluster identification to associate the mobile device to a first cluster that comprises a plurality of customers, wherein a first customer associated with the mobile device is one of the plurality of customers;
receive, via the interface of the mobile device, transaction data for an anonymized customer associated with the first cluster, the transaction data to describe a purchase made by the anonymized customer;
determine, by the model based on the transaction data, a vote indicating that 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 first cluster;
communicate, in response to a receipt of the transaction data, a message comprising the vote to indicate that the purchase is fraudulent or that the purchase is non-fraudulent based on the transaction data, the message to identify the transaction data with encoded data; and
train the model with the transaction data, based on receipt of an indication of whether the purchase is fraudulent or non-fraudulent based on a plurality of votes from a plurality of customer devices, wherein training of the model includes:
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.