Abstract: Systems and methods for smart remediation of transactions are disclosed. A payment processing system receives information of a first transaction with a merchant. The system approves the first transaction and identifies a monitoring period which may be a set amount of time after the approval of the first transaction. The system collects data of one or more second transactions during the monitoring period, and provides the data to a machine learning model. The machine learning model determines a first fraud score indicative of fraud for the first transaction. A fraud threshold may be identified based on one or more merchant profiles. If the first fraud score is above the first fraud threshold, a refund may be provided for the first transaction.

Abstract: A method and apparatus for hard deletion of user data are described. The method may include receiving a request from a user computer system to delete user data. The method may also include determining a unique user identifier associated by a system with a user making the request. The method may also include determining whether a data partition, in which data generated by a job or subsystem of the computer system is stored, is predicted to contain a record having the unique user identifier. Then, the method may include searching, when the data partition is predicted to contain a record having the unique user identifier, data records stored in the data partition for a user data record based on the unique identifier, and performing a hard deletion of the user data record from the partition when found during the searching.

Abstract: A method and apparatus for authenticating a transaction are described. The method may include: receiving, at an authentication device, an electronic transaction request as part of authenticating a card-originated transaction between a merchant and a consumer; determining, based on characteristics of the electronic transaction request, a risk value associated with the electronic transaction request; selecting a first identification value or a second identification value based on a comparison of the risk value to a risk threshold, wherein the first identification value indicates that the risk value exceeds the risk threshold and the second identification value indicates that the risk value does not exceed the risk threshold; generating a communication to transmit to an authorization device, the communication comprising a first data field having the selected first identification value or the selected second identification value; and transmitting the communication to the authorization device.

Abstract: A method and apparatus for hard deletion of user data are described. The method may include receiving a request from a user computer system to delete user data. The method may also include determining a unique user identifier associated by a system with a user making the request. The method may also include determining whether a data partition, in which data generated by a job or subsystem of the computer system is stored, is predicted to contain a record having the unique user identifier. Then, the method may include searching, when the data partition is predicted to contain a record having the unique user identifier, data records stored in the data partition for a user data record based on the unique identifier, and performing a hard deletion of the user data record from the partition when found during the searching.

Abstract: A method and apparatus for fraud detection during transactions using identity graphs are described. A method includes receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method also includes determining, by the commerce platform system, an identity associated with the user associated with additional transaction attributes not received with the transaction. Furthermore, the method includes accessing a feature set associated with the initial transaction attributes and the additional transaction attributes that includes machine learning (ML) model features for detecting transaction fraud.

Abstract: A method and apparatus for fraud detection during transactions using identity graphs are described. The method may include receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method may also include determining, by the commerce platform system, an identity associated with the user, wherein the identity is associated with additional transaction attributes not received with the transaction. Furthermore, the method may include accessing, by the commerce platform system, a feature set associated with the initial transaction attributes and the additional transaction attributes, wherein the feature set comprises machine learning (ML) model features for detecting transaction fraud.

Abstract: A method and apparatus for performing privacy preserving fraud detection in network based transactions are described. The method may include receiving a fraud detection message during a transaction between a user system and a merchant system, the message having a set of cryptographically transformed universal resource locator (URL) components generated from a URL of a web page of the merchant system on which the transaction is to occur. The method may also include generating one or more secure and anonymous fraud detection features, each fraud detection feature comprising a select subset of the cryptographically transformed URL components. The method may also include performing fraud detection for the web page using the one or more secure and anonymous fraud detection features to determine a likelihood that fraud is occurring in the transaction.

Abstract: A method and apparatus for hard deletion of user data are described. The method may include receiving a request from a user computer system to delete user data. The method may also include determining a unique user identifier associated by a system with a user making the request. The method may also include determining whether a data partition, in which data generated by a job or subsystem of the computer system is stored, is predicted to contain a record having the unique user identifier. Then, the method may include searching, when the data partition is predicted to contain a record having the unique user identifier, data records stored in the data partition for a user data record based on the unique identifier, and performing a hard deletion of the user data record from the partition when found during the searching.