Identifying false positive geolocation-based fraud alerts

    公开(公告)号:US11334894B1

    公开(公告)日:2022-05-17

    申请号:US15466014

    申请日:2017-03-22

    Abstract: In a computer-implemented method of using customer data to determine that geolocation-based fraud alerts are false positives, it may be determined that an electronic fraud alert is a geolocation-based alert generated based upon an unexpected or abnormal transaction location. In response, customer data may be obtained from two or more sources via radio frequency links. It may then be determined that the customer data from the sources indicates that a customer is traveling. In response, it may be determined that a customer location indicated by the customer data corresponds to the transaction location. In response to determining that the customer location corresponds to the transaction location, the electronic fraud alert may be marked as a false positive, and the electronic fraud alert may be prevented from being transmitted to a mobile device of the customer, in order to reduce an amount of false positives that are transmitted to customers.

    Identifying chargeback scenarios based upon non-compliant merchant computer terminals

    公开(公告)号:US11037159B1

    公开(公告)日:2021-06-15

    申请号:US15465858

    申请日:2017-03-22

    Abstract: A method of identifying a merchant computer terminal warranting a chargeback includes identifying a merchant computer terminal associated with a fraudulent financial transaction, and receiving information associated with the merchant computer terminal. The information may include an actual configuration, parameters, and/or specifications associated with the merchant computer terminal. The method also includes receiving up-to-date dispute rules associated with chargebacks, and analyzing the dispute rules to determine merchant computer terminal requirements. The method further includes comparing the terminal requirements with the information associated with the merchant computer terminal to identify whether the merchant computer terminal is compliant with the dispute rules. Based upon the comparison, an electronic notification may be generated that indicates whether a chargeback is warranted due to the merchant computing terminal being non-compliant with the dispute rule, and the electronic notification may be transmitted to a merchant computing device to facilitate resolving financial transaction disputes.

    Identifying fraudulent online applications

    公开(公告)号:US10825028B1

    公开(公告)日:2020-11-03

    申请号:US15465874

    申请日:2017-03-22

    Abstract: A method of using browsing activity to identify fraudulent online or virtual applications includes receiving a virtual application over one or more radio frequency links, determining an applicant name on the virtual application, determining an IP address of a source computer from which the virtual application originated, determining an online browsing or search history associated with the IP address, determining whether the online browsing or search history indicates recent Internet searches for the applicant name, and, in response to determining that the online browsing or search history does indicate recent Internet searches for the applicant name, flagging the virtual application as fraudulent and generating an electronic alert indicating that the virtual application is fraudulent to facilitate identifying fraudulent virtual applications for goods or services.

    HEURISTIC CREDIT RISK ASSESSMENT ENGINE

    公开(公告)号:US20250005661A1

    公开(公告)日:2025-01-02

    申请号:US18884722

    申请日:2024-09-13

    Abstract: A heuristic engine includes capabilities to collect an unstructured data set and a current business context to calculate a credit worthiness score. Providing a heuristic algorithm, executing within the engine, with the data set and the context may allow determination of predicted future contexts and recommend subsequent actions, such as assessing a credit risk of a customer transaction and reducing the risk of customer transactions by processing the available data. Such heuristic algorithms may learn from past data transactions and appropriate correlations with events and available data.

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