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    Automated fraud classification using machine learning
    27.
    发明授权
    Automated fraud classification using machine learning 有权

    公开(公告)号：US11170375B1

    公开(公告)日：2021-11-09

    申请号：US15465842

    申请日：2017-03-22

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Timothy Kramme ,  Elizabeth Flowers ,  Reena Batra ,  Miriam Valero ,  Puneit Dua ,  Shanna L. Phillips ,  Russell Ruestman ,  Bradley A. Craig

    IPC: G06Q20/40 ,  G06N20/00 ,  G06N5/04

    Abstract: A method of automating a fraud classification process includes generating or updating fraud classification rules, at least by training a machine learning program using fraud classifications of a plurality of financial accounts and financial transaction data associated with those accounts. The method also includes retrieving first financial transaction data associated with a first financial account, and selecting, by applying the fraud classification rules to the first financial transaction data, a first fraud classification. The first fraud classification may be selected from among a plurality of predetermined fraud classifications. The method also includes causing an indication of the first fraud classification to be displayed to one or more people via one or more respective computing device user interfaces, the indication further specifying at least the first financial account.

    Reducing false positives using customer data and machine learning
    28.
    发明授权
    Reducing false positives using customer data and machine learning 有权

    公开(公告)号：US11049109B1

    公开(公告)日：2021-06-29

    申请号：US16899486

    申请日：2020-06-11

    Applicant: State Farm Mutual Automobile Insurance Company

    Inventor： Timothy Kramme ,  Elizabeth A. Flowers ,  Reena Batra ,  Miriam Valero ,  Puneit Dua ,  Shanna L. Phillips ,  Russell Ruestman ,  Bradley A. Craig

    IPC: G06Q20/40 ,  G06Q20/32

    Abstract: A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

    Reducing false positives using customer data and machine learning
    29.
    发明授权
    Reducing false positives using customer data and machine learning 有权

    公开(公告)号：US10832248B1

    公开(公告)日：2020-11-10

    申请号：US15465827

    申请日：2017-03-22

    Applicant: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY

    Inventor： Timothy Kramme ,  Elizabeth Flowers ,  Reena Batra ,  Miriam Valero ,  Puneit Dua ,  Shanna L. Phillips ,  Russell Ruestman ,  Bradley A. Craig

    IPC: G06Q20/40 ,  G06Q20/32

    Abstract: A method of detecting whether electronic fraud alerts are false positives includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that determines whether to generate an electronic fraud alert for the financial transaction based upon the data, and, when an electronic fraud alert is generated, inputting the data into a machine learning program trained to identify one or more facts indicated by the data. The method may also include determining whether the identified facts can be verified by customer data and, in response to determining that the facts can be verified, retrieving or receiving first customer data. The method may further include verifying that the electronic fraud alert is not a false positive based upon analysis of the first customer data, and transmitting the verified electronic fraud alert to a mobile device of the customer to alert the customer to fraudulent activity.

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