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    Fraud detection using emotion-based deep learning model
    2.
    发明授权
    Fraud detection using emotion-based deep learning model 有权

    公开(公告)号：US12008579B1

    公开(公告)日：2024-06-11

    申请号：US17397494

    申请日：2021-08-09

    申请人： Wells Fargo Bank, N.A.

    发明人： Abhishek Kumar ,  Dipanjan Deb ,  Julia A Kosheleva-Coates ,  Amit Agarwal ,  Naveen Gururaja Yeri

    IPC分类号： G06Q30/016 ,  G06N3/08

    CPC分类号： G06Q30/016 ,  G06N3/08

    摘要： Techniques are described for determining a likelihood that a customer communication is fraudulent using one or more machine learning models. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: receive a set of emotion factor values for communication data of a current communication associated with a customer, wherein each emotion factor value indicates a measure of a particular emotion factor in the current communication; classify, using an emotion variance model running on the one or more processors, the current communication into an emotional fraud category based on the set of emotion factor values for the current communication associated with the customer; and determine a risk score for the current communication indicative of a probability that the current communication is fraudulent based on at least the emotional fraud category for the current communication.

    FRAUD DETECTION USING EMOTION-BASED DEEP LEARNING MODEL
    3.
    发明公开
    FRAUD DETECTION USING EMOTION-BASED DEEP LEARNING MODEL 审中-公开

    公开(公告)号：US20240112198A1

    公开(公告)日：2024-04-04

    申请号：US18541646

    申请日：2023-12-15

    申请人： Wells Fargo Bank, N.A.

    发明人： Abhishek Kumar ,  Dipanjan Deb ,  Julia A. Kosheleva-Coates ,  Amit Agarwal ,  Naveen Gururaja Yeri

    IPC分类号： G06Q30/016 ,  G06N3/08

    CPC分类号： G06Q30/016 ,  G06N3/08

    摘要： Techniques are described for determining a likelihood that a customer communication is fraudulent using one or more machine learning models. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: receive a set of emotion factor values for communication data of a current communication associated with a customer, wherein each emotion factor value indicates a measure of a particular emotion factor in the current communication; classify, using an emotion variance model running on the one or more processors, the current communication into an emotional fraud category based on the set of emotion factor values for the current communication associated with the customer; and determine a risk score for the current communication indicative of a probability that the current communication is fraudulent based on at least the emotional fraud category for the current communication.

    Predicting customer interaction using deep learning model
    5.
    发明授权
    Predicting customer interaction using deep learning model 有权

    公开(公告)号：US12079826B1

    公开(公告)日：2024-09-03

    申请号：US17359102

    申请日：2021-06-25

    申请人： Wells Fargo Bank, N.A.

    发明人： Abhishek Kumar ,  Naveen Gururaja Yeri ,  Dipanjan Deb ,  Amit Agarwal ,  Mayank Kumari

    IPC分类号： G06Q30/0201 ,  G06F18/2431 ,  G06N20/00

    CPC分类号： G06Q30/0201 ,  G06F18/2431 ,  G06N20/00

    摘要： Techniques are described for personalizing customer interactions using one or more machine learning models for customer communications. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to: retrieve, from a database in memory, one or more sets of emotion factor values for communication data associated with a customer over time; classify, using an emotion propensity model running on the one or more processors, the customer into an emotional profile according to the customer's typical emotional response during customer communications based on the one or more sets of emotion factor values for the communication data associated with the customer over time; and determine a probability that the customer will respond positively to a particular type of customer engagement based on the emotional profile for the customer.

    Complaint prioritization using deep learning model
    7.
    发明授权
    Complaint prioritization using deep learning model 有权

    公开(公告)号：US11954443B1

    公开(公告)日：2024-04-09

    申请号：US17338167

    申请日：2021-06-03

    申请人： Wells Fargo Bank, N.A.

    发明人： Abhishek Kumar ,  Dipanjan Deb ,  Amit Agarwal

    IPC分类号： G06F40/30 ,  G06N20/00 ,  G06V40/20

    CPC分类号： G06F40/30 ,  G06N20/00 ,  G06V40/20

    摘要： Techniques are described for performing complaint prioritization using one or more machine learning models for customer communications. For example, a computing system includes a memory and one or more processors in communication with the memory. The one or more processors are configured to receive communication data indicative of a service inquiry from a user device, generate a set of emotion factor values that indicate a measure of particular emotions in the service inquiry, determine, using a machine learning model and based on the set of emotion factor values, an emotional priority score for the service inquiry, and determine a response priority order for the service inquiry based on at least the emotional priority score.