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    System and method for a near field communications reader device
    81.
    发明授权
    System and method for a near field communications reader device 有权

    公开(公告)号：US12008428B2

    公开(公告)日：2024-06-11

    申请号：US18202522

    申请日：2023-05-26

    Applicant: Stripe, Inc.

    Inventor： Hwai Sian Tsai ,  Chi Wah Lo

    IPC: G06K7/10

    CPC classification number: G06K7/10297 ,  G06K7/10316

    Abstract: A reader device for attachment to a smart device comprising a display, the reader comprising an antenna, processing and reading circuitry, and a communications module, wherein the antenna, the processing and reading circuitry, the processor and the communications module are coupled to each other, and whereby the antenna encloses said display. When a card is tapped on the display, the antenna receives a signal and transmits the signal to the processing and reading circuitry. The processing and reading circuitry processes the signal to produce data, and the produced data is transmitted to a device external to the reader device by the communications module.

    Using one or more networks to assess one or more metrics about an entity
    82.
    发明授权
    Using one or more networks to assess one or more metrics about an entity 有权

    公开(公告)号：US11997098B2

    公开(公告)日：2024-05-28

    申请号：US17975316

    申请日：2022-10-27

    Applicant: Stripe, Inc.

    Inventor： Pranav Dandekar ,  Peter Lofgren ,  Ashish Goel

    IPC: G06F21/50 ,  G06F15/16 ,  H04L9/40

    CPC classification number: H04L63/102 ,  G06F21/50 ,  G06F15/16 ,  H04L63/0263

    Abstract: Described herein are systems and methods for predicting a metric value for an entity associated with a query node in a graph that represents a network. In embodiments, using a user's profile as the query node, a metric about that user may be estimated based, at least in part, as a function of how well connected the query node is to a whitelist of “good” users/nodes in the network, a blacklist of “bad” users/nodes in the network, or both. In embodiments, one or more nodes or edges may be weighted when determining a final score for the query node. In embodiments, the final score regarding the metric may be used to take one or more actions relative to the query node, including accepting it into a network, allowing or rejecting a transaction, assigning a classification to the node, using the final score to compute another estimate for a node, etc.

    Machine learning-based loss forecasting model
    83.
    发明授权
    Machine learning-based loss forecasting model 有权

    公开(公告)号：US11995714B1

    公开(公告)日：2024-05-28

    申请号：US17475215

    申请日：2021-09-14

    Applicant: STRIPE, INC.

    Inventor： Mengjie Ding ,  Bo Xu ,  Yaakov Erlichman

    IPC: G06Q40/03 ,  G06Q40/06

    CPC classification number: G06Q40/03 ,  G06Q40/06

    Abstract: Systems and methods for implementing a machine learning loan portfolio loss forecasting system are provided. A current state of active loans of a loan portfolio during a first time period of a set of time periods may be determined. For each of the set of time periods, a roll rate of the active loans from each delinquency state to a subsequent delinquency state may be determined based on historical data of the loan portfolio. The machine learning model may then, iteratively, for each subsequent time period, determine a percentage of the active loans that will transition to each of the set of delinquency states during the subsequent time period based on a current state of the active loans during a previous time period and the roll rate from each delinquency state to a subsequent delinquency state for the subsequent time period.

    FRAUD DETECTION USING REAL-TIME AND BATCH FEATURES
    84.
    发明公开
    FRAUD DETECTION USING REAL-TIME AND BATCH FEATURES 审中-公开

    公开(公告)号：US20240161115A1

    公开(公告)日：2024-05-16

    申请号：US17985732

    申请日：2022-11-11

    Applicant: Stripe, Inc.

    Inventor： Ryan Drapeau ,  Logan Howard ,  Seth Hendrickson ,  Isaac Madwed

    IPC: G06Q20/40

    CPC classification number: G06Q20/4016

    Abstract: A method and system for detecting a fraudulent payment are described herein. The method can include detecting, at a server computer system, a request to perform a transaction between a merchant system and a customer of the merchant system, and in response to the detecting, determining, in real time one or more real time features corresponding to the transaction. The method may also include determining, by the server computer system, one or more batch features that correspond to one or more attributes associated with the transaction. The method may also include determining, by the server computer system, whether the payment is potentially fraudulent based on utilizing a model that includes inputs corresponding to the combination of the one or more real time features and the one or more batch features, and in response to determining that the payment is potentially fraudulent, performing, a remediation action associated with the transaction.

    DATA MANAGEMENT USING SCORE CALIBRATION AND SCALING FUNCTIONS
    85.
    发明公开
    DATA MANAGEMENT USING SCORE CALIBRATION AND SCALING FUNCTIONS 审中-公开

    公开(公告)号：US20240152923A1

    公开(公告)日：2024-05-09

    申请号：US17979985

    申请日：2022-11-03

    Applicant: Stripe, Inc.

    Inventor： Emmanuel Ameisen

    IPC: G06Q20/40

    CPC classification number: G06Q20/4016

    Abstract: Various embodiments described herein support or provide for data management operations, such as identifying an uncalibrated fraud score that corresponds to a set of transactions; using a machine learning model to generate a calibrated fraud score based on the uncalibrated fraud score; determining a calibrated fraud score distribution associated with the calibrated fraud score; identifying an uncalibrated fraud score distribution associated with the uncalibrated fraud score; using a score scaling function to generate a mapping between the calibrated fraud score distribution and the uncalibrated fraud score distribution; and generating a scaled calibrated fraud score based on the mapping.

    SYSTEMS AND METHODS FOR PROOF OF APPLICATION OWNERSHIP
    88.
    发明公开
    SYSTEMS AND METHODS FOR PROOF OF APPLICATION OWNERSHIP 审中-公开

    公开(公告)号：US20240119499A1

    公开(公告)日：2024-04-11

    申请号：US18541862

    申请日：2023-12-15

    Applicant: Stripe, Inc.

    Inventor： Joseph Patrick Kirwin

    IPC: G06Q30/0601

    CPC classification number: G06Q30/0609 ,  G06Q2220/00

    Abstract: A method and apparatus for a commerce platform providing proof of application ownership of a network distributable application are described. The method may include receiving a request to approve an application developed by a merchant system, wherein the application includes an application programming interface (API) component, a software development kit (SDK) component, or a combination thereof provided by the commerce platform to the merchant system. The method may also include generating a unique identifier (ID) for the application to be included as metadata within the application. Furthermore, the method may include obtaining, from an application information system, data describing the application, and extracting an ID from metadata in the data obtained by the application information system. Then, the method may include that in response to determining that the ID extracted from the metadata matches the unique ID, associating the merchant with the application in a merchant account at the commerce platform and approving the application for interacting with the commerce platform.

    SYSTEMS AND METHODS FOR ENHANCED TRANSACTION AUTHENTICATION
    89.
    发明公开
    SYSTEMS AND METHODS FOR ENHANCED TRANSACTION AUTHENTICATION 审中-公开

    公开(公告)号：US20240112192A1

    公开(公告)日：2024-04-04

    申请号：US17959198

    申请日：2022-10-03

    Applicant: Stripe, Inc.

    Inventor： Sahar Mor, JR. ,  Abhishek Kulgod ,  Ryan Drapeau

    IPC: G06Q20/40 ,  G06Q20/34

    CPC classification number: G06Q20/4016 ,  G06Q20/34

    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.

    MACHINE LEARNING MODEL TRAINING AND DEPLOYMENT PIPELINE
    90.
    发明公开
    MACHINE LEARNING MODEL TRAINING AND DEPLOYMENT PIPELINE 审中-公开

    公开(公告)号：US20240070484A1

    公开(公告)日：2024-02-29

    申请号：US17900376

    申请日：2022-08-31

    Applicant: Stripe, Inc.

    Inventor： Ketan SINGH ,  Peter Lofgren ,  Ryan Lee Drapeau ,  Abhishek Jha ,  Anthony Pianta

    IPC: G06N5/02

    CPC classification number: G06N5/022

    Abstract: In an example embodiment, a machine learning training pipeline is introduced that continuously monitors and processes training data having multiple transaction types using a sliding window, adding labels as they are available for the various different types of transactions in the training data. The processed training data, with the appropriate labels added, can then be utilized by any machine learning model that is being onboarded using the pipeline, without any specialized setup being necessary. Further, even if new data is added to the pipeline to aid in the training of a new model (such as data regarding a new payment type), this new data can be processed quickly and added to the existing data without requiring specialized processes by the entity requesting the new machine learning model. This allows the actual training of the new machine learning model to be accomplished very quickly, and deployment to be accomplished even faster.

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