Instant lending decisions
    2.
    发明授权

    公开(公告)号:US11055772B1

    公开(公告)日:2021-07-06

    申请号:US16198599

    申请日:2018-11-21

    IPC分类号: G06Q40/02

    摘要: The method and system involves instant loan decisions by generating a risk profile of a small business (SMB). The risk profile is generated based on accounting data and other third party business management application (BMA) data of the SMB. In particular, the accounting data and other third party BMA data are retrieved from a BMA (e.g., accounting application, payroll application, tax preparation application, personnel application, etc.) as a software-as-an-service (SaaS) used by the SMB. Specifically, the risk profile represents the likelihood of the SMB to be delinquent and/or to default on a loan. The risk profile is then provided to a lender for making an expedient lending decision with respect to the SMB. In addition, statistics of lenders' lending decisions based on provided risk profiles are analyzed to generate a correlation. Accordingly, the algorithm(s) used to generate the risk profile from the accounting data and other third party BMA data are adjusted to maximize the correlation.

    Accountant account takeover fraud detection

    公开(公告)号:US10789643B1

    公开(公告)日:2020-09-29

    申请号:US15798124

    申请日:2017-10-30

    摘要: A method for fraud detection may include receiving, via a first user account of a business management application (BMA), a first loan application for a first business entity. The first user account may be accessible to an accountant of an accounting firm. The method may further include receiving, via a second user account of the BMA, a second loan application for a second business entity. The second user account may be accessible to the accountant. The method may further include determining, using a cluster analysis, (i) a connection strength between the first business entity and the second business entity relative to the accounting firm, and (ii) a fraud score for the accounting firm, and determining, based on the connection strength and the fraud score, a probability that the first loan application is fraudulent.

    User data augmented propensity model for determining a future financial requirement

    公开(公告)号:US10373267B2

    公开(公告)日:2019-08-06

    申请号:US15143485

    申请日:2016-04-29

    IPC分类号: G06Q40/00 G06Q10/06

    摘要: A method for determining a future financial requirement of a business entity. The method includes obtaining a propensity model that models how data of a business entity relates to a future financial requirement. Also, the method includes gathering the data of the business entity. The data includes financial data of the business entity, and metadata describing use of a platform by users associated with the business entity. The data matches at least a subset of the propensity model. Further, the method includes scoring the business entity by applying the propensity model to the data of the business entity. In addition, the method includes generating, based on the score of the business entity, a classification of the future financial requirement of the business entity. Still yet, the method includes transmitting a message to the business entity based on the classification of the future financial requirement of the business entity.

    Externally augmented propensity model for determining a future financial requirement

    公开(公告)号:US11107027B1

    公开(公告)日:2021-08-31

    申请号:US15169718

    申请日:2016-05-31

    摘要: A method for utilizing an externally augmented propensity model for determining a future financial requirement. The method includes obtaining at least one propensity model that models how data associated with a business entity relates to a future financial requirement of the business entity, and gathering the data associated with the business entity. The data includes a first portion created based on a platform utilized by users associated with the business entity, and financial data of an owner of the business entity. The data matches at least a subset of the at least one propensity model. The business entity is scored by applying the at least one propensity model to the data. Further, based on the score, the future financial requirement of the business entity is classified. Still yet, a message is transmitted to the business entity based on the classification of the future financial requirement of the business entity.

    PROPENSITY MODEL FOR DETERMINING A FUTURE FINANCIAL REQUIREMENT

    公开(公告)号:US20170316512A1

    公开(公告)日:2017-11-02

    申请号:US15143499

    申请日:2016-04-29

    IPC分类号: G06Q40/00 G06Q10/06

    摘要: A method for determining a future financial requirement of a business entity. The method includes obtaining a propensity model. The propensity model models how data of the business entity relates to a future financial requirement of the business entity. Also, the method includes gathering the data of the business entity. The data is created based on a platform utilized by the business entity, and the data of the business entity matches at least a subset of the propensity model. In addition, the method includes scoring the business entity by applying the propensity model to the data of the business entity. The method also includes generating, based on the score of the business entity, a classification of the future financial requirement of the business entity. Further, the method includes transmitting a message to the business entity based on the classification of the future financial requirement of the business entity.

    USER DATA AUGMENTED PROPENSITY MODEL FOR DETERMINING A FUTURE FINANCIAL REQUIREMENT

    公开(公告)号:US20170316511A1

    公开(公告)日:2017-11-02

    申请号:US15143485

    申请日:2016-04-29

    IPC分类号: G06Q40/00

    CPC分类号: G06Q40/12 G06Q10/06375

    摘要: A method for determining a future financial requirement of a business entity. The method includes obtaining a propensity model that models how data of a business entity relates to a future financial requirement. Also, the method includes gathering the data of the business entity. The data includes financial data of the business entity, and metadata describing use of a platform by users associated with the business entity. The data matches at least a subset of the propensity model. Further, the method includes scoring the business entity by applying the propensity model to the data of the business entity. In addition, the method includes generating, based on the score of the business entity, a classification of the future financial requirement of the business entity. Still yet, the method includes transmitting a message to the business entity based on the classification of the future financial requirement of the business entity.