Intuit, Inc.

Eva Diane Chang

Madhu Shalini Iyer

Jeffrey Lewis Kaufman

Richard Neil Preece

Siddharth Ram

Joseph Timothy Callinan, Jr

Kathy Tsitovich

Bei Huang

Michael Brener

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.

Externally augmented propensity model for determining a future financial requirement

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.

Instant lending decisions

A method for modeling risk of a derogatory financial event. The method includes generating a risk profile of a small business (SMB) using a risk model based on accounting data and other third party business management application (BMA) data of the SMB. In particular, the risk model is generated using a training data set that includes accounting data and other third party BMA data of multiple SMBs over particular time periods related to historic derogatory events (e.g., to be delinquent and/or to default on a loan) of these SMBs. Generated using the risk model, the risk profile of the SMB represents the likelihood of a derogatory financial event occurring to the SMB.

Algorithm and models for creditworthiness based on user entered data within financial management application

A method for generating a business health score of a business entity that includes identifying tools used to manage the business entity, obtaining business information from the tools, filtering the business information into at least one data category to obtain categorized business information, populating the categorized business information into a data silo, and analyzing, by a computer processor, the categorized business information in the data silo to create a composite business factor. The method also includes applying an algorithm to the composite business factor to generate a result, generating, based on the result, the business health score of the business entity, and providing the business health score to an interested entity.

Business health score and prediction of credit worthiness using credit worthiness of customers and vendors

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.

Accountant account takeover fraud detection

A method for transmitting a message based on the occurrence of a workflow event and the output of an externally augmented propensity model. The method includes scoring a business entity by applying a propensity model to data. The data includes a first portion created based on a platform utilized by users associated with the business entity, and a second portion that includes financial data of an owner of the business entity. The method includes generating, based on the score, a classification of a future financial requirement of the business entity, and determining that the classification meets a financial requirement threshold. Moreover, the method includes determining, using the first portion of data, that an aspect of the business entity meets a business activity threshold. Also, the method includes detecting that a workflow event has occurred on the platform, and, in response, transmitting a message to a user of the business entity.

Transmission of a message based on the occurrence of a workflow event and the output of an externally augmented propensity model identifying a future financial requirement

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.

Propensity model for determining a future financial requirement

A method and system for detecting fraudulent bill payment service transactions using dynamic multi-parameter predictive modeling provides for detecting fraudulent bill payment transactions more accurately. Therefore, a technical solution to the long standing technical problem of inaccurate fraudulent bill payment transaction detection is provided. In addition, the method and system for detecting fraudulent bill payment service transactions using dynamic multi-parameter predictive modeling is capable of self-learning and dynamically adapting to new data and/or a changing threat environment. Consequently, a technical solution to the long standing technical problem of static and inflexible fraudulent bill payment transaction detection is also provided.

Method and system for detecting fraudulent bill payment transactions using dynamic multi-parameter predictive modeling

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.

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Mountain View, CA, United States of America

Eva Diane Chang