Intuit, Inc.

Kevin Michael Furbish

Nirmala Ranganathan

Vijay Manikandan Janakiraman

Kymm K Kause

Glenn Carter Scott

Saikat Mukherjee

Calum G Murray

Lalla Mouatadid

Jeffrey A Langston

John J Tumminaro

Aaron Dibner-Dunlap

Michael Radwin

Sumayah Rahman

A method optimizes questions to retain engagement. The method includes generating, using a machine learning model, a churn risk from user interaction data. The method includes selecting, when the churn risk satisfies a threshold, a field, from multiple fields, using multiple prediction confidences corresponding to multiple prediction values generated for the multiple fields. The method includes obtaining a prediction value for the field and obtaining a question, corresponding to the field, using the prediction value. The method includes presenting the question and receiving a user input in response to the question.

Optimizing questions to retain engagement

A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Machine learning for improving mined data quality using integrated data sources

A processor may obtain financial data for a user. The processor may process the financial data to generate feature data indicative of at least one feature. The processor may compare the at least one feature to at least one threshold value to determine that the user has a cognitive bias affecting a financial preference of the user and associated with the at least one threshold value. The at least one threshold value may denote a threshold for membership in a cluster of unlabeled users having the cognitive bias. In response to the comparing, the processor may identify a change applicable to a financial account of the user. The change may be associated with the cognitive bias. The processor may automatically cause the change to be implemented by a network-accessible financial service.

Bias prediction and categorization in financial tools

A method involves identifying account data of an entity for a present time period, where the account data includes more than one first data value, creating a comparison group for the entity. The comparison group includes more than one second data value, the account data of the entity includes the second data values, and the second data values originate from a prior time period. The method further involves selecting, from the first data values, a subset of the first data values, selecting, from the second data values, a subset of the second data values, identifying, by accessing a library including anomaly detection methods, an anomalous value within the subset of the first data values by comparing the subset of the first data values with the subset of the second data values, selecting an action in response to identifying the anomalous value within the subset of first data values, and initiating the action.

Detecting anomalous values in small business entity data

A method for transaction verification scoring includes obtaining, from a distributed computing system of distributed computing systems distributed throughout a computing network, a transaction description describing a financial transaction with a vendor, obtaining, from the distributed computing systems, transaction records of potential corroborators, and reconciling, with the financial transaction, the transaction records to obtain at least one matching transaction record of at least one corroborator, in the potential corroborators, to the financial transaction. The method further includes scoring the transaction description based on a function of each of the at least one corroborator to the financial transaction to obtain a verification score, and presenting, on a display device, a recommendation of the vendor to a consumer based on the verification score.

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Mountain View, CA, United States of America

Kevin Michael Furbish