Intuit, Inc.

Tarek Rabbani

Siddhartha Misra

Kishore Nene

Mahesh Subramanian

Neha Kumari

Prateek Vasant Kakirwar

Ruobing Lu

Don Emmanuel Bernal

Nageswara Prasad Cherukuri

Certain aspects of the present disclosure provide techniques for improving a user experience based on electronic records of transactions. Embodiments include training a classifier using training data comprising a set of historical transaction descriptions and a set of corresponding historical classifications that indicate whether or not each historical transaction description of the set of historical transaction descriptions is associated with a user location. Embodiments further include receiving a transaction record describing a transaction associated with a user. Embodiments further include using the classifier to determine a classification for the transaction. The classification indicates whether or not the transaction description is associated with a location of the user. Embodiments further include providing, based at least in part on the classification, a communication to the user that relates to the location of the user.

Methods for identifying transactions with user location information

A method and system of identifying account duplication in data management systems utilizes a dual-phase approach, consisting of a projection phase and a clustering phase. In the projection phase, user account and transaction data is obtained and grouped into transactions entities. These transactions entities are then processed to create transactions entity projections. In the clustering phase, the transactions entity projections are grouped and aggregated according to specified parameters, which results in the generation of transactions graphs. The data from the transactions graphs is processed and analyzed to generate reliable indicators representing the presence or absence of account duplication in a data management system.

Method and system for identifying account duplication in data management systems

Computerized systems and methods for efficiently refreshing financial data for financial accounts for login identifications of users at respective financial institutions in a batch data pull via a communication network. The login identifications are prioritized for being refreshed based on determining a probability that each login identification has a new transaction since the last successful refresh of the login identification using a learning algorithm utilizing past financial data. The login identifications are then refreshed in a batch data pull from the financial institutions in an order from highest probability to lowest probability of having a new transaction since the last batch data pull.

Methods and systems for efficiently retrieving financial data for financial accounts via a comunication network

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Mountain View, CA, United States of America

Tarek Rabbani