Intuit, Inc.

Miriam Hanna Manevitz

Aviv Ben Arie

Yair Horesh

Adi Shalev

Elik Sror

Yehezkel Shraga Resheff

Shlomi Medalion

Sigalit Bechler

Shiomi Medalion

Or Basson

Liat Ben Porat Roda

Hagai Fine

A plurality of graph snapshots for a plurality of consecutive periodic time samples maps between connected components in consecutive graph snapshots and describes at least one feature of each connected component. A recursively-built tree tracks an evolution of one of the connected components through the plurality of graph snapshots, the tree including a root node representing the connected component at a final one of the consecutive periodic time samples and a plurality of leaf nodes branching from the root node. A plurality of paths is extracted from the tree by traversing the tree from the root node to respective ones of the plurality of leaf nodes. Each path contains data describing an evolution of a respective one of the connected components through time as indicated by evolution of the at least one feature of the respective one of the connected components. Each of the plurality of paths is converted into a respective numerical vector of a plurality of numerical vectors that may be used as inputs to a time series anomaly detection algorithm.

Feature extraction and time series anomaly detection over dynamic graphs

A method includes training, using first real data objects, a generative adversarial network having a generator model and a discriminator model to create a trained generator model that generates realistic data, and training, using adversarial data objects and second real data objects, the discriminator model to output an authenticity binary class for the adversarial data objects and the second real data objects. The method further includes deploying the discriminator model to a production system. In the production system, the discriminator model outputs the authenticity binary class to a system classifier model.

Adversarial detection using discriminator model of generative adversarial network architecture

A method may include receiving, for a package, shipment details including attributes, obtaining, for a subset of the attributes, logistic preferences, applying the logistic preferences to the shipment details to obtain modified shipment details, training a classifier using shipment transactions each including values for the attributes and labeled with a vendor logistic service, generating, by applying the classifier to the modified shipment details, scores for vendor logistic services, and recommending a vendor logistic service from the vendor logistic services using the scores.

Logistic recommendation engine

Systems and methods may be used to generate and use a merchant community graph generated based on merchant financial transaction data. Connections between merchants and other data within the merchant community graph can be used to detect fraud, target product offerings and or other advertisements, detect similar communities, generate dynamic attributes that may be used to develop machine learning models, and develop new user interfaces (UIs) and other features of an information service.

Methods and systems using and constructing merchant communities based on financial transaction data

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Tel Aviv, Israel

Miriam Hanna Manevitz