Intuit, Inc.

Synamedia Limited

Yaakov Tayeb

Hadar Lackritz

Noah Eyal Altman

Yair Horesh

Steven Jason Epstein

Sigalit Bechler

Natalie Bar Eliyahu

Nadav Avikasis

Daniel Vaisman

Teddy Kevin Rose

Eyal Irit Zmora

Rineke Van Noort

Yael Hochma

Aspects of the present disclosure provide techniques for automated categorization of electronic information. Embodiments include providing inputs to a machine learning model based on attributes of an electronic data item. Embodiments include receiving one or more first outputs from the machine learning model based on the inputs. Embodiments include selecting, based on the one or more first outputs, a question from a plurality of questions. Embodiments include providing the question for display via a user interface. Embodiments include receiving an answer to the question via the user interface. Embodiments include providing updated inputs to the machine learning model based on the answer. Embodiments include receiving one or more second outputs from the machine learning model based on the updated inputs. Embodiments include determining a category for the electronic data item based on the one or more second outputs.

Guided feedback loop for automated information categorization

A method implements influencer segmentation detection. The method includes selecting transaction data for a time window and processing the transaction data for the time window to generate a graph for the time window. The method further includes extracting, from the graph, a feature set for a node of the graph for the time window and processing the feature set to generate a predicted rank for the node for a subsequent time window using a machine learning model. The method further includes selecting, using the predicted rank, an entity identifier corresponding to the node and presenting the entity identifier.

Influencer segmentation detector

Certain aspects of the present disclosure provide techniques for generating a metric, include receiving a rule defining one or more text strings; determining a set of transactions based on a user attribute; determining a first subset of transactions; determining a second subset of transactions; generating a first categorical distribution based on each transaction of the first subset of transactions being associated with a transaction description containing at least one text string of the one or more text strings; calculating a first unity metric based on the first categorical distribution; generating a second categorical distribution based on each transaction of the second subset of transactions being associated with a transaction description that does not contain a text string of the one or more text strings; calculating a second unity metric based on the second categorical distribution; determining a reliability metric for the rule; and providing the reliability metric.

Automated categorization of data by generating unity and reliability metrics

The present disclosure provides techniques for detecting and correcting outliers in categories of transactions. One example method includes receiving electronic transaction data indicative of one or more current transactions, wherein the one or more current transactions are associated with a user of a software application, identifying, for each transaction of the one or more transactions, a category using a first machine learning model, computing a distribution for each category of a plurality of categories of the user, identifying, a particular category of the user as an anomalous category, based on the distribution for the particular category of the user and corresponding distributions for the particular category of other users, and updating a category assigned to one or more transactions such that a delta between a value relating to the anomalous category of the user and corresponding values relating to the particular category of the other users is reduced.

Detecting and correcting outliers in categories of transactions

The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes receiving electronic transaction data indicative of one or more transactions, identifying, from the one or more transactions, a subset of transactions that are associated with for known attribute values with respect to one or more unique recipients, computing, for each unique provider of the one or more unique providers, a provider feature based on the known attribute values with respect to a subset of the one or more associated unique recipients, computing, for a given recipient indicated in one or more given transactions that are not included in the subset of transactions, a recipient feature based on the provider feature of each unique provider of the one or more associated unique providers, and predicting, based on the recipient feature, a value for the attribute with respect to the given recipient.

Predicting attributes for recipients

Techniques for adaptive validation and remediation are described. In some embodiments, the method includes determining, for a plurality of media service accounts, corresponding fraud suspicion values based on a model. The method also includes identifying a plurality of suspected accounts based on the corresponding fraud suspicion values. The method additionally includes identifying one or more suspected devices and predicting a likelihood of account takeover from each of the one or more suspected devices. The method further includes detecting a triggering event from a device of the one or more suspected devices associated with an account. The method additionally includes executing a validation and/or remediation procedure based on a trigger sensitivity value associated with the triggering event, a respective likelihood of account takeover from the device associated with the account, a respective device risk value associated with the device, and a respective fraud suspicion value associated with the account.

Adaptive validation and remediation systems and methods for credential fraud

A method implements a source rank metric of measuring sources of influence. The method includes processing transactions to generate a graph. The method further includes processing the graph to select nodes. A node is identified as one or more of a source node and a destination node of an edge of the graph. The method further includes processing source ranks, of the nodes, to update the source ranks. A source rank is calculated using a subset of indegree values and a subset of outdegree edges. The method further presenting identifiers, of multiple entities represented by the nodes, sorted using the source ranks.

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Tzur Hadassa, Israel

Yaakov Tayeb