Palantir Technologies Inc.

Pierre Cholet

Matthew Elkherj

Xavier Falco

Jacob Albertson

Paul Gribelyuk

Kelvin Lau

Marissa Kimball

Han Xu

Andrew Ehrich

Giulio D'Ali' Aula

Corentin Petit

Paul Baseotto

Timur Iskhakov

Victoria Galano

Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.

Systems and methods for implementing a machine learning approach to modeling entity behavior

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a feature clustering of users, user correlation database access, and user interface generation system. The system can obtain information stored in different databases located across geographic regions, and determine unique users from the different information. The information can be included in unique records in the databases, with each record describing a particular user, and with each user described with imperfect identifying information. The system can analyze the different information utilizing machine learning models, and can associate each record with a particular unique user. The system can obtain identifications of items associated with each user, and determine the propensity of the user to disassociate with one or more items, or determine likelihoods of future association with different items not presently associated with the user.

Feature clustering of users, user correlation database access, and user interface generation system

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New York, NY, United States of America

Pierre Cholet