Palantir Technologies Inc.

Kelvin Lau

Paul Gribelyuk

Pierre Cholet

Viktor Nordling

Han Xu

Francisco Ferreira

Joanna Peller

Anton Apostolatos

Maciej Laska

Florian Diegruber

Joseph Kane

Mikael Ibrahim Mofarrej

Philipp Hoefer

Ryan Norris

Adam Lieskovský

Spencer McCollester

Max-Philipp Schrader

A method, performed by one or more processors, is disclosed, comprising providing, to a plurality of parties permitted to communicate data via a shared database, an ontology application associated with a common core ontology, the core ontology defining constraints required to be met for producing, from one or more received datasets, one or more data objects for storing in the shared database. The ontology application may be configured to receive one or more datasets from one or more parties and to use the core database ontology to determine if the received one or more datasets conform to the constraints of the core ontology, and store the received one or more datasets as data objects in the shared database, conditional on the constraints being met.

Data communications between parties

A computer-implemented method enforces data security constraints in a data pipeline. The data pipeline takes one or more source datasets as input and performs one or more data transformations on them. The method includes using data defining one or more data security constraints to configure the data pipeline to perform a data transformation on a restricted subset of entries of the source datasets. The restriction is defined by the data defining one or more data security constraints. The method further includes performing the data transformation according to the configuration to produce one or more transformed datasets. The method further includes using the data defining one or more data security constraints to perform a verification on one or more of the transformed datasets to ensure that entries in the one or more of the transformed datasets are restricted as defined by the one or more data security constraints.

Enforcing data security constraints in a data pipeline

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

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Kew, Australia

Kelvin Lau