Abstract: Methods, systems, and apparatus for training model parameters stored in shared memory to predict risk. The method may include obtaining training data that includes a plurality of training data structures that each represent attributes of an entity, wherein each training data structure represents (i) features derived from a first set of categories defined by a first model and from a second set of categories defined by a second model, and (ii) a risk-level associated with the entity. For each respective training data structure, providing the training data structure as an input to the model, receiving an output from the model based on the model's processing of the training data structure, determining an amount of error between the output of the model and the risk-level of the training data structure, and adjusting a parameter value of the model stored in a shared memory based on the determined error.

Abstract: Methods, systems, and apparatus for generating a set of training data structures that are used to improve the performance of a machine learning model used to predict an entity risk. The method may include obtaining a attributes related to an entity and storing the attributes in a shared memory, segmenting the obtained attributes stored in the shared memory into distinct sets of risk-scoring components, wherein the distinct sets of risk-scoring components include a first set of risk-scoring components and a second set of risk-scoring components, mapping the risk-scoring components of the first set of risk-scoring components to one or more categories of a first plurality of categories, mapping the risk-scoring components of the second set of risk-scoring components to one or more categories of a second plurality of categories, and generating a training data structure based on the mapping of the first set and the mapping of the second set.

Abstract: Methods, systems, and apparatus for training model parameters stored in shared memory to predict risk. The method may include obtaining training data that includes a plurality of training data structures that each represent attributes of an entity, wherein each training data structure represents (i) features derived from a first set of categories defined by a first model and from a second set of categories defined by a second model, and (ii) a risk-level associated with the entity. For each respective training data structure, providing the training data structure as an input to the model, receiving an output from the model based on the model's processing of the training data structure, determining an amount of error between the output of the model and the risk-level of the training data structure, and adjusting a parameter value of the model stored in a shared memory based on the determined error.