Abstract: A computer-implemented method is provided for detecting actionable transaction risks. The method includes grouping an inbound event related to a transaction with a target event group and determining an actionable group of events that are deemed high risk and a non-actionable group of non-actionable events that are deemed low risk. The method also includes evaluating the target event group relative to the actionable and non-actionable groups of events. This includes computing a first distance between the target event group and the non-actionable group and a second distance between the target event group and actionable group. The first distance is compared with the second distance to determine if the target event group, including the inbound event, is closer to the actionable group or to the nonactionable group.

Abstract: Systems and methods are described for integrating one or more machine learning models with a client application using Remote Procedure Calls (RPCs). A server deploys a software container associated with a client application, the container comprising executable code corresponding to a machine learning model, a plurality of inputs to the machine learning model, and a plurality of outputs of the machine learning model. The server generates a protocol buffer profile using the inputs and the outputs, the protocol buffer profile defining RPC functions for integrating the client application and the machine learning model. The server receives, from the client application, a request to access the machine learning model using a first RPC function. The server executes the machine learning model to generate a classification value for input provided in the request. The server transmits the classification value to the client application using a second RPC function.

Abstract: Methods and apparatuses are described for intelligent text classification with limited or no training data. A server computing device receives one or more of structured text or unstructured text corresponding to compliance text data from a database. The server computing device executes a trained few-shot natural language inference (NLI) classification model on one or more sentences in the received compliance text data to identify whether the one or more sentences comprise a compliance violation. The server computing device transmits the results of the model execution to a remote computing device.

Abstract: Systems and methods for tagging data strings in electronic documents using a convolutional neural network model. The method includes receiving an electronic document and extracting data strings from the electronic document. The method also includes tokenizing each of the data strings into tokens and determining a first tag corresponding to a first token for a first data string using a convolutional neural network model. The method further includes receiving user response data corresponding to an accuracy of the first tag and determining a second tag corresponding to the first token based on the user response data using the convolutional neural network model. The method also includes storing results data into a database and generating for display the results data on a user device.

Abstract: A method including: defining a model space within which all subsurface models are in general local isostatic equilibrium; generating a representative set of trial models which span the model space, wherein a requirement that all of the trial models be in isostatic equilibrium provides a constraint that narrows a range of solutions that fit observed gravity data, wherein the trial models provide scenarios of Earth's crustal geometry, and crust and mantle rock properties; and determining, within an inversion process, a model solution based on minimizing a misfit between the observed gravity data and synthetic gravity data generated from at least one of the trial models.

Abstract: A method including: defining a model space within which all subsurface models are in general local isostatic equilibrium; generating a representative set of trial models which span the model space, wherein a requirement that all of the trial models be in isostatic equilibrium provides a constraint that narrows a range of solutions that fit observed gravity data, wherein the trial models provide scenarios of Earth's crustal geometry, and crust and mantle rock properties; and determining, within an inversion process, a model solution based on minimizing a misfit between the observed gravity data and synthetic gravity data generated from at least one of the trial models.