Abstract: Systems, methods, and computer program products for identifying a fraudulent device. A device analytics engine receives device data from a computing device, the device data including parameters associated with the computing device. The device analytics engine selects a set of rules in a plurality of rules that indicate at least one parameter in the plurality of parameters in the device data for determining a device identifier. The set of rules are evaluated in an order until the device identifier is determined from the at least one parameter indicated in the set of rules, the device data, and previously stored data from multiple computing devices. A score is generated for the computing device using one or more of the device identifier, device data, a set of rules, and previously receive device data that corresponds to the device identifier. A computing device is identified as a fraudulent computing device based on the score.

Abstract: There are provided systems and methods for a shared prediction engine for machine learning model deployment. A service provider may provide AI hosting platforms that allow for clients, customers, and other end users to upload AI models for execution, such as machine learning models. A user may utilize one or more user interfaces provided to a client device by the service provider to select machine learning models to perform predictive services based on input features provided in an input string. Thereafter, a machine learning engine may host and execute the models during an instance of the engine provided to the client device. The engine may then process the input features in a processing thread remotely from the client device during the instance so that machine learning predictions may be determined. Thereafter, an output string for the predictions and model explanations may be provided to the client device.

Abstract: There are provided systems and methods for a shared prediction engine for machine learning model deployment. A service provider may provide AI hosting platforms that allow for clients, customers, and other end users to upload AI models for execution, such as machine learning models. A user may utilize one or more user interfaces provided to a client device by the service provider to select machine learning models to perform predictive services based on input features provided in an input string. Thereafter, a machine learning engine may host and execute the models during an instance of the engine provided to the client device. The engine may then process the input features in a processing thread remotely from the client device during the instance so that machine learning predictions may be determined. Thereafter, an output string for the predictions and model explanations may be provided to the client device.

Abstract: Systems, methods, and computer program products for identifying a fraudulent device. A device analytics engine receives device data from a computing device, the device data including parameters associated with the computing device. The device analytics engine selects a set of rules in a plurality of rules that indicate at least one parameter in the plurality of parameters in the device data for determining a device identifier. The set of rules are evaluated in an order until the device identifier is determined from the at least one parameter indicated in the set of rules, the device data, and previously stored data from multiple computing devices. A score is generated for the computing device using one or more of the device identifier, device data, a set of rules, and previously receive device data that corresponds to the device identifier. A computing device is identified as a fraudulent computing device based on the score.

Abstract: There are provided systems and methods for a shared prediction engine for machine learning model deployment. A service provider may provide AI hosting platforms that allow for clients, customers, and other end users to upload AI models for execution, such as machine learning models. A user may utilize one or more user interfaces provided to a client device by the service provider to select machine learning models to perform predictive services based on input features provided in an input string. Thereafter, a machine learning engine may host and execute the models during an instance of the engine provided to the client device. The engine may then process the input features in a processing thread remotely from the client device during the instance so that machine learning predictions may be determined. Thereafter, an output string for the predictions and model explanations may be provided to the client device.

Abstract: There are provided systems and methods for a shared prediction engine for machine learning model deployment. A service provider may provide AI hosting platforms that allow for clients, customers, and other end users to upload AI models for execution, such as machine learning models. A user may utilize one or more user interfaces provided to a client device by the service provider to select machine learning models to perform predictive services based on input features provided in an input string. Thereafter, a machine learning engine may host and execute the models during an instance of the engine provided to the client device. The engine may then process the input features in a processing thread remotely from the client device during the instance so that machine learning predictions may be determined. Thereafter, an output string for the predictions and model explanations may be provided to the client device.

Abstract: There are provided systems and methods for machine learning model verification for assessment pipeline deployment. A service provider may provide AI hosting platforms that allow for clients, customers, and other end users to upload AI models for execution, such as machine learning models. A user may utilize one or more user interfaces to provide model data and files, such as model artifacts, model requirements, and model test data. Thereafter, a model deployer may validate that the AI hosting platform has the required code packages and other model framework requirements for the AI model. The model test data may be used to ensure that the AI model is behaving correctly and providing the correct predictions based on input data and features. If so, the AI model may be deployed in a live production computing environment and used for predictive services.

Abstract: There are provided systems and methods for predictive data aggregations for real-time detection of anomalous data. A service provider, such as an electronic transaction processor for digital transactions, may access feature data for accounts prior to the feature data being used in a live risk analysis system, for example, at a designated time and/or for a designated time period. The service provider may predetermine data values from the feature data, such as aggregates of the feature data that are for certain time periods and utilized by the live risk analysis system. This processing may be done in a batch processing job in order to determine data values for multiple accounts. These data values are prestored in an available database for a distributed computing system of the service provider. Thereafter, when the live risk analysis system requires the data values, the data values may be immediately retrieved.