Abstract: The present disclosure relates to systems and methods for determining anomalies using machine learning models. In examples, systems can be configured to receive network operation data representing a plurality of network operations. The system can classify a subset of the network operations as comprising an anomaly using an isolation forest model. The system can then determine, for each network operation of the subset of network operations, that the anomaly is a positive anomaly, a negative anomaly, or noise. In some examples, the systems can be configured to generate a graphical user interface (GUI) comprising a warning message, the warning message identifying at least one network operation of the subset of network operations as being a negative anomaly.

Abstract: Automated lambda discovery and instruction generation is provided. A method includes receiving an abstract syntax tree (AST) of a set of executable instructions in a first programming language. The method includes identifying, using the AST, one or more loops represented by the AST for the set of executable instructions. The method includes identifying, for each of the one or more loops, a count of a number of variables referenced or assigned for each of the one or more loops. The method includes determining, for a loop of the one or more loops, that the count meets a threshold for which to generate a lambda expression in a second programming language for the loop. The method includes generating, based at least on the determination, a second set of executable instructions in the second programming language including the lambda expression to represent the loop.

Abstract: Risk-based classification is provided. A system can identify search terms and user profiles to identify virtual asset service provider (VASP) related transactions. The system can search, using the terms, transaction data of one or more fiat service providers to identify VASP related transactions. The system can aggregate the transactions and determine metrics of the aggregated transactions. The data processing system can apply rules to the metrics and classify one or more of the aggregated transactions as one of various types of prohibited transactions.

Abstract: Disclosed herein are a system, a method and a device for providing a state map based model instance load balancing. A server can receive a request from a device in a region to access an instance of an AI model of a plurality of AI models deployed across regions. The server can maintain an AI model map of AI models based at least on the type of AI model. The server can identify, based at least on the request, the region of the request and the type of AI model requested. The server can determine, using the AI model map, the instance of the type of AI model deployed in the region from the plurality of AI models deployed in the region. The server can provide, based at least on the determination, a response to the request providing access to the instance of the type of AI model.

Abstract: The present solution provides systems and methods to receive a data set comprising a representation of one or more questions from a survey and provide the data set as input to each of a plurality of machine learning models trained to predict a domain associated with the one or more questions. The systems and methods can receiving as output a first domain prediction for the domain from each of the plurality of machine learning models and determine a second domain prediction for the domain for each question of the one or more questions based on applying a function to each of the first domain predictions. The systems and methods can select, based on the data set and the second domain prediction, an enumerated list of one or more answers from an answer set and cause a display of the enumerated list via a user interface for a selection.

Abstract: The present solution provides systems and methods to receive an artifact, cause the artifact to be compared with many data structures such as files, based on the types of the data structures, determining a similarity between the artifact and elements of the data structures, comparing the similarity to one or more confidence intervals, and storing an output file containing one or more similar elements.

Abstract: The present solution provides systems and methods to receive an artifact, cause the artifact to be compared with many data structures such as files, based on the types of the data structures, determining a similarity between the artifact and elements of the data structures, comparing the similarity to one or more confidence intervals, and storing an output file containing one or more similar elements.

Abstract: The present solution provides systems and methods to receive a data set comprising a representation of one or more questions from a survey and provide the data set as input to each of a plurality of machine learning models trained to predict a domain associated with the one or more questions. The systems and methods can receiving as output a first domain prediction for the domain from each of the plurality of machine learning models and determine a second domain prediction for the domain for each question of the one or more questions based on applying a function to each of the first domain predictions. The systems and methods can select, based on the data set and the second domain prediction, an enumerated list of one or more answers from an answer set and cause a display of the enumerated list via a user interface for a selection.

Abstract: The present solution provides systems and methods to receive an artifact, cause the artifact to be compared with many data structures such as files, based on the types of the data structures, determining a similarity between the artifact and elements of the data structures, comparing the similarity to one or more confidence intervals, and storing an output file containing one or more similar elements.