Abstract: Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Abstract: A method may be performed by a network server and includes: applying historical raw user data as an input to a first application running in a simulation environment; receiving activity log entries from the first application in the simulation environment; identifying a first group of the activity log entries corresponding to an error of the first application; saving an error entry in non-volatile storage media in response to identifying the first group of the activity log entries, the error entry including the first group of the activity log entries and an identification of action to be taken in response to the error of the first application; and causing a monitoring system to generate a first alert corresponding to the error entry.

Abstract: There are provided systems and methods for intelligent clustering of account communities for account feature adjustment. A user may utilize an online account with the service provider to process transactions electronic. However, initially the account may be unverified or otherwise untrusted, and limits on electronic transaction processing may be imposed on the account. To provide intelligent feature adjustment, the service provider may utilize a machine learning technique to cluster verified accounts into communities based on their data representations. Thereafter, when a transaction by an unverified account violates a limit imposed on the unverified account, the service provider may process the unverified account using a machine learning model trained for account correlations to the community clusters in order to determine a corresponding community cluster of accounts and adjust a feature based on behaviors and traits of the verified accounts in the community cluster.

Abstract: Systems and methods are presented for providing a response to a user query. Reception of a user query is detected. An augmentation machine learning model is utilized to determine one or more variations of the user query that correspond to a semantic meaning of the user query. A plurality of response candidates is determined that correspond to the user query by comparing the user query and the one or more variations of the user query to a plurality of documents. A final response candidate is determined from the plurality of response candidates based on utilizing a semantic machine learning model to perform a semantic comparison between the plurality of response candidates and at least the user query.

Abstract: A method may be performed by a network server and includes: applying historical raw user data as an input to a first application running in a simulation environment; receiving activity log entries from the first application in the simulation environment; identifying a first group of the activity log entries corresponding to an error of the first application; saving an error entry in non-volatile storage media in response to identifying the first group of the activity log entries, the error entry including the first group of the activity log entries and an identification of action to be taken in response to the error of the first application; and causing a monitoring system to generate a first alert corresponding to the error entry.

Abstract: A method may be performed by a network server and includes: applying historical raw user data as an input to a first application running in a simulation environment; receiving activity log entries from the first application in the simulation environment; identifying a first group of the activity log entries corresponding to an error of the first application; saving an error entry in non-volatile storage media in response to identifying the first group of the activity log entries, the error entry including the first group of the activity log entries and an identification of action to be taken in response to the error of the first application; and causing a monitoring system to generate a first alert corresponding to the error entry.

Abstract: A method may be performed by a network server and includes: applying historical raw user data as an input to a first application running in a simulation environment; receiving activity log entries from the first application in the simulation environment; identifying a first group of the activity log entries corresponding to an error of the first application; saving an error entry in non-volatile storage media in response to identifying the first group of the activity log entries, the error entry including the first group of the activity log entries and an identification of action to be taken in response to the error of the first application; and causing a monitoring system to generate a first alert corresponding to the error entry.