Abstract: The techniques described herein relate to a method including: receiving, from a score service application executed by one or more processors and by a backend server, a request for a merchant lifetime trust score for a merchant; determining one or more calibration models of a bottom layer and one or more merchant features; generating, by the determined a machine learning model, a raw score for the merchant lifetime trust score request; generating, by the score service application, a merchant lifetime trust score based on a business rule to the raw score; and blocking, by the score service application in communication with a payment processing application executed by a frontend server, a transaction based on the merchant lifetime trust score for the merchant.

Abstract: Systems, methods, and computer-readable media are disclosed for the dynamic, real-time detection and clustering of emerging fraud patterns. Example methods may include determining an expected account registration volume and an actual account registration volume during a same period of time. Certain methods may include determining an abnormal fluctuation in account registration volume based on a difference between the expected account registration volume and the actual account registration volume during the period of time. Certain methods may include generating subsets of account registrations received during the period of time based on one or more shared characteristics. Certain methods may include generating an account cluster based on the subsets of account registrations. Certain methods may include sending the account cluster to a bulk closure system.

Abstract: A method and system is disclosed for storing and manipulating customer transaction data received from a plurality of sources. The method may use a computer system comprising a storage device for storing the customer transaction data and a processor for processing the customer transaction data. The method may comprise receiving the customer transaction data, the customer transaction data relating to spending characteristics; appending customer demographic information to the customer transaction data, the customer demographic information including customer demographic variables; organizing the customer transaction data within a predetermined organizational structure; aggregating the customer transaction data based on at least one of customer demographic variables and spending characteristics; and creating a customer profile based on the customer transaction data.

Abstract: Systems for processing requests to use virtual computing resources in communicatively isolated computing environments apply a multi-tier framework for limiting the amount of virtual computing resources that can be allocated to a user in a computing environment. A bottom tier is associated with a user account that has not been analyzed for security risks, a top tier is associated with a user account that has been validated to use a corresponding computing environment; an intermediate tier, having limits between the bottom and top tiers, is applied to computing environments where the user has not requested resources. Initially, all computing environments are on the bottom tier. In response to a valid request for resources, validated computing environments are moved to the top tier and all other computing environments are moved to the intermediate tier. Requests in an intermediate-tier computing environment trigger validity review; the computing environment can move to top tier.