Abstract: New client acquisition insights and forecasting are provided. A system, method, and computer readable storage device analyze historical data for identifying various client segments based on various combinations of similar attributes; determine cumulative marginal values (CMVs) associated with provisioning clients in each client segment, wherein the CMVs indicate whether provisioning a client within each client segment positively or negatively affects the system; analyze each client segment behavior for learning insights associated with clients in the client segment and effects on the system; and forecast CMVs for a selected client segment based on the learned insights for the selected client segment. In various implementations, a dashboard user interface comprising a data visualization of the forecasted CMVs is generated for display.

Abstract: An improvement to the timespan for processing very large data sets using fuzzy logic to analyze billions of combinations of characters to enable an early detection of identity masking corresponding to new account fraud is provided. An identity masking fraud detection system is provided that receives new client data records including identifying information, compares the identifying information against prior user profiles stored in a database, determines when a prior user profile is matched with the identity information in the new client data record, and flags the new client data record and corresponding prior user profile as suspicious when an unresolved transaction is identified in associated with the prior user profile. The system prompts the new client user for additional identification information to verify that the new client user is not associated with the prior user profile or for the new client user to resolve the unresolved transaction.