Multidimensional recursive learning process and system used to discover complex dyadic or multiple counterparty relationships

Abstract

A multidimensional recursive and self-perfecting process used to discover dyadic or multi-counterparty relationships between parties, the process comprising: (a) collecting information from a plurality of data sources; (b) discovering dyadic or multi-counterparty relationships between the parties from the collected information; (c) clustering the parties to infer the dyadic or multi-counterparty relationships between the parties based on common or partially intersecting attributes between the parties, thereby forming clustered parties; (d) evaluating the clustered parties for business linkage potential by integrating the collected information and contextually assessing indicia from the data sources to detect and measure consistency and inconsistency for a given party or dyadic or multi-counterparty relationship; (e) positing and evaluating relationship type and role said party plays in each relationship; and (f) assessing the confidence level regarding the likelihood that the dyadic or multi-counterparty relationship exists between the parties.