Abstract: An empirically rigorous system and method for establishing a digital fingerprint of a pattern of behavior and then prosecuting any ecosystem (either the one in which that pattern was found or a completely disparate one) for isomorphic relationships that resemble that pattern of behavior in a meaningful and quantifiable way. The method also addresses concerns of bias including random discovery, anecdotal similarity, and computational intractability. Applications include the discovery of patterns indicative of money laundering, fraud, and malfeasance.

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 rela

Abstract: A multidimensional recursive and self-perfecting process used to discover dyatic or multi-counterparty relationships between parties, the process comprising: (a) collecting information from a plurality of data sources; (b) discovering dyatic or multi-counterparty relationships between the parties from the collected information; (c) clustering the parties to infer the dyatic 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 dyatic 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 dyatic or multi-counterparty rela