Fraud detection using augmented analytics

A system, comprising a non-transitory computer-readable medium storing instructions configured to effectuate:

• a data ingestion component configured to ingest fraud contextual data related to fraud events, wherein the fraud data includes contextual data describing context surrounding the fraud events and receive real-time transaction data, wherein the real-time transaction data includes data describing a requested transaction and real-time transactional contextual data associated with the requested transaction;
• a correlation component configured to determine whether a fraud correlation match exists for the requested transaction based on the real-time transaction data corresponding to a causative correlation,
• an unsupervised machine learning component configured to determine at least one fraud correlation between fraud contextual data related to one or more fraud events and the real-time transactional contextual data;
• a supervised machine learning component configured to analyze the at least one fraud correlation using supervised machine learning to identify the causative correlation;
• and
• a transaction approval component configured to complete the real-time transaction or freeze the real-time transaction based on the fraud correlation match.

A non-transitory computer-readable medium storing instructions that when executed by a processor effectuate operations comprising:

• ingesting fraud contextual data related to fraud events, wherein the fraud data includes contextual data describing context surrounding the fraud events
• receiving real-time transaction data, wherein the real-time transaction data includes data describing a requested transaction and real-time transactional contextual data associated with the requested transaction;
• determining at least one fraud correlation between the fraud contextual data and the real-time transactional contextual data using unsupervised machine learning;
• analyzing the at least one fraud correlation determined by the unsupervised machine learning and using supervised machine learning to identify a causative correlation;
• determining whether a fraud correlation match exists for the requested transaction based on the real-time transaction data corresponding to the causative correlation identified by supervised machine learning, and
• causing completion of the real-time transaction when the fraud correlation match does not exist;
• or
• freezing the real-time transaction when the fraud correlation match exists.