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 receive fraud contextual data describing context surrounding fraud events, transaction data describing transactional contextual data associated with transactions, and 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;
• an unsupervised machine learning component configured to determine a plurality of correlations between the fraud contextual data and the transactional contextual data using unsupervised machine learning;
• a supervised machine learning component configured to analyze the plurality of correlations using supervised machine learning to identify at least one causative correlation;
• 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 at least one of the at least one causative correlation;
• and
• a user interface configured to provide a notification based on the correlation.

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

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