Automated fraud risk assessment systems and methods

A system for account fraud detection, comprising:

• one or more processors coupled to memory, the one or more processors being programmed to executes application code instructions that are stored in a storage device, the application code instructions cause the one or more processors to:
• create models of accounts based on machine learning process using inputs of data related to fraudulent account events and non-fraudulent account events;
• receive data regarding a potential fraudulent account event from a fraud detection platform processor;
• segregate the data regarding the potential fraudulent account event into (1) a portfolio type grouping comprising at least one of branded cards type, retail services type, or retail bank type and (2) a fraud type grouping comprising at least one of account takeover fraud type, never received issues fraud type, transaction fraud type, or identification fraud type;
• extract additional data related to the potential fraudulent account event from plurality of sources using intelligence search engines based on the models created by the machine learning process;
• generate metadata elements to encode with one or more instances of the additional data based on characteristics and data patterns of the additional data;
• search the additional data and the metadata elements for one or more data patterns that indicate whether or not the potential fraudulent account event is fraudulent;
• identify a resolution path based on a search result, wherein at least one data pattern is found that indicates that the potential fraudulent account event is fraudulent;
• identify a final result of the potential fraudulent account event;
• and
• input the final result into the machine learning process to further train the machine learning process.

A method for account fraud detection, the method comprising:

• creating models of accounts based on machine learning process using inputs of data related to fraudulent account events and non-fraudulent account events;
• receiving data regarding a potential fraudulent account event from a fraud detection platform processor;
• segregating the data regarding the potential fraudulent account event into (1) a portfolio type grouping comprising at least one of branded cards type, retail services type, or retail bank type and (2) a fraud type grouping comprising at least one of account takeover fraud type, never received issues fraud type, transaction fraud type, or identification fraud type;
• extracting additional data related to the potential fraudulent account event from plurality of sources using intelligence search engines based on the models created by the machine learning process;
• generating metadata elements to encode with one or more instances of the additional data based on characteristics and data patterns of the additional data;
• searching the additional data and the metadata elements for one or more data patterns that indicate whether or not the potential fraudulent account event is fraudulent;
• identifying a resolution path based on a search result, wherein at least one data pattern is found that indicates that the potential fraudulent account event is fraudulent;
• identifying a final result of the potential fraudulent account event;
• and
• inputting the final result into the machine learning process to further train the machine learning process.

One or more non-transitory computer-readable media storing instructions thereon, wherein the instructions cause one or more processors to perform operations comprising:

• creating models of accounts based on machine learning process using inputs of data related to fraudulent account events and non-fraudulent account events;
• receiving data regarding a potential fraudulent account event from a fraud detection platform processor;
• segregating the data regarding the potential fraudulent account event into (1) a portfolio type grouping comprising at least one of branded cards type, retail services type, or retail bank type and (2) a fraud type grouping comprising at least one of account takeover fraud type, never received issues fraud type, transaction fraud type, or identification fraud type;
• extracting additional data related to the potential fraudulent account event from plurality of sources using intelligence search engines based on the models created by the machine learning process;
• generating metadata elements to encode with one or more instances of the additional data based on characteristics and data patterns of the additional data;
• searching the additional data and the metadata elements for one or more data patterns that indicate whether or not the potential fraudulent account event is fraudulent;
• identifying a resolution path based on a search result, wherein at least one data pattern is found that indicates that the potential fraudulent account event is fraudulent;
• identifying a final result of the potential fraudulent account event;
• and
• inputting the final result into the machine learning process to further train the machine learning process.