Systems and methods for generating a probationary automated-decisioning workflow in a machine learning-task oriented digital threat or digital abuse mitigation system

A computer-program product embodied in a non-transitory machine-readable storage medium storing computer instructions that, when executed by one or more processors, perform operations comprising:

• identifying, via the one or more processors, a plurality of distinct digital event features indicative of digital fraud based on an assessment of a digital event data corpus associated with a target subscriber;
• automatically deriving, via the one or more processors, a plurality of distinct digital event decisioning criteria based on the plurality of distinct digital event features and the digital event data corpus, wherein automatically deriving the plurality of distinct digital event decisioning criteria includes:
• (1) training a digital event meta machine learning model comprising a tree classifier using labeled training data samples of the digital event data corpus, wherein the labeled training data samples include a subset of training data samples having one or more of a digital fraud label and a digital abuse label,
• wherein the tree classifier, once trained, computes a likelihood that a target digital event is either a digital fraud event or a digital abuse event;
• (2) based on the training of the tree classifier, exposing a classification structure including a plurality of digital event classification paths of the tree classifier, wherein the classification structure includes learned conditions and/or criteria applied by the tree classifier for classifying the target digital event to a distinct digital threat class of a plurality of distinct digital threat classes;
• (3) extracting from the classification structure of the tree classifier the plurality of distinct digital event decisioning criteria comprising one or more distinct digital event decisioning criteria and/or one or more distinct digital event decisioning conditions for configuring digital fraud detection logic or digital abuse detection logic of a target automated digital fraud or digital abuse detection workflow;
• automatically constructing, via the one or more processors, a probationary automated fraud or abuse detection workflow based on the plurality of distinct digital event decisioning criteria, wherein automatically constructing the probationary automated fraud or abuse detection workflow includes:
• (a) defining, via the one or more processors, a plurality of distinct automated digital event decisioning routes in the probationary automated fraud or abuse detection workflow, and
• (b) setting, via the one or more processors, a distinct route execution condition for each of the plurality of distinct automated digital event decisioning routes based on the plurality of distinct digital event decisioning criteria;
• and
• deploying, via the one or more processors, the probationary automated fraud or abuse detection workflow to a target digital fraud prevention environment of the target subscriber based on identifying efficacy metrics computed for the automated fraud or abuse detection workflow satisfy pre-determined workflow performance criteria.

A computer-implemented system comprising:

• one or more processors;
• a memory;
• a computer-readable medium operably coupled to the one or more processors, the computer-readable medium having computer-readable instructions stored thereon that, when executed by the one or more processors, cause the computing device to perform operations comprising:
• identifying, via the one or more processors, a plurality of distinct digital event features indicative of digital fraud based on an assessment of a digital event data corpus associated with a target subscriber;
• automatically deriving, via the one or more processors, a plurality of distinct digital event decisioning criteria based on the plurality of distinct digital event features and the digital event data corpus, wherein automatically deriving the plurality of distinct digital event decisioning criteria includes:
• (1) training a digital event meta machine learning model comprising a tree classifier using labeled training data samples of the digital event data corpus, wherein the labeled training data samples include a subset of training data samples having one or more of a digital fraud label and a digital abuse label,
• wherein the tree classifier, once trained, computes a likelihood that a target digital event is either a digital fraud event or a digital abuse event;
• (2) based on the training of the tree classifier, exposing a classification structure including a plurality of digital event classification paths of the tree classifier, wherein the classification structure includes learned conditions and/or criteria applied by the tree classifier for classifying the target digital event to a distinct digital threat class of a plurality of distinct digital threat classes;
• (3) extracting from the classification structure of the tree classifier the plurality of distinct digital event decisioning criteria comprising one or more distinct digital event decisioning criteria and/or one or more distinct digital event decisioning conditions for configuring digital fraud detection logic or digital abuse detection logic of a target automated digital fraud or digital abuse detection workflow;
• automatically constructing, via the one or more processors, a probationary automated fraud or abuse detection workflow based on the plurality of distinct digital event decisioning criteria, wherein automatically constructing the probationary automated fraud or abuse detection workflow includes:
• (a) defining, via the one or more processors, a plurality of distinct automated digital event decisioning routes in the probationary automated fraud or abuse detection workflow, and
• (b) setting, via the one or more processors, a distinct route execution condition for each of the plurality of distinct automated digital event decisioning routes based on the plurality of distinct digital event decisioning criteria;
• and
• deploying, via the one or more processors, the probationary automated fraud or abuse detection workflow to a target digital fraud prevention environment of the target subscriber based on identifying efficacy metrics computed for the automated fraud or abuse detection workflow satisfy pre-determined workflow performance criteria.