Systems and methods for configuring and implementing a card testing machine learning model in a machine learning-based digital threat mitigation platform

A machine learning-based system for detecting and mitigating digital abuse or digital fraud that involves malicious account testing, the method comprising:

• a distributed network of computers implementing a machine learning-based digital threat mitigation service that:
• implements a machine learning threat model that predicts malicious account testing using misappropriate accounts,
• wherein a subset of a plurality of learnable variables of an algorithmic structure of the machine learning threat model includes one or more learnable variables derived based on feature data indicative of malicious account testing;
• wherein implementing the machine learning threat model includes:
• (i) identifying event data from an online event that is suspected to involve digital fraud or digital abuse,
• (ii) extracting adverse feature data from the event data that map to the one or more learnable variables of the subset, and
• (iii) providing the adverse feature data as model input to the machine learning threat model;
• and
• computes, using the machine learning threat model, a threat prediction indicating a probability that the online event involves malicious account testing.