Systems and methods for authenticating a resource system

A system comprising:

• one or more processors;
• and
• at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• receiving a dataset that includes a first subset and a second subset associated with a first resource system;
• down-sampling the first subset but not the second subset, wherein down-sampling the first subset includes:
• standardizing the first subset;
• and
• transforming the standardized first subset into a unique hash key for matching with a stored data string;
• generating a first feature for a machine learning model based on the down-sampled first subset;
• generating a second feature for the machine learning model based on the second subset;
• generating, via input of at least one of the first feature or the second feature into the machine learning model that is trained to output a fraudulent measure, one or more data objects indicative of validating the fraudulent measure;
• and
• initiating performance of one or more prediction-based actions in response to the generating of the one or more data objects.

At least one non-transitory computer readable medium, the at least one non-transitory computer readable medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving a dataset that includes a first subset and a second subset associated with a first resource system;
• down-sampling the first subset but not the second subset, wherein down-sampling the first subset includes:
• standardizing the first subset;
• and
• transforming the standardized first subset into a unique hash key for matching with a stored data string;
• generating a first feature for a machine learning model based on the down-sampled first subset;
• generating a second feature for the machine learning model based on the second subset;
• generating, via input of at least one of the first feature or the second feature into the machine learning model that is trained to output a fraudulent measure, one or more data objects indicative of validating the fraudulent measure;
• and
• initiating performance of one or more prediction-based actions in response to the generating of the one or more data objects.