System and method to dynamically evaluate feedback data

A method, comprising:

• receiving a first communication operation associated with a first entity;
• and
• executing a machine learning algorithm to perform one or more operations comprising:
• determining first feedback data in the first communication operation, wherein the first communication operation comprises an interaction over a communication network and first feedback data is determined from that interaction as information provided by the first entity, the first feedback data comprising a first plurality of datapoints that represents information provided by the first entity;
• in response to determining the first feedback data in the first communication operation, determining a first plurality of categorization formats associated with the first plurality of datapoints, each communication parameter being associated with each datapoint;
• assigning a first specific weighted value to each datapoint of the first plurality of datapoints based on respective categorization formats;
• comparing the first plurality of datapoints to a plurality of reference datapoints indicating user information associated with one or more user profiles, wherein the plurality of reference datapoints are in reference interaction data previously processed and determined to match user information associated with one or more user profiles;
• performing one or more probabilistic linkage operations to connect one or more interactions in the communication network where the first entity is attempting to access multiple accounts and network resources associated with the one or more user profiles, wherein the one or more probabilistic linkage operations comprise correlating user information from feedback data relating to the one or more interactions and one or more activities associated with the one or more user profiles;
• determining whether the first plurality of datapoints at least partially matches the plurality of reference datapoints;
• determining a first plurality of weighted values for each of the first plurality of datapoints that match the plurality of reference datapoints;
• aggregating the first plurality of weighted values into a first match value;
• determining whether the first match value is less than a first value threshold, wherein the first value threshold comprises a lower boundary that, when the first match value is less than the first value threshold, triggers association of the first entity to the one or more user profiles in an authentication process;
• and
• in response to determining that the first match value is less than the first value threshold, determining that the first entity is associated with the one or more user profiles.

A non-transitory computer-readable medium storing instructions that when executed by a processor cause the processor to:

• receive a first communication operation associated with a first entity;
• and
• execute a machine learning algorithm to:
• determine first feedback data in the first communication operation, wherein the first communication operation comprises an interaction over a communication network and first feedback data is determined from that interaction as information provided by the first entity, the first feedback data comprising a first plurality of datapoints that represents information provided by the first entity;
• in response to determining the first feedback data in the first communication operation, determine a first plurality of categorization formats associated with the first plurality of datapoints, each communication parameter being associated with each datapoint;
• assign a first specific weighted value to each datapoint of the first plurality of datapoints based on respective categorization formats;
• compare the first plurality of datapoints to a plurality of reference datapoints indicating user information associated with one or more user profiles, wherein the plurality of reference datapoints are in reference interaction data previously processed and determined to match user information associated with one or more user profiles;
• perform one or more probabilistic linkage operations to connect one or more interactions in the communication network where the first entity is attempting to access multiple accounts and network resources associated with the one or more user profiles, wherein the one or more probabilistic linkage operations comprise correlating user information from feedback data relating to the one or more interactions and one or more activities associated with the one or more user profiles;
• determine whether the first plurality of datapoints at least partially matches the plurality of reference datapoints;
• determine a first plurality of weighted values for each of the first plurality of datapoints that match the plurality of reference datapoints;
• aggregate the first plurality of weighted values into a first match value;
• determine whether the first match value is less than a first value threshold, wherein the first value threshold comprises a lower boundary that, when the first match value is less than the first value threshold, triggers association of the first entity to the one or more user profiles in an authentication process;
• and
• in response to determining that the first match value is less than the first value threshold, determine that the first entity is associated with the one or more user profiles.