Modifying artificial intelligence modules of a fraud detection computing system

A method for execution by a fraud detection computing system, the method comprising:

• selecting an entry from a current transaction-answer (TA) matrix for evaluation by a fraud detection computing system, wherein the entry is regarding a transaction, and wherein the current TA matrix includes a prediction field for each entry;
• determining whether the prediction field associated with the entry includes an indication that a predicted fraud answer for the transaction is favorable;
• when the predicted fraud answer is unfavorable:
• obtaining inputted data utilized by a fraud assessment model utilized to produce the unfavorable predicted fraud answer, wherein the fraud assessment model includes a plurality of artificial intelligence (AI) modules;
• obtaining additional data regarding the transaction;
• augmenting the inputted data with the additional data to produce updated data;
• executing the fraud assessment model utilizing the updated data produce an updated fraud evaluation answer;
• determining whether the updated fraud evaluation answer is favorable;
• when the updated fraud evaluation answer is favorable, determining a difference between the inputted and the additional data to produce difference data;
• and
• updating an AI module of the plurality of AI modules based on the difference data.