Artificial-intelligence-based user data validation

A non-transitory computer-readable medium storing processor-executable instructions, wherein the instructions include:

• receiving an indication that a user record associated with a user has been updated with a first change;
• determining a confidence score that the first change is valid;
• and
• in response to a determination that the confidence score has not met a confidence threshold:
• filtering a set of communications associated with the user to create a subset of communications, wherein filtering includes determining whether a respective communication of the set of communications includes a communication topic of the first change;
• determining, via an analysis of the subset of communications by a first machine learning model, whether the first change has met one or more of a set of validity criteria, wherein:
• a communication of the subset of communications includes a first portion, a second portion, and a third portion, and
• the first portion and the third portion are analyzed by the first machine learning model before the second portion is analyzed;
• and
• in response to a determination that the first change has not met the set of validity criteria:
• based on a first outcome of a set of reporting criteria, automatically generating a prompt with a first suggested revision of the first change;
• and
• based on a second outcome of the set of reporting criteria, automatically revising the first change with the first suggested revision.

A system comprising:

• memory hardware configured to store instructions;
• and
• processor hardware configured to execute instructions stored by the memory hardware, wherein the instructions include:
• receiving an indication that a user record associated with a user has been updated with a first change;
• determining a confidence score that the first change is valid;
• and
• in response to a determination that the confidence score has not met a confidence threshold:
• filtering a set of communications associated with the user to create a subset of communications, wherein filtering includes determining whether a respective communication of the set of communications includes a communication topic of the first change;
• determining, via an analysis of the subset of communications by a first machine learning model, whether the first change has met one or more of a set of validity criteria, wherein:
• a communication of the subset of communications includes a first portion, a second portion, and a third portion, and
• the first portion and the third portion are analyzed by the first machine learning model before the second portion is analyzed;
• and
• in response to a determination that the first change has not met the set of validity criteria:
• based on a first outcome of a set of reporting criteria, automatically generating a prompt with a first suggested revision of the first change;
• and
• based on a second outcome of the set of reporting criteria, automatically revising the first change with the first suggested revision.