Machine Learning Based Auto-Reconciliation By Generating Remediation Records As Necessary Based On Time-Aware Models

A method comprising:

• obtaining training datasets of historical data, the training datasets comprising:
• discrepancies between historical datasets of event records;
• and
• labels specifying one of:
• an auto-reconciliation process performed without human intervention or a manual reconciliation process performed by a human for remediating the discrepancies between the historical datasets of event records;
• training a machine learning model to determine whether to perform (a) auto-reconciliation operations, without human intervention, to remediate discrepancies between datasets or (b) manual remediation operations, with human intervention, to remediate the discrepancies between the datasets;
• obtaining a target pair of datasets comprising:
• a first dataset comprising a first set of event records;
• and
• a second dataset comprising a second set of event records;
• analyzing the first set of event records and the second set of event records to identify a first discrepancy between the first set of event records and the second set of event records;
• and
• applying the machine learning model to the first discrepancy to generate a first selection to perform an auto-reconciliation operation, without human intervention, to remediate the first discrepancy,
• wherein the method is performed by at least one device including a hardware processor.

A system comprising:

• at least one device including a hardware processor;
• the system being configured to perform operations comprising:
• obtaining training datasets of historical data, the training datasets comprising:
• discrepancies between historical datasets of event records;
• and
• labels specifying one of:
• an auto-reconciliation process performed without human intervention or a manual reconciliation process performed by a human for remediating the discrepancies between the historical datasets of event records;
• training a machine learning model to determine whether to perform (a) auto-reconciliation operations, without human intervention, to remediate discrepancies between datasets or (b) manual remediation operations, with human intervention, to remediate the discrepancies between the datasets;
• obtaining a target pair of datasets comprising:
• a first dataset comprising a first set of event records;
• and
• a second dataset comprising a second set of event records;
• analyzing the first set of event records and the second set of event records to identify a first discrepancy between the first set of event records and the second set of event records;
• and
• applying the machine learning model to the first discrepancy to generate a first selection to perform an auto-reconciliation operation, without human intervention, to remediate the first discrepancy.