Systems and methods for automating incident severity classification

An apparatus for automating incident severity classification, the apparatus comprising:

• communications circuitry configured to:
• receive a historical incident dataset, the historical incident dataset including information regarding a set of historical incidents and an assigned severity classification for each historical incident in the set of historical incidents, and
• receive a new incident dataset, the new incident dataset including information regarding a set of new incidents and the assigned severity classification for each new incident in the set of new incidents;
• a model generator configured to:
• cleanse the historical incident dataset by removing one or more predetermined ancillary and irrelevant data from the historical incident dataset to prevent overfitting of the historical incident dataset and by scaling values within the historical incident dataset to a predetermined standard scale;
• a prediction engine configured to, after cleansing the historical incident dataset, generate a set of predicted severity classifications for the set of new incidents using a machine learning model, the historical incident dataset, and the new incident dataset;
• and
• comparator circuitry configured to determine that a severity classification mismatch between at least one assigned severity classification and a predicted severity classification of the set of predicted severity classifications exists for the new incident dataset,
• wherein the communications circuitry is further configured to, in response to the comparator circuitry determining that the severity classification mismatch exists, prompt further investigation of the severity classification mismatch through outputting an indication of the severity classification mismatch.

A computer program product for automating incident severity classification, the computer program product comprising at least one non-transitory computer-readable storage medium storing software instructions that, when executed, cause an apparatus to:

• receive, a historical incident dataset, the historical incident dataset including information regarding a set of historical incidents and an assigned severity classification for each historical incident in the set of historical incidents;
• receive a new incident dataset, the new incident dataset including information regarding a set of new incidents and the assigned severity classification for each new incident in the set of new incidents;
• cleanse the historical incident dataset by removing one or more predetermined ancillary and irrelevant data from the historical incident dataset to prevent overfitting of the historical incident dataset and by scaling values within the historical incident dataset to a predetermined standard scale;
• after cleansing the historical incident dataset, generate a set of predicted severity classifications for the set of new incidents using a machine learning model, the historical incident dataset, and the new incident dataset;
• determine that a severity classification mismatch between at least one assigned severity classification and a predicted severity classification of the set of predicted severity classifications exists for the new incident dataset;
• and
• in response to determining that the severity classification mismatch exists, prompting further investigation of the severity classification mismatch by outputting an indication of the severity classification mismatch.