Method for scoring confidence of an algorithmically proposed risk

A system, comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured to:
• train a machine learning model with input materials and output materials from a plurality of prior risk assessments, wherein the input materials comprise text-based documents from prior manually processed initiative requests, the text-based documents comprising a plurality of intake forms, a plurality of answered questions, and a plurality of slide presentations, and wherein the output materials comprise risks and mitigations previously generated in response to the input materials;
• generate using the machine learning model, a risk assessment for an initiative request received from an intent requestor, wherein the risk assessment comprises one or more risks and one or more mitigations;
• display a risk assessment interface that presents summary information for the risk assessment, the one or more risks in association with the one or more mitigations, and a feedback interface that allows the intent requestor to provide feedback for each risk in the one or more risks;
• receive the feedback from the intent requestor, wherein the feedback comprises a relevance indicator and an accuracy indicator;
• and
• retrain the machine learning model based on the relevancy indicator and the accuracy indicator to cause the machine learning model to provide more accurate results in response to future initiative request.

A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

• training a machine learning model with input materials and output materials from a plurality of prior risk assessments, wherein the input materials comprise text-based documents from prior manually processed initiative requests, the text-based documents comprising a plurality of intake forms, a plurality of answered questions, and a plurality of slide presentations, wherein the output materials comprise risks and mitigations previously generated in response to the input materials;
• generating, using the machine learning model, a risk assessment for an initiative request received from an intent requestor, wherein the input materials comprise text-based documents from prior manually processed initiative requests, and wherein the risk assessment comprises one or more risks and one or more mitigations;
• displaying a risk assessment interface that presents summary information for the risk assessment, the one or more risks in association with the one or more mitigations, and a feedback interface that allows the intent requestor to provide feedback for each risk in the one or more risks;
• receiving the feedback from the intent requestor, wherein the feedback comprises a relevancy indicator and an accuracy indicator;
• and
• retraining the machine learning model based on the relevancy indicator and the accuracy indicator to cause the machine learning model to provide more accurate results in response to a future initiative request.