Machine Learning Risk Assessment Scoring
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Solution Overview
Problem
Existing risk analysis processes for organizations are time-consuming and manually intensive, leading to bottlenecks in implementing changes and compliance with regulations.
Innovation Solution
A system utilizing a machine learning model trained on a corpus of prior risk assessments to generate risk assessments and mitigations for novel initiative requests, reducing the need for manual review and speeding up the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual risk analysis processes are used, then risk assessment accuracy is maintained, but processing time increases and productivity decreases
Solution Approach 1:
The system creates a digital copy of the manual risk assessment process by training a machine learning model on historical risk assessments, initiative requests, and mitigations. This trained model then automatically generates risk assessments and mitigations for new initiatives, replicating the expertise of human risk advisors while operating at machine speed.
Solution Approach 2:
The system performs preliminary risk assessment generation automatically before human review. The machine learning model pre-generates risk assessments and mitigations, which are then presented to risk advisors for verification and refinement, reducing their workload and accelerating the overall process.
2Reliability
If manual risk analysis processes are used, then comprehensive risk evaluation is achieved, but resource consumption increases
Solution Approach 1:
The machine learning model serves itself by automatically generating risk assessments and mitigations without requiring extensive manual intervention. The system learns from historical data and autonomously applies this knowledge to new initiatives, reducing the energy and time investment required from human risk advisors.
Solution Approach 2:
The machine learning model acts as an intermediary between historical risk assessments and new initiative evaluations. It translates past expertise into automated recommendations, reducing the direct burden on human advisors while maintaining comprehensive risk evaluation through their subsequent review and refinement.
3Productivity
If automated machine learning models are used, then processing speed increases, but measurement precision may decrease
Solution Approach 1:
The system implements feedback loops where risk advisors review and refine the machine learning model's generated risk assessments. This human feedback is used to continuously improve and retrain the model, ensuring that automated assessments maintain high accuracy while benefiting from rapid processing capabilities.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for training and deploying a machine learning model to generate an assessment of risks and mitigations in response to a novel initiative request. After generating labeled data from a corpus of prior risk assessments, a machine learning model may be trained to programmatically generate a risk assessment in response to a novel initiative request. A risk auditor may subsequently review the risk assessment generated using the machine learning model to provide various feedback reflecting the accuracy of the risks and mitigations. The machine learning model may then be retrained based on the feedback provided by the risk auditor to provide more accurate risks and mitigations in response to future initiatives.