AI Risk Compliance Platform Remediation Automation
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Solution Overview
Problem
Current governance, risk management, and compliance (GRC) systems are inefficient due to manual and time-consuming remediation processes, leading to increased costs and delayed responses to regulatory changes, which result in poor customer satisfaction and misinformed risk and compliance decisions.
Innovation Solution
A machine learning-based intelligence platform that processes historical risk and compliance data to generate structured semantic models, predicting risk and compliance insights and automatically determining remediation solutions, thereby reducing manual intervention and improving decision-making efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual remediation processes are used, then compliance with regulations can be achieved, but the process becomes time-consuming and costly
Solution Approach 1:
The patent replaces manual mechanical processes with an automated intelligence platform that uses machine learning models, natural language processing, and semantic analysis to perform remediation tasks. The system automatically ingests regulatory guidance, identifies affected customers, determines remediation solutions, and generates actionable insights, eliminating the need for manual analysis and significantly reducing turnaround time while maintaining compliance reliability.
2Reliability
If manual remediation processes are used, then compliance actions can be performed, but costs increase
Solution Approach 1:
The intelligence platform performs self-service by automatically analyzing regulatory guidance, identifying compliance issues, determining affected customers, and generating remediation solutions without requiring extensive human intervention. The system uses trained machine learning models to autonomously process data and generate actionable insights, reducing both computing resource waste through efficient algorithms and human resource consumption through automation.
3Measurement precision
If manual analysis of regulatory guidance is performed, then compliance issues can be identified, but the response is delayed
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on historical regulatory guidance and compliance data before new regulations are issued. When new regulatory guidance is received, the pre-trained models can immediately analyze and identify compliance issues without requiring manual setup or analysis from scratch, thereby maintaining high identification accuracy while achieving rapid response speeds.
4Measurement precision
If extensive manual intervention is used, then accurate remediation decisions can be made, but productivity decreases
Solution Approach 1:
The intelligence platform incorporates feedback mechanisms where remediation decisions and outcomes are continuously monitored and fed back into the machine learning models for retraining and improvement. This allows the system to maintain high decision accuracy through continuous learning from real-world outcomes while operating at automated speeds, eliminating the need for extensive manual intervention in each decision cycle.
Data Source
AI summary
A device may receive historical risk data identifying historical risks associated with entities, and historical compliance data identifying historical compliance actions performed by the entities. The device may train a machine learning model with the historical risk data and the historical compliance data to generate a structured semantic model, and may receive entity risk data identifying new and existing risks associated with an entity. The device may receive entity compliance data identifying new and existing compliance actions performed by the entity, and may process the entity risk data and the entity compliance data, with the structured semantic model, to determine risk and compliance insights for the entity. The risk and compliance insights may include insights associated with a key performance indicator, a compliance issue, a regulatory issue, an operational risk, a compliance risk, or a qualification of controls. The device may perform actions based on the risk and compliance insights.