AI Control Testing System for Risk Mitigation
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Solution Overview
Problem
Current control testing processes are manual, time-consuming, and lack accuracy, requiring human testers to search multiple repositories for relevant documents and involve limited knowledge sharing, which hampers the efficiency and effectiveness of risk mitigation in organizations.
Innovation Solution
A system utilizing AI and machine learning techniques to automate control testing by extracting and classifying sentences from control documents, generating interpretations of questions, identifying relevant documents, and organizing meetings between testers to discuss test plan effectiveness, leveraging active and pro-active learning, Inverse Reinforcement Learning, and Natural Language Processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual control testing process is used, then human testers can understand and discuss control effectiveness, but the process consumes lot of time and lacks accuracy
Solution Approach 1:
The patent segments the control testing process into distinct automated components: sentence extraction from control documents, question identification and classification, document retrieval from repositories, and answer extraction. This segmentation allows each function to be automated independently, improving accuracy while reducing time consumption.
Solution Approach 2:
The patent introduces an intermediary system (automated testing system with AI components) that mediates between the control documents and the testers. This intermediary automatically performs document retrieval, question classification, and answer extraction, eliminating the time-consuming manual search while maintaining accuracy through systematic processing.
2Loss of information
If manual document retrieval from multiple repositories is performed, then relevant documents can be identified, but the process lacks accuracy and involves limited knowledge sharing
Solution Approach 1:
The patent creates a universal automated system that can retrieve documents from multiple different repositories using standardized processes. This multi-functional system handles various document types and repository formats uniformly, improving identification accuracy while increasing productivity through automation.
Solution Approach 2:
The patent implements feedback mechanisms where the automated system learns from previous testing interactions, improving document identification accuracy over time. The system uses feedback to refine question classification and document retrieval, continuously enhancing both accuracy and efficiency.
3Ease of operation
If human testers search for documents manually, then they can understand context, but the process is time-consuming and repetitive
Solution Approach 1:
The patent enables the system to serve itself by automatically retrieving documents, extracting questions, and identifying answers without human intervention. This self-service capability dramatically reduces the time required for document retrieval while maintaining ease of operation through automated workflows.
Solution Approach 2:
The patent performs preliminary actions by pre-processing control documents to extract and classify questions before the actual testing begins. Documents are organized and indexed in advance, so when testing occurs, the system can quickly retrieve relevant information without time-consuming manual search.
4Productivity
If automated AI system is used, then accuracy and efficiency improve, but the system complexity increases
Solution Approach 1:
The patent segments the complex automated system into modular functional components: document processing module, question classification module, document retrieval module, and answer extraction module. This segmentation manages system complexity by making each component independent and manageable while maintaining high overall productivity.
Data Source
AI summary
The present disclosure relates to system(s) and method(s) to perform control testing to mitigate risks in an organization. The system may extract sentences from control documents, and may classify the sentences into one of questions and non-questions, based on at least one of active learning and pro-active learning. Interpretations of the questions may thereafter be generated. Relevant documents related to each of the interpretations of the questions may be identified and extracted, from repositories. Artificial Intelligence (AI) may be used to identify the relevant documents. A cognitive master may be implemented to organize meetings between control testers and process owners for discussing over effectiveness of design and implementation test of test plans to mitigate the risks.