Contextual Mapping Engine for Business Process Control Redundancy
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Solution Overview
Problem
Business process control design is hindered by extensive duplication and redundancy in risk statements and control requirements, especially when written by diverse individuals, and inefficient manual mapping of control requirements to policies and procedures.
Innovation Solution
A contextual mapping engine is used to compare and generate matching scores between statements, allowing for the removal of redundant statements and the establishment of links between lists, thereby reducing redundancy and improving efficiency in business process control design and risk analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If risk statements and control requirements are written by a team of diverse individuals, then the coverage and perspective of risk identification are improved, but the degree of duplication and redundancy increases significantly
Solution Approach 1:
The patent merges multiple risk statements and control requirements into a consolidated, non-redundant set by comparing semantic similarity between statements. The system automatically identifies and combines duplicate or substantially identical statements, maintaining the comprehensive coverage achieved by diverse contributors while eliminating redundancy.
Solution Approach 2:
The system discards redundant risk statements and control requirements that have been identified through semantic comparison, while recovering and preserving the unique substantive content. This allows the system to retain the value of diverse input while removing duplicate information.
2Measurement precision
If manual mapping of control requirements to policies and procedures is performed, then the accuracy of mapping can be maintained, but the efficiency and time consumption are significantly reduced
Solution Approach 1:
The patent replaces the manual mechanical process of mapping control requirements to policies and procedures with an automated computational system. The system uses semantic analysis and similarity comparison algorithms to automatically establish mappings, substituting human manual work with computer-based processing while maintaining mapping quality.
Solution Approach 2:
The system creates automated copies of control requirements and their corresponding policies and procedures through computational matching. Instead of manual copying and linking, the system automatically generates and maintains the relationships between control requirements and their implementing documents through algorithmic comparison.
3Quantity of substance
If the list of risk statements and control requirements grows into the hundreds and thousands of statements, then the comprehensiveness of risk coverage is improved, but the manageability and processing efficiency deteriorate
Solution Approach 1:
The system merges thousands of individual risk statements and control requirements into a more manageable consolidated structure by identifying and combining semantically equivalent statements. This reduces the effective number of unique items that need to be managed while preserving the comprehensive risk coverage.
Solution Approach 2:
The system discards redundant statements from the large list, recovering only the unique substantive content. This filtering process reduces the manageability burden by removing duplicates while maintaining the comprehensive coverage achieved through the extensive original list.
Data Source
AI summary
Systems and methods are provided for designing business process controls utilizing a contextual mapping engine. A set of statements relating to business process controls are received and stored in a repository. The contextual mapping engine compares the statements to determine matching scores between statements. Statements with matching scores greater than a predetermined threshold are removed from the repository.