ML Clustering Model for Auditable Entities
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Solution Overview
Problem
Manual assembly of auditable entities (AEs) for internal audits is inefficient and qualitative, leading to suboptimal assignment of audits and lack of standardization, which hampers the accuracy and efficiency of audit processes.
Innovation Solution
A machine learning-based clustering model that automatically clusters business processes into auditable entities based on similarity analyses of attributes, creating well-defined, scientifically credible clusters for improved audit efficiency and accurate auditor assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual assembly of auditable entities is used, then flexibility in customization is improved, but productivity and standardization are worsened
Solution Approach 1:
The system performs preliminary automated clustering of business processes into auditable entities using machine learning algorithms before the manual assignment phase. This preliminary action creates standardized groupings that improve productivity while allowing subsequent manual customization by auditors based on specific audit requirements.
Solution Approach 2:
The patent introduces an automated clustering system as an intermediary between raw business process data and manual audit assignment. This intermediary layer processes and groups business processes using ML algorithms, providing a standardized foundation that both improves efficiency and maintains flexibility for human judgment.
2Ease of operation
If manual assembly of auditable entities is used, then ease of operation is improved, but measurement precision and reliability are worsened
Solution Approach 1:
The patent replaces the mechanical/manual process of assessing business process similarity with an automated machine learning-based clustering system. This substitution maintains ease of operation through automated processing while dramatically improving measurement precision by using consistent, data-driven similarity metrics rather than subjective human judgment.
Solution Approach 2:
The system creates standardized templates and models of auditable entities through automated clustering, which can then be replicated and used as reference standards. This copying approach ensures consistent, precise measurements across different audit assemblies while maintaining operational simplicity through template-based workflows.
3Productivity
If automated clustering is implemented, then productivity is improved, but device complexity is worsened
Solution Approach 1:
The patent segments the audit assembly system into distinct functional modules: data ingestion module, machine learning clustering module, and audit assignment module. This segmentation improves productivity by enabling parallel processing while managing complexity through clear separation of concerns and independent module development.
Solution Approach 2:
The automated clustering system is designed as a universal platform that can handle multiple types of business processes and audit requirements through configurable parameters and algorithms. This multi-functionality approach improves productivity across diverse audit scenarios while containing complexity through a single, versatile system architecture rather than multiple specialized systems.
Data Source
AI summary
Techniques are described for automatic creation of optimal auditable entities (AEs) using a machine learning (ML)-based clustering model. The clustering model, when executed on one or more computing devices within an audit system of a company, is configured to automatically cluster the company's business processes into AEs based on similarity analyses of business process attributes. More specifically, in some examples, the clustering model ingests business processes and their corresponding attributes from a database, automatically clusters together business processes to achieve maximum intra-cluster similarity scores, and outputs the final clusters as model AEs. The resulting model AEs may be used as functional units for internal audits of the company's business processes. The resulting model AEs may improve audit efficiency due to the model AEs including only highly similar business processes. In addition, the resulting model AEs may enable more accurate assignment of audits based upon auditor experience and technical skills.