Semantic Evaluation System for Expense Audit Automation
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Solution Overview
Problem
Enterprises face challenges in differentiating between necessary and unnecessary travel expenses, which can be resource-intensive to audit due to the lack of contextual information and manual effort required to scrutinize expenses for conformance to policies and regulatory schemas.
Innovation Solution
The implementation generates explanatory electronic documents based on semantic evaluation using a knowledge graph, semantic context association, and user profile associations to identify necessary and unnecessary expenses, providing explanations for abnormal patterns and optimizing the identification of unnecessary spending.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual audit methods are used to scrutinize expenses, then detailed examination of each expense can be performed, but the process becomes resource-burdensome and time-consuming
Solution Approach 1:
The system enables self-service by automatically generating explanatory documents that justify expenses without requiring manual auditor intervention. The semantic evaluation system autonomously compares expenses against peer patterns and enterprise policies to produce audit-ready explanations, freeing auditors from routine scrutiny work while maintaining detailed examination capabilities.
Solution Approach 2:
The patent introduces an intermediary semantic evaluation system that acts as a mediator between raw expense data and human auditors. This intermediary automatically generates explanatory documents that bridge the gap between expense submissions and audit decisions, reducing the time burden on auditors while preserving audit accuracy through structured semantic analysis.
2Loss of information
If contextual information is collected and analyzed for each expense, then insight into expense appropriateness is improved, but the complexity of the audit process increases
Solution Approach 1:
The system segments the complex audit process into distinct automated components: contextual information collection, peer expense pattern matching, semantic evaluation, and explanatory document generation. Each segment handles specific aspects of analysis independently, making the overall complex process manageable and automatable while ensuring comprehensive contextual information is captured and analyzed.
3Reliability
If comprehensive expense analysis is performed to differentiate necessary from unnecessary expenses, then compliance with enterprise policies is improved, but computing resources are consumed
Solution Approach 1:
The system performs preliminary action by pre-computing and storing peer expense patterns and contextual information in structured formats before audit time. This advance preparation enables rapid semantic evaluation during actual audits, maintaining high policy compliance reliability while reducing real-time computing resource consumption through efficient data retrieval and comparison.
Data Source
AI summary
Implementations are directed to providing an explanatory electronic document with actions including providing a target subject profile based on user input and one or more ontologies, the target subject profile including associations describing a subject at respective degrees of specificity, providing a set of peer user profiles using semantic user profile association between the user profile and each peer user profile in a superset of peer user profiles, retrieving one or more peer subject profiles, each peer subject profile being associated with a peer user profile in the set of peer user profiles, and including associations describing a past subject experienced by a peer user, filtering at least one association from a peer subject profile based on data provided in a knowledge graph, and providing at least one explanatory text string associated with the subject based on at least one remaining association in the peer subject profile.