Machine Learning Policy Models for Transaction Audit
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Solution Overview
Problem
Existing automated expense management systems lack efficient methods for auditing transactions against organizational policies, leading to potential fraud and compliance issues.
Innovation Solution
The system employs machine learning policy models trained on historical data to automatically determine policy compliance by comparing extracted tokens from receipts to predefined policy questions, generating audit alerts for policy violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated expense management systems are used to analyze and monitor travel expenses, then worker productivity is increased and time spent on expense reports is reduced, but the systems lack efficient methods for auditing transactions against organizational policies, leading to potential fraud and compliance issues
Solution Approach 1:
The system performs self-auditing by automatically comparing transaction data against stored organizational policies using machine learning models. The automated policy audit service evaluates compliance without requiring external human intervention, allowing the system to monitor and control expenses while maintaining accuracy and productivity simultaneously
Solution Approach 2:
Manual auditing processes are replaced with automated machine learning-based policy audit models. These models process transaction data and determine policy compliance automatically, eliminating the need for manual review while maintaining or improving compliance reliability compared to human auditing
2Reliability
If manual auditing processes are used to ensure policy compliance, then fraud and compliance issues can be detected, but the process is time-consuming and reduces worker productivity
Solution Approach 1:
The system performs self-auditing by automatically comparing transaction data against stored organizational policies using machine learning models. The automated policy audit service evaluates compliance without requiring external human intervention, allowing the system to monitor and control expenses while maintaining accuracy and productivity simultaneously
Solution Approach 2:
Manual auditing processes are replaced with automated machine learning-based policy audit models. These models process transaction data and determine policy compliance automatically, eliminating the need for manual review while maintaining or improving compliance reliability compared to human auditing
3Device complexity
If traditional auditing methods are used without machine learning, then the system structure is simpler, but the auditing speed and accuracy are reduced
Solution Approach 1:
Organizational policies are pre-stored in the system with structured formats and key indicators identified in advance. Machine learning models are pre-trained on historical transaction data to recognize policy violations. This preliminary preparation enables rapid real-time auditing without complex ad-hoc analysis, improving speed while maintaining manageable system complexity
Solution Approach 2:
The system transforms unstructured policy documents into structured parameters and features that machine learning models can process efficiently. By converting policies into quantifiable parameters with identified key indicators, the system achieves fast automated auditing without requiring excessively complex infrastructure
4Measurement precision
If comprehensive policy auditing is performed on all transactions, then compliance accuracy is improved, but the computational resources and processing time increase
Solution Approach 1:
The system extracts only the key indicators and critical features from transaction data and organizational policies that are most relevant for compliance determination. By focusing on these extracted key features rather than analyzing all raw data comprehensively, the system achieves high compliance accuracy while significantly reducing computational resource requirements
Solution Approach 2:
The machine learning models are trained on extensive historical data to achieve high accuracy, but during operation, they apply this learned knowledge to perform partial audits focusing on high-risk transactions and key policy areas. This allows the system to maintain measurement precision while managing computational resource consumption
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes receiving receipt data associated with an entity. Policy questions associated with the entity are associated with at least one policy question answer that corresponds to a conformance or a violation of a policy selected by the entity. For each policy question, a machine learning policy model is identified for the policy question that includes, for each policy question answer, receipt data features that correspond to the policy question answer. The machine learning policy model is used to automatically determine a selected policy question answer to the policy question by comparing features of extracted tokens to respective receipt data features of the policy question answers that are included in the machine learning policy model. In response to determining that the selected policy question answer corresponds to a policy violation, an audit alert is generated.