Transaction Splitting Audits Using Time-Based Data Grouping
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Solution Overview
Problem
Current auditing methods are inefficient in detecting transaction splitting activities due to the complexity and large size of procurement transaction records, making it difficult to manually identify fraudulent practices.
Innovation Solution
A method and system utilizing a processor to sort and group accounting data based on time and transaction information, identifying candidate groups with date gaps below a threshold, and tagging potential split entries for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of procurement transaction records is performed, then detection accuracy of transaction splitting activities can be maintained, but auditing efficiency and productivity deteriorate due to the complexity and large size of the data
Solution Approach 1:
The patent segments the large volume of procurement transaction data into smaller, manageable groups based on vendor, time period, and other criteria. This segmentation enables automated processing while maintaining detection accuracy by applying analysis rules to each segment systematically, thereby resolving the contradiction between handling large data volumes and maintaining precision.
Solution Approach 2:
The patent replaces manual mechanical inspection with automated computer-based processing. The system uses algorithms to automatically sort, group, and analyze transaction records, substituting human manual review with computational methods that can process large datasets efficiently while maintaining consistent detection accuracy through systematic application of analysis rules.
2Loss of time
If random sampling of transaction records is used for auditing, then auditing time is reduced, but detection capability of transaction splitting activities deteriorates
Solution Approach 1:
The patent applies preliminary actions by pre-sorting and pre-grouping transaction data before analysis based on vendor, time period, and transaction characteristics. This preliminary organization enables the system to quickly identify potential transaction splitting patterns without requiring time-consuming random sampling, thereby reducing auditing time while maintaining or improving detection capability through systematic data organization.
Solution Approach 2:
The system incorporates feedback mechanisms where transaction records are analyzed, patterns are identified, and results feed back into the sorting and grouping process. This iterative feedback loop allows the system to continuously refine its detection capabilities, improving reliability over time while maintaining efficient processing speeds through learned patterns from previous analyses.
3Reliability
If complete manual inspection of all transaction records is performed, then detection capability is maximized, but loss of time and resources increases significantly
Solution Approach 1:
The patent applies partial action by focusing analysis on specific groups of transactions that exhibit characteristics of potential splitting activities, rather than uniformly inspecting all records. The system identifies and concentrates resources on high-risk segments based on sorting criteria such as vendor, time period, and transaction amount patterns, achieving effective detection with reduced overall processing time by not uniformly applying full inspection to every single record.
Data Source
AI summary
A method for auditing to detect transaction splitting activities is related to a plurality of entries of accounting data each including at least a transaction information sub-entry and a time sub-entry. The method includes: performing a sorting operation on the accounting data based on the time sub-entries, so as to create a sorted list of accounting data; performing a grouping operation on the sorted list of accounting data, based on at least one grouping parameter, so as to obtain a plurality of groups of accounting data; and with respect to each of the groups, when the entries of accounting data included in the group indicate a date gap smaller than a predetermined time threshold, tagging the group as a candidate group, and tagging each of the entries of accounting data in the group as a potential split entry.