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Identifying Undisclosed Related Party Relationships and Revenue Recognition Irregularities: A Rule-Based Analytical Approach for Audit Planning

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  • Liang, Dun

Abstract

Related party transactions and revenue recognition manipulations remain persistent sources of financial statement fraud, posing significant challenges to audit procedures and investor protection. This research develops a rule-based analytical framework designed to identify potential irregularities in corporate financial disclosures during the audit planning phase. The proposed approach integrates network analysis techniques for detecting undisclosed related-party relationships by cross-referencing entity information and time-series pattern-detection methods for identifying suspicious revenue recognition behaviors, including period-end concentration and cash flow divergence. A composite risk scoring mechanism combines multiple indicators to prioritize audit attention. Empirical analysis using SEC EDGAR filings from 847 publicly traded companies demonstrates the framework's effectiveness, achieving a precision rate of 78.3% in flagging high-risk company filings

Suggested Citation

  • Liang, Dun, 2026. "Identifying Undisclosed Related Party Relationships and Revenue Recognition Irregularities: A Rule-Based Analytical Approach for Audit Planning," Journal of Science, Innovation & Social Impact, Pinnacle Academic Press, vol. 2(2), pages 26-36.
    • Handle: RePEc:dba:jsisia:v:2:y:2026:i:2:p:26-36
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