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

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