Adaptability of Indirect Methods in Income Reconstruction: A Systematic Literature Review

Muhammad Alfin Yusra, Tarjo Tarjo, Rita Yuliana

Abstract


This research aims to evaluate the adaptability of indirect methods in income reconstruction across various forensic cases. Income reconstruction is an important method in forensic audits to uncover hidden income, especially in cases of fraud and tax evasion. However, there are often limitations in data access, so indirect methods are needed to estimate revenue. Through the Systematic Literature Review (SLR) approach, this article examines the application of indirect methods in investigating tax and corporate financial fraud. Data was obtained from the Scopus database and analyzed to determine the effectiveness of this method in the context of limited data. The results of this study indicate that indirect methods in income reconstruction are highly adaptable for identifying hidden income, particularly when data is limited. This research highlights the important role of indirect methods as tools in forensic accounting with wide practical applications.

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References


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