Detection of earnings manipulation in financial reports: a data-driven approach

D. Divya, Vivek M. Bhasi and O.N. Arunkumar

International Journal of Accounting, Auditing and Performance Evaluation, 2025, vol. 21, issue 3/4, 499-512

Abstract: Earnings manipulation attracts attention from both industry and academia as detection of earnings manipulation helps them to invest wisely rather than based on falsified financial statements. This paper discusses a process through which earnings manipulators can be identified. This can help shareholders to detect companies who made modifications to their financial statements. Earlier researchers developed many mathematical models to identify earnings manipulators. However, their works require an in-depth understanding of financial ratios and accounting principles. The advancement of data-driven algorithms has now brought comparable machine learning detection techniques into the picture, wherein data scientists can use historical data to detect earnings manipulators. This paper discusses the use of an artificial neural network (ANN) for detecting manipulations in the dataset.

Keywords: earnings manipulation; data analytics; neural network; financial report; fraud; financial ratio. (search for similar items in EconPapers)
Date: 2025
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