CADM: Creative Accounting Detection Model in Saudi-Listed Companies

Abstract

In business, financial statements are the primary source of information for investors and other stakeholders. Despite extensive regulatory efforts, the quality of financial reporting in Saudi Arabia still requires improvement, as prior studies have documented evidence of creative accounting. This practice occurs when managers manipulate accounting figures within the boundaries of the International Financial Reporting Standards to present a more favourable image of the company. Although various fraud detection methods exist, identifying manipulations that are legal yet misleading remains a significant challenge. This research introduces the Creative Accounting Detection Model (CADM), a deep learning (DL)-based approach that employs Long Short-Term Memory (LSTM) networks to identify Saudi-listed companies engaging in creative accounting. Two versions of the model were developed: CADM1, trained on a simulated dataset based on established accounting measures from the literature, and CADM2, trained on a dataset tailored to reflect financial patterns observed in the Saudi market. Both datasets incorporated financial and non-financial features derived from a preliminary survey of Saudi business experts. The models achieved training accuracies of 100% (CADM1) and 95% (CADM2). Both models were then tested on real-world data from the Saudi energy sector (2019–2023). CADM1 classified one company as engaging in creative accounting, whereas CADM2 classified all companies as non-creative but demonstrated greater stability in prediction confidence. To interpret these results, a follow-up qualitative study involving expert interviews confirmed CADM as a promising supplementary tool for auditors, enhancing analytical and oversight capabilities. These findings highlight CADM’s potential to support regulatory oversight, strengthen auditing procedures, and improve investor trust in the transparency of financial statements.