Studies on Saudi accounting practices have identified evidence of creative accounting in the financial statements of listed companies.Despite the application of various fraud detection methods, identifying legal but misleading manipulations remains challenging.This paper extends the Creative Accounting Detection Model (CADM), an LSTM-based model originally proposed by Bineid et al. (2023, 2024) for detecting creative accounting.Two versions, (CADM1) and (CADM2), were trained on two simulated datasets with different bases, achieving 100% and 95% accuracy, respectively.Testing on the energy sector (2019-2023), CADM1 identified one company as engaging in creative accounting, while CADM2 classified all companies as non-creative with greater confidence stability.The findings establish CADM as a robust, scalable solution for the early detection of financial manipulation.By combining predictive strength with explainability, CADM can be employed to advance current approaches to forensic accounting and risk analytics, offering valuable insights to regulators, auditors, and decision-makers.

Global financial scandals have demonstrated the harmful impact of creative accounting, a practice where managers creatively manipulate financial reports to conceal a company's actual performance and influence stakeholders' decision-making.Studies showed that Saudi-listed companies use it in preparing financial statements.Despite posing a significant risk to the Saudi financial market, detecting it using ordinary auditing procedures remains challenging.Big data analytics has provided practical applications in auditing, and recently, the employment of Deep Learning in fraud detection has delivered remarkably accurate results.Still, limited research has considered it in detecting creative accounting.This study proposes a novel framework using a hybrid learning approach.It suggests training on a simulated dataset of financial statements prepared (i.e., deliberately manipulated) based on financial statements available in the literature for supervised learning.It is then tested on real-world financial reports from the Saudi Open Data and Saudi Statistics.Our framework contributes to the literature with a new governing approach to limit creative accounting and improve financial reporting quality.