CADM: Big Data to Limit Creative Accounting in Saudi-Listed Companies

Abstract

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.

Deep learning for economic transformation: a parametric review

“…DL has emerged as a transformative force in various sectors of Pakistan's economy, addressing unique challenges through state-of-the-art algorithms and technologies. In Islamic economics, DL applications dependent upon deep belief networks (DBNs) and autoencoders have revolutionized zakat management and Islamic banking, enhancing financial stability and compliance with Sharia [13]. The agriculture and rural development sector has seen advancements with CNNs for crop health monitoring and long short term memory (LSTM) networks for market trend forecasting, significantly aiding in poverty  ISSN: 2502-4752 Indonesian J Elec Eng & Comp Sci, Vol.…”