Through our study, we explore how data analytics can improve accounting an‎d auditing in todayʹs digital world. Traditional auditing methods based on manual auditing an‎d sampling are no longer effective in managing the vast an‎d complex financial data generated by modern systems. As organizations adopt technologies such as enterprise resource planning (ERP) systems, cloud platforms, an‎d electronic transactions, the need for intelligent an‎d automated audit tools is growing. We examine how big data analytics (BDA), artificial intelligence (AI), an‎d machine learning (ML) can enhance fraud detection, risk assessment, an‎d the accuracy of financial reporting. We review the literature an‎d summarize the key tools an‎d techniques used in modern auditing, such as big data platforms (Hadoop, Spark), machine learning algorithms (Ran‎dom Forest, SVM, DNN), an‎d audit tools (ACL, IDEA, Power BI, an‎d Tableau). The study demonstrates that the integration of artificial intelligence an‎d data analytics helps auditors transition from traditional manual methods to predictive an‎d continuous auditing. These new systems can analyze entire data sets, detect irregularities faster, an‎d improve decision-making. However, the study also highlights challenges related to ethics, data privacy, an‎d the need for qualified professionals. Finally, the study emphasizes that intelligent data-driven auditing can make financial reporting more accurate, transparent, an‎d reliable. It recommends that future research focus on practical applications of artificial intelligence, the ethical use of data, an‎d the adoption of technologies such as blockchain to ensure secure an‎d continuous auditing.

10/28/2025 12:00:00 AM

88024

1404/08/07

Study on Data Analysis Methods for Accounting an‎d Auditing

(كلان داده، يادگيري ماشين، تحليل داده)

(big data,machine learning,Data Analytics)