Auditul digital și analiza datelor: noi frontiere în detectarea fraudelor financiare

Abstract

This paper examines the convergence of digital auditing and advanced data analytics in the context of detecting and preventing financial fraud - one of the most pressing challenges facing contemporary accounting systems and internal control frameworks. As financial transactions increasingly migrate to digital platforms and the volume of data generated by economic entities grows exponentially, traditional audit methods prove insufficient for the timely identification of fraudulent schemes. The study traces the evolution of digital audit tools, from early applications of accounting journal analysis to modern systems grounded in artificial intelligence (AI), machine learning, and blockchain technology. The international regulatory framework is examined - including ISA 240, ISAE 3402, and PCAOB standards - alongside the principal software platforms employed in practice (ACL/Galvanize, IDEA, Tableau, Power BI), as well as established statistical techniques such as Benford's Law analysis, anomaly detection, and supervised classification models. The case studies presented illustrate concrete applications in the banking sector, insurance, and capital markets. The paper concludes with a critical assessment of current limitations and priority research directions, including the ethical considerations raised by the use of AI in audit processes. CZU: [657.6:004.8]:343.53(100+478); JEL: M41, M42, M28