Evaluation of Blockchain Functions and Machine Learning Algorithms in Enhancing Future-Oriented Auditing Systems with a Focus on Fraud Detection and Public Trust Improvement

Abstract

The swift advancement of emerging technologies, most notably blockchain and machine learning, has introduced profound changes to the auditing profession. These innovations not only improve the precision and transparency of financial reporting but also open new avenues for strengthening public trust and enhancing the detection of financial fraud. The purpose of this research is to examine the role of blockchain and machine learning in shaping future-oriented auditing frameworks, with particular emphasis on fraud identification and the reinforcement of stakeholder confidence. The methodology of this study combines a systematic review of scholarly publications from the past five years with an analysis of authentic financial data drawn from international audit reports. The evidence suggests that blockchain contributes to the permanence of financial records and ensures greater transparency across transaction chains. At the same time, machine learning algorithms, through their capacity to analyze large and complex datasets, demonstrate significant potential in identifying irregularities and suspicious activities within accounts. Moreover, integrating these technologies offers a viable pathway for minimizing human error while elevating the overall quality of auditing. The findings further indicate that their adoption not only streamlines audit procedures via automation but also fortifies investor and stakeholder trust. Ultimately, these results carry important implications for regulators, auditors, and financial institutions in establishing innovative governance models and promoting higher levels of transparency in global financial markets.