فهرست نشریات علمی دسترسی آزاد ایران

The aim of the research is to design a knowledge model for accounting fraud detection based on digital innovations with a focus on human-technology interaction in organizations. The increasing spread of digital innovations and the increasing complexity of financial processes have fundamentally changed the nature of accounting fraud in organizations and challenged the effectiveness of traditional fraud detection approaches. In such circumstances, relying solely on technological tools or human judgments alone is not sufficient to detect fraud in a timely and effective manner, but the synergy of human knowledge and the capacities of digital technologies has become doubly important. The structural interaction analysis method of MICMAC software was used in information processing. Based on the results obtained, 10 criteria (big data analytics, fraud machine learning, audit artificial intelligence, smart transaction tracking, encryption and transparency, financial process automation, hidden behavioral data mining, digital anomaly detection, financial blockchain platform, and continuous real-time monitoring) were categorized into 7 levels. This approach, by creating synergy between the professional knowledge of accountants and auditors, the analytical capabilities of smart technologies, and organizational knowledge sharing and learning platforms, enables more timely and accurate identification of fraud patterns. The results of such a model can lead to improved financial transparency, strengthened stakeholder trust, improved supervisory decision-making, and organizations moving toward predictive and knowledge-based control systems; which will ultimately play an important role in improving the economic health and financial governance of organizations.