Transparency in the Digital Era: Leveraging AI to Enhance Fraud Prevention in Indonesian Higher Education
Year: 2025
Type: article
Abstract: This study investigates the breadth and depth of fraud prevention disclosures among 76 Indonesian higher education institutions and explores the transformative role of Artificial Intelligence (AI) in enhancing governance and early fraud detection systems. Utilizing a qualitative content analysis approach, the research assesses institutional transparency across eight critical dimensions: integrity and governance, policy, internal audit, bursary, core values, fraud prevention strategies, fraud response procedures, and awareness raising, based on publicly available data from 2025. The analysis reveals an average disclosure rate of 48.19 percent, with the lowest levels found in fraud response procedures (22.3 percent) and awareness raising (26.2 percent), while Bandung Institute of Technology (ITB) and Jambi University (UNJA) recorded the highest disclosure levels at 85 percent. These findings expose a pronounced gap between substantial governance and implementation practices, indicating that institutions often prioritize formal compliance over substantive ethical conduct. The study highlights the urgent need for standardized disclosure frameworks at the national level, the institutionalization of AI-based analytical systems, and the reinforcement of ethical governance structures to ensure transparency and accountability. The integration of AI technologies in audit automation, anomaly detection, and ethical capacity building presents a forward-looking framework for proactive fraud prevention and governance modernization. This study extends existing fraud prevention disclosure research by integrating AI-based governance perspectives within the higher education context, which has been largely unexplored in Indonesia, and offers a strategic roadmap for policymakers and academic leaders seeking to restore public trust through integrity, regulatory coherence, and technological innovation.
Source: Dangdai jingji kexue
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7127088492