The Roles of Regulatory Pressure, Organisational Support, and Data Quality in AI Capability Development for Fraud Reduction in Financial Institutions - Agnes Bieattant; Gatot Soepriyanto (Journal of Mathematics Instruction, Social Research and Opinion, 2026)
The rapid expansion of digital financial services in Indonesia has increased fraud risks and intensified demands for technological transformation. Although artificial intelligence (AI) is increasingly applied in fraud detection, the process through which governance conditions contribute to fraud reduction remains insufficiently understood. This study examines the development of AI capabilities within Indonesian financial institutions by integrating the Technology–Organization–Environment (TOE) framework and the Information Systems Success Model. AI capability development is conceptualized through three stages: AI Integration, AI Assimilation, and AI Effectiveness. Survey data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that regulatory pressure does not directly improve fraud reduction outcomes. Instead, regulatory and organizational conditions support Data Quality, facilitating AI Integration and strengthening subsequent AI capability development. Fraud reduction is achieved only when AI systems reach operational effectiveness. The results suggest that fraud reduction represents an outcome of organizational capability development rather than a direct consequence of regulatory pressure or technological implementation alone.