The growing complexity of digital corporate ecosystems has intensified exposure to financial and economic crimes, rendering traditional rule-based audit systems increasingly ineffective. Artificial intelligence (AI) and advanced analytics have emerged as...
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The growing complexity of digital corporate ecosystems has intensified exposure to financial and economic crimes, rendering traditional rule-based audit systems increasingly ineffective. Artificial intelligence (AI) and advanced analytics have emerged as transformative instruments in corporate governance, enabling the transition from reactive fraud investigation to predictive risk prevention. This study systematizes the evolution of AI-driven approaches to fraud and financial risk detection, integrating technological, managerial, and ethical perspectives within a unified analytical framework. The research aimed to trace the progression from early statistical and rule-based systems to explainable, adaptive AI architectures, identifying methodological innovations and governance implications. A mixed-methods design was employed, combining quantitative model testing with qualitative triangulation. The empirical dataset comprised 2.7 million corporate transactions and 480,000 procurement re
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