AI and Analytics for Detecting Fraud and Financial Risks

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 records from multinational enterprises across the manufacturing, retail, and financial sectors. Analytical methods included supervised learning (SVM, Random Forest), deep learning (LSTM, CNN), and graph neural networks (GNNs), supported by explainability techniques such as SHAP and LIME. Results demonstrated that AI integration reduced financial losses by 38–44% and improved detection precision (F1-score = 0.90). Sectoral analysis revealed distinct advantages: unsupervised anomaly detection in manufacturing, temporal learning in retail, and graph-based modeling in financial institutions. The inclusion of explainable AI enhanced transparency, regulatory compliance, and stakeholder trust, while embedding models within ISO 31000:2018-based risk management frameworks improved overall organizational resilience. The findings confirm that AI transforms fraud detection into a predictive and ethically governed function of corporate management. Future research should focus on developing multimodal, self-learning systems that combine numerical, textual, and behavioral data streams, ensuring both predictive accuracy and ethical transparency in the evolving digital economy.