This article synthesizes contemporary scholarship on the application of artificial intelligence (AI) within corporate finance, regulatory compliance, and fraud risk management to propose an integrative theoretical and practical framework. Drawing on recent empirical and conceptual studies that examine AI-enabled decision-support systems, natural language processing, machine learning pipelines, robotic process automation, and governance-oriented automation, the paper foregrounds how these technologies reconfigure the detection, prevention, and oversight functions that underpin corporate financial integrity. The abstracted contributions are threefold. First, the article develops a layered taxonomy linking AI capabilities (predictive modeling, anomaly detection, transaction monitoring, text analytics) to specific corporate finance functions (reporting quality, treasury operations, audit trails, regulatory reporting), highlighting the pathways through which AI can enhance efficiency and decision quality while introducing new operational and ethical risks (Rane, 2024; Hassan et al., 2023). Second, it elaborates design principles for ethically scalable automation and governance architectures that reconcile performance with regulatory transparency and accountability (Lin, 2024; Ajmal et al., 2025; Adeyelu et al., 2024). Third, the paper advances concrete approaches for AI-driven fraud risk management that combine model robustness, human-in-the-loop processes, interpretability, and regulatory oversight practices for emerging markets with constrained institutional capacity (Asad, 2025; Aziza et al., 2023). Throughout, the analysis critically examines trade-offs—between automation and human judgment, predictive power and fairness, throughput and auditability—offering prescriptive recommendations for practitioners, policymakers, and researchers. The article concludes with a research agenda identifying pressing empirical tests, methodological improvements, and policy reforms needed to realize AI’s promise in corporate finance without compromising governance, financial stability, or equitable outcomes.

Artificial intelligence, corporate finance, regulatory compliance, fraud risk management, governance, automation, interpretability

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