AI’s Dual-Use Paradox in Financial Fraud: Navigating Offense and Defense in the Digital Era

This study addresses the dual-use paradox of artificial intelligence (AI) in digital financial fraud, illustrating how the same technologies can both strengthen defensive systems and enable increasingly adaptive attacks. The research adopts a qualitative–conceptual methodology, aimed at developing a theoretical framework that captures this paradox and highlights the AI asymmetry gap between offensive and defensive capabilities. The evidence indicates that this gap is widening due to reactive detection models, data-access constraints, and fragmented intelligence sharing. Although applied solutions (e.g., Mastercard Decision Intelligence Pro; SymphonyAI Sensa Copilot) report improvements in detection accuracy and investigative efficiency, their effectiveness appears constrained by regulatory, institutional, and data limitations. The study proposes a framework that integrates hybrid AI architectures, cross-institutional intelligence networks, adversarial-AI research, and AI-specific regulatory measures.