ARTIFICIAL INTELLIGENCE IN FINANCIAL FRAUD DETECTION: FROM ALGORITHMS TO MANAGERIAL DECISIONS

In the ongoing arms race between financial institutions and fraudsters, artificial intelligence is often heralded as the ultimate safeguard for transactional security. But is technological perfection alone a guarantee of success? This article explores the critical, often overlooked disconnect between state-ofthe- art algorithms and their real-world economic viability and managerial applicability. The authors posit that the primary barriers to creating effective anti-fraud systems lie not in the mathematics of the models, but in the intricate trade-offs between speed and accuracy, transparency and predictive power, and the necessity to adapt to the unique logic of diverse financial sectors. It invites the reader to view fraud detection not as a purely technical challenge, but as a complex managerial problem where strategic decisions are paramount. The paper presents a novel, holistic perspective on building systems that are not only accurate but also economically sustainable, operationally robust, and compliant with an evolving regulatory landscape.