ARTIFICIAL INTELLIGENCE IN FRAUD DETECTION FOR DIGITAL FINANCIAL SERVICES: EFFICIENCY GAINS AND ETHICAL RISKS

Abstract

 The rapid expansion of digital financial services has increased efficiency and accessibility but has also amplified exposure to fraud. Artificial Intelligence (AI), particularly machine learning and deep learning algorithms, has emerged as a powerful tool for detecting and preventing fraudulent transactions in real time. This thesis examines the efficiency gains generated by AI-driven fraud detection systems and evaluates the associated ethical risks, including privacy concerns, algorithmic bias, and transparency issues. Drawing on secondary data, case studies, and comparative analysis of digital financial platforms, the study finds that AI significantly reduces fraud losses and operational costs while improving detection speed and accuracy. However, ethical risks remain substantial, especially regarding data governance and fairness. The paper concludes with policy and managerial recommendations to balance technological innovation with ethical responsibility.