Artificial intelligence and algorithmic decisions in fraud detection: An interpretive structural model

Abstract The use of artificial intelligence and algorithmic decision-making in public policy processes is influenced by a range diverse drivers. This article provides comprehensive view 13 drivers their interrelationships, identified through empirical findings from the taxation social security domains Belgium. These are organized into five hierarchical layers that designers need to focus on when introducing advanced analytics fraud detection: (a) trust layer, (b) interoperability (c) perceived benefits (d) data governance (e) digital layer. layered approach enables holistic assessing adoption challenges concerning new technologies. research uses thematic analysis interpretive structural modeling.