AI-Powered Fraud Detection in Financial Networks: A Systematic Literature Review.

The financial sector's transition to AI-driven fraud detection requires new strategies for technology adoption that balance operational efficacy, data privacy, and strict regulatory governance. Existing literature often focuses on algorithmic performance, neglecting the critical architectural and managerial implications. The study aimed to systematically review the literature to provide an evidence-based pathway for engineering managers concerning the operationalization, architecture, and governance of AI-powered fraud detection systems (RQ1-RQ3). A Systematic Literature Review (SLR) was conducted across IEEE Xplore, Scopus, and other high-impact databases (2000–2026). A hybrid thematic analysis was performed on the extracted data to develop a Grounded Theory of AI Adoption Pathways. The analysis established that operational efficacy (RQ1) relies on Graph Neural Networks to counter complex fraud rings. Architectural strategy (RQ2) mandates Distributed Intelligence, with Federated Learning confirmed as a tool for collaboration under GDPR constraints and Edge-Cloud hybrids achieving sub-100ms latency. Governance (RQ3) requires mandatory XAI for regulatory compliance and continuous bias monitoring, with bias audits revealing a 22% higher false positive rate in some cross-border transactions. Successful AI deployment is a strategic management decision requiring new architectural and governance frameworks to ensure regulatory trust and operational resilience. Future empirical research should focus on a formal MLOps Maturity Model to benchmark organizational readiness for bias mitigation and adversarial robustness.