Integrated Trust Architectures: AI-Driven Fraud Detection And Blockchain-Enhanced Security For Digital Financial Ecosystems

Abstract

This article presents an integrated, theoretically rigorous synthesis of contemporary approaches to securing digital financial ecosystems through the combined application of artificial intelligence (AI) for real-time fraud detection and blockchain technologies for data integrity, privacy, and trust. The confluence of AI and distributed ledger technologies offers promising avenues to mitigate rising threats in fintech, yet challenges remain in operationalization, governance, scalability, and ethical deployment. Building on a structured review of recent empirical and conceptual contributions, this work explicates core mechanisms—stream processing architectures for low-latency detection, cryptographic anchoring and immutability via blockchain, multifactor authentication practices, and standards of deployment in banking environments—and synthesizes these into a conceptual architecture termed the Integrated Trust Architecture (ITA). The ITA foregrounds design principles that align AI-driven anomaly detection modules with blockchain-backed provenance records, enabling auditable, privacy-preserving, and resilient responses to fraud attempts. The article further interrogates trade-offs involving latency, throughput, regulatory compliance, and reputational risk, and presents a layered methodological approach for researchers and practitioners to evaluate and implement these systems ethically and effectively. Limitations of extant literature are critically examined, and a detailed research agenda is proposed to guide rigorous field experimentation, cross-disciplinary evaluation, and standards development. The synthesis emphasizes that while AI and blockchain individually contribute vital capabilities, their purposeful integration—coupled with appropriate governance and human oversight—offers the most viable path toward sustained trust in digital financial services.