CONVERGENT TRUST FRAMEWORKS: INTEGRATING AI-DRIVEN REAL-TIME FRAUD DETECTION WITH BLOCKCHAIN-BASED IDENTITY AND PROVENANCE FOR RESILIENT DIGITAL BANKING

Abstract

This article develops a comprehensive theoretical and practical synthesis on integrating artificial intelligence (AI)–based real-time fraud detection with blockchain-enabled identity, access control, and provenance mechanisms for modern digital banking ecosystems. The work draws on a curated set of contemporary studies spanning stream-processing fraud detection, blockchain architectures for identity and data integrity, biometric and multifactor authentication practices, and the socio-technical governance necessary for trustworthy financial services (Hebbar, 2025; Liao et al., 2022; Nguyen et al., 2020; Ravi, 2021). The paper articulates a Convergent Trust Framework (CTF) that reconciles operational requirements—low-latency detection, explainability, privacy preservation, and regulatory compliance—with cryptographic and ledger design choices—permissioned ledgers, off-chain commitments, and selective disclosure strategies (Hassani et al., 2018; Ahmad et al., 2024). Methodologically, the article synthesizes thematic evidence, performs rigorous conceptual integration, and derives design propositions and implementation pathways for practitioners while mapping a research agenda to address empirical gaps. Findings highlight that stream-processing AI systems (e.g., Kafka-based architectures) can achieve near-real-time detection but must be coupled with tiered explainability and adaptive retraining to reduce false positives and model drift (Hebbar, 2025; Grammatikos & Papanikolaou, 2021). Blockchain layers offer tamper-evident anchors for forensic artifacts and identity claims, yet throughput, privacy, and governance constraints necessitate hybrid on-chain/off-chain designs and permissioned consortium models for banking contexts (Liao et al., 2022; Uriawan et al., 2025). The framework emphasizes human-in-the-loop governance, policy-driven smart contracts, and privacy-preserving cryptographic commitments as essential for operationalizing trust. Limitations of current research—scarcity of large-scale field deployments and empirical adversarial evaluations—are delineated, and a multi-year empirical program combining pilot deployments, red-team exercises, and regulatory impact studies is proposed. This integrated perspective aims to move research and practice beyond isolated technological silos toward resilient, auditable, and user-centered financial infrastructures.