Dynamic Risk Assessment Engines for Lending Using Verified Blockchain Transactions

Abstract

The increasing velocity and complexity of financial transactions demand risk assessment systems that operate continuously, transparently, and adaptively. This paper proposes a Dynamic Risk Assessment Engine (DRAE) that ingests cryptographically verified blockchain transactions alongside cross-platform financial feeds to deliver real-time risk signals for lending decisions. The engine couples event-driven smart contracts and IoT/ledger attestations to enable continuous monitoring and automated risk triggers, extending prior work on blockchain-enabled continuous assessment in peer-to-peer lending [1],[6]. We incorporate secure access and predictive intelligence modules to synthesize transactional, behavioral, and supply-chain signals into hybrid risk scores, informed by advances in smart automation and AI for financial security [2],[9]. Cross-platform unification and zero-trust integration reduce information silos and strengthen fraud detection and compliance pipelines [3],[4]. The architecture explicitly targets SME financing and supply-chain finance use cases, addressing credit gaps through provenance-aware underwriting and lightweight on-chain proofs [5],[7],[8]. Grounded in risk-engineering principles for complex ICT systems [10], the DRAE is evaluated on latency, detection lead time, predictive accuracy, and auditability. Experimental results on simulated and anonymized enterprise transaction streams indicate improved early-warning capabilities and reduced false positives compared to periodic-batch scoring baselines. We conclude by discussing operational trade-offs,privacy, scalability, and governance and outline pathways for integrating causal risk models and trusted off-chain computation to enhance deployability in regulated lending environments.