Edge-Intelligent Finance: Evaluating the Efficiency of Edge AI in Decentralized Credit Risk Assessment and Real-Time Lending Decisions

Abstract

Cloud-based centralized credit scoring is accurate but suffers from network delays, high bandwidth costs, privacy concerns, and non-transparent decision-making processes. We present an edge intelligent finance framework that combines shallow neural models with symbolic, rule-based reasoning to make real-time, transparent loan decisions in the wild over low-cost edge devices. Trained via federated learning on popular benchmarks (German Credit, LendingClub, FICO), the framework achieves competitive discrimination performance (AUC ≈ 0.83) while reducing inference latency to 50ms and energy use to 0.3J/inference compared to their centralized deep models. An interpretable validation layer is proposed to encode fairness constraints and regulation rules, which outputs the interpretable rationales and group-gap metrics (<0.05). Under bandwidth constraints, stress tests indicate that accuracy and response times remain stable, as inference is performed locally. Our results demonstrate that under edge AI, efficient lending workflows and peer-to-peer ecosystems can be enhanced with privacy, fairness, and compliance through a series of information yellow-red-green cycles. We also address scalability, rule complexity, and deployment recommendations for financial institutions and regulators.