Predictive Analytics of Stablecoin De-Pegging Events: Deploying Distributed AWS Middleware for Real-Time Blockchain Anomaly Detection

Abstract

The rapid expansion of decentralized finance has introduced unprecedented systemic risks, most notably the phenomenon of stablecoin runs. Traditional econometric models analyzing financial fragility rely heavily on retrospective data, which is insufficient for tracking high-velocity, algorithmic bank runs on blockchain networks. This paper proposes a cloud-native architectural solution utilizing distributed Amazon Web Services middleware to ingest, normalize, and analyze blockchain ledger data in real-time. By deploying an asynchronous Python orchestration pipeline integrated with eXtreme Gradient Boosting and K-Nearest Neighbors algorithms, the proposed system identifies transaction velocity anomalies indicative of panic-selling and de-pegging events. This methodology fundamentally shifts the analysis of stablecoin fragility from theoretical post-mortem to programmatic, real-time detection. Preliminary architectural evaluations demonstrate that decoupling the data ingestion layer from the predictive inference engine significantly reduces latency, providing financial regulators and researchers with a scalable, deterministic tool for monitoring digital asset stability.