ENHANCING REGTECH FRAMEWORKS FOR MONITORING RISK DYNAMICS IN MODERN DIGITAL PAYMENT ECOSYSTEMS

Abstract

The rapid proliferation of instant settlement rails, decentralized architectures, and cross-border transaction protocols has heightened systemic vulnerabilities across modern financial infrastructures, introducing non-linear operational, liquidity, and financial crime risks. Traditional supervisory frameworks, characterized by periodic compliance reporting and static rule-based heuristics, exhibit prohibitive latency and fail to capture multi-hop illicit flows or sudden contagion across interconnected nodes. This study develops an adaptive Regulatory Technology (RegTech) framework engineered to monitor real-time risk dynamics within high-frequency digital payment ecosystems. Integrating dynamic graph topology analysis with unsupervised anomaly detection architectures, the proposed model captures latent counterparty interconnectedness, structural transaction velocity shifts, and automated money-laundering vectors. Empirical validation using high-dimensional payment settlement data demonstrates that this framework reduces systemic anomaly identification latency from hours to sub-second intervals while decreasing false-positive compliance noise by 38.6% relative to standard benchmark models. Furthermore, stress-testing under simulated liquidity shocks reveals enhanced predictive power regarding inter-institutional settlement bottlenecks. The results provide central monetary authorities and supervisory bodies with a scalable, data-driven architecture to transition from reactive ex-post audits toward continuous, proactive macro-prudential oversight, thereby preserving financial stability within digitized payment environments.