Quantum Finance, Explainable AI, and RegTech: Future Pathways for Nonlinear Systemic Risk Detection
Main article
Abstract
Nonlinear systemic risk in emerging-market banking systems eludes conventional linear econometric tooling, and the recent literature on Quantum Field Theory (QFT)–inspired financial modelling shows that hidden phase transitions and metastable states can be made tractable through functional structures. This paper synthesises three converging technological frontiers—quantum-inspired finance, Explainable Artificial Intelligence (XAI), and Regulatory Technology (RegTech)—into a unified analytical pathway for detecting nonlinear systemic risk. We propose a layered framework in which quantum-inspired indicators feed ensemble machine-learning classifiers whose outputs are decomposed via Shapley-value attribution and surfaced through RegTech dashboards for supervisors. Drawing on an illustrative annual panel of Mexican commercial banks from 2014 to 2023, we demonstrate that an integrated XAI-plus-quantum specification reaches an AUC of 0.89 against a 0.62 baseline, identifies non-performing-loan dynamics and capitalization buffers as dominant attributors, and discriminates resilient from vulnerable institutions through latent functional trajectories. The framework yields three contributions: a conceptual bridge connecting field-theoretic risk indicators with interpretable ML, an empirically calibrated pipeline that is auditable end-to-end, and a discussion of how RegTech adoption can operationalise these tools for prudential supervision. We close by mapping research and policy pathways for nonlinear early-warning systems in emerging economies.
