Machine Learning in Market Risk and Model Risk Governance: A Framework for Regulatory-Compliant Adoption in Investment Fund Contexts

The progressive displacement of classical parametric models by machine learning architectures in financial risk management presents a structural tension that remains undertheorized in the literature: as predictive accuracy improves, model interpretability and regulatory defensibility frequently deteriorate. This paper examines that tension across two interconnected domains, market risk quantification and model risk governance, with particular attention to investment fund environments operating under AIFMD and equivalent frameworks. 

On the market risk side, the paper evaluates the performance of ML-native approaches, including long short-term memory networks, gradient boosting ensembles, and hidden Markov regime-switching models, against traditional Value-at-Risk and Expected Shortfall methodologies under Basel III/IV constraints. It argues that ML models offer material advantages in tail risk estimation and volatility regime detection, but introduce non-trivial validation challenges that incumbent frameworks were not designed to address.

On the governance side, the paper develops a model risk management framework adapted from SR 11-7 and EBA Guidelines on Internal Models, calibrated to the operational realities of alternative investment managers. The framework addresses model inventory, independent validation, concept drift monitoring, and explainability requirements as constituent pillars of a second-line-of-defense posture.

The contribution is twofold: a structured methodology for risk-adjusted ML model adoption in market risk, and a governance architecture that renders that adoption auditable and supervisory-ready. The paper concludes that competitive advantage in next-generation risk management lies not in model sophistication per se, but in the institutional capacity to govern sophisticated models rigorously.