Adversarially Robust Machine Learning for Financial Risk Prediction under Distribution Shifts

Abstract

Financial risk prediction is increasingly mediated by machine learning systems that operate in non-stationary markets, adversarial environments, and highly regulated institutional settings. Although predictive accuracy under historical test distributions remains a dominant evaluation criterion, it offers limited assurance when market regimes shift, data-generating processes change, or strategic actors manipulate inputs and signals. This paper examines adversarially robust machine learning for financial risk prediction through a system-level lens, emphasizing architecture, infrastructure, governance, deployment, sustainability, fairness, and policy. It argues that robustness cannot be treated as a narrow model property added after training; rather, it must be designed into socio-technical pipelines that include data acquisition, feature construction, validation, uncertainty calibration, human oversight, auditability, and incident response. The analysis connects adversarial machine learning, domain adaptation, invariant learning, and distributionally robust optimization to the distinctive properties of financial markets, including heavy tails, volatility clustering, regime breaks, reflexive behavior, and regulatory constraints. It further considers how calibration and uncertainty quantification shape risk decisions, how fairness concerns intersect with systemic stability, and how operational sustainability depends on monitoring, documentation, and governance capacity. Case illustrations from credit scoring, fraud detection, market risk, and anti-money laundering show that robustness failures often emerge from institutional misalignment rather than from isolated algorithmic defects. The paper concludes with a research and policy agenda that treats adversarial robustness as an infrastructural obligation, requiring coordinated advances in model design, validation, accountability, and regulatory supervision.