Adversarially Resilient Financial Risk Forecasting with Split-Trained Transformer Language Models

Abstract

The integration of transformer-based language models into financial risk forecasting has enabled unprecedented accuracy in capturing semantic and temporal dependencies within heterogeneous data streams. However, the deployment of such models in real-world financial infrastructures introduces critical vulnerabilities to adversarial manipulation, particularly when training is distributed across multiple custodians to comply with privacy and regulatory constraints. This paper proposes and analyzes a system architecture for adversarially resilient financial risk forecasting that leverages split-trained transformer language models. Split training partitions the model across different parties, thereby preserving data locality while enabling collaborative learning. The central contribution is the formalization of an adversarial threat model tailored to the split-training paradigm, encompassing poisoning, evasion, and backdoor attacks that target both the feature extractor and the classification head. We synthesize recent advances in defense mechanisms, including gradient sanitization, prototype consistency verification, and robust aggregation protocols, and evaluate their effectiveness under realistic financial data distributions. Through a multi-layered analysis of architectural trade-offs, we examine how communication overhead, model fidelity, and privacy guarantees interact with adversarial resilience. The paper further discusses the broader governance, fairness, and sustainability implications of deploying such systems in critical financial infrastructure, emphasizing the need for regulatory frameworks that address adversarial robustness as a core design requirement. Our findings indicate that while no single defense is sufficient, a combination of prototype-based validation and differential privacy offers a promising path toward trustworthy financial AI. The proposed framework provides a foundation for future empirical validation and policy development in the intersection of machine learning security and financial regulation.