Self-Supervised Learning for Regime-Aware Financial Risk Representation

Abstract

This paper examines self-supervised learning as a foundation for regime-aware financial risk representation. Financial systems are non-stationary, and risk exposures often shift abruptly across volatility, liquidity, credit, and policy regimes. Conventional supervised models depend on labeled outcomes that are scarce, delayed, and unstable across structural breaks. Self-supervised learning offers a route to learn dense representations from unlabeled market, transactional, textual, and alternative data, while regime-aware design aligns those representations with changing market states. The paper develops a system-level argument rather than a narrow modeling argument. It considers representation objectives, temporal and cross-sectional architectures, data infrastructure, model deployment, monitoring, robustness, fairness, governance, and sustainability. It argues that regime-aware self-supervision can improve early warning, stress testing, portfolio risk estimation, and counterparty surveillance, but only when embedded in resilient socio-technical infrastructure. Key tensions include representation generality versus regime specificity, low-latency inference versus deep contextual modeling, data reuse versus privacy and lineage, and adaptive learning versus regulatory stability. The paper also compares financial risk representation with adjacent domains such as healthcare, energy, and supply-chain analytics. It concludes that regime-aware self-supervised representation is best understood as an infrastructural capability requiring evaluation protocols, model documentation, human oversight, and policy alignment.