Risko1: Reasoning for Risk Management Governed by Structural Constraints

Marco Molinari, Deasy Darlene Tunas, Saharsha Navani, Tianyu Gao

Keywords: Foundation Models under Market Constraints, Risk Management, Scenario Generation

TL;DR: We introduce Risko1, a constraint-aware generative model for accurate financial reasoning and effective scenario generation for risk management

Abstract:

Risk management is a crucial task for both individual investors and financial institutions that seek to identify and quantify the risks they are exposed to. We introduce Risko1, an 8B-parameter financial reasoning model trained with Group Relative
Policy Optimization (GRPO) on both textual context and financial information. It
identifies specific risks to which companies are exposed and quantifies their impact
in terms of standard risk metrics: Value at Risk (VaR), Conditional Value at Risk
(CVaR), and Volatility. These metrics must strictly satisfy fundamental constraints
such as CVaRα > VaRα and monotonicity across confidence levels. Performance
is slightly above the much larger Llama 3.3 70B in accuracy, and roughly on par in
Mean Squared Error (MSE). Beyond quantitative ability, we analyze the quality of
the risk scenarios generated. Regulators require institutions to establish controls
to mitigate risk exposure. This is done using a risk taxonomy that classifies risks
across tiers based on granularity, with 1 being a broad category (for instance,
operational risks), and 4 being the most granular (a specific event). Controls are
enacted at the appropriate level of granularity. We explore the distribution of tiers
of generated risks, and find that they are coherent with the given context (mainly
market and operational) and granular (mostly tier 3 and 4), and hence amenable to
mitigation, as controls may be assigned effectively.

Submission Number: 111