Risko1: Reasoning for Risk Management Governed by Structural Constraints

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.