用法律条款约束强化学习，让再保险报价更透明合规。
ClauseLens: Clause-Grounded, CVaR-Constrained Reinforcement Learning for Trustworthy Reinsurance Pricing
- 将条款嵌入智能体观测与动作约束，实现可解释决策
- 降低51%偿付能力违规，尾部风险改善27.9%（CVaR_0.10）
- 生成88.2%准确的条款解释，适合监管合规与审计场景
再保险合约定价需满足严格的监管标准，但现有报价方式仍不透明且难以审计。本文提出ClauseLens，一种基于条款的强化学习框架，生成透明、合规且风险敏感的合约报价。将报价任务建模为风险感知约束马尔可夫决策过程（RA-CMDP），从法律与承保语料库中检索法定及政策条款，嵌入智能体观测，既约束可行动作，又生成基于条款的自然语言解释。在基于行业数据校准的多智能体模拟器中评估，ClauseLens将偿付能力违规减少51%，尾部风险性能提升27.9%（CVaR_0.10），条款解释准确率达88.2%，检索精度87.4%，召回率91.1%。结果表明，将法律上下文融入决策与解释路径，可实现符合Solvency II、NAIC RBC及欧盟《人工智能法案》的可解释、可审计报价行为。
原文摘要 · Abstract (English)
Reinsurance treaty pricing must satisfy stringent regulatory standards, yet current quoting practices remain opaque and difficult to audit. We introduce ClauseLens, a clause-grounded reinforcement learning framework that produces transparent, regulation-compliant, and risk-aware treaty quotes. ClauseLens models the quoting task as a Risk-Aware Constrained Markov Decision Process (RA-CMDP). Statutory and policy clauses are retrieved from legal and underwriting corpora, embedded into the agent's observations, and used both to constrain feasible actions and to generate clause-grounded natural language justifications. Evaluated in a multi-agent treaty simulator calibrated to industry data, ClauseLens reduces solvency violations by 51%, improves tail-risk performance by 27.9% (CVaR_0.10), and achieves 88.2% accuracy in clause-grounded explanations with retrieval precision of 87.4% and recall of 91.1%. These findings demonstrate that embedding legal context into both decision and explanation pathways yields interpretable, auditable, and regulation-aligned quoting behavior consistent with Solvency II, NAIC RBC, and the EU AI Act.
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