Multi-Agent AI Architecture for Regulated Insurers: A generic AI framework under Solvency II and the AI Act in Austria and Germany
Source: <https://arxiv.org/abs/2609.27636>
Authors: Walter Kurz
Published: 2026-09-23
Categories: q-fin.GN, cs.MA, q-fin.RM
arXiv: 2609.27636
Abstract
This paper proposes a formal multi-agent architecture for implementing enterprise AI in regulated insurance firms, integrating economic theory with institutional design. The framework synthesises three core theoretical perspectives: Arrow's risk pooling theory to formalise risk transformation under uncertainty, Nash equilibrium to model strategic interactions between decision agents, and Principal-Agent theory to address incentive alignment under information asymmetry. The insurer is modelled as a constrained optimisation entity operating under solvency, legal, ESG, and operational boundaries, with specific focus on the regulatory contexts of Austria and Germany. The architecture decomposes the firm into multiple specialised agents, each representing distinct functional domains such as capital management, underwriting, claims processing, compliance, fraud detection, and client interaction. Human-in-the-loop agents are integrated through a tiered access control system, ensuring differentiated data visibility and decision influence based on user roles. An orchestrator agent supervises inter-agent coordination, enforcing regulatory admissibility and institutional coherence under frameworks such as Solvency II, the AI Act, and the Insurance Distribution Directive. Protocol integration is based on asynchronous execution and dual-layer communication infrastructures, specifically the Model Context Protocol (MCP) and Agent-to-Agent (A2A) messaging. This structure enables the systematic design of compliant, auditable multi-agent systems aligned with the institutional logic of financial firms in Austria and Germany.
Summary
A formal multi-agent architecture for enterprise AI in regulated insurers (Austria/Germany, Solvency II + AI Act context). Grounded in three economic lenses: Arrow's risk-pooling theory formalises risk transformation under uncertainty; Nash equilibrium models strategic interactions between decision agents; Principal-Agent theory handles incentive alignment under information asymmetry. The insurer is a constrained optimisation entity under solvency/legal/ESG/operational boundaries, decomposed into specialised agents (capital management, underwriting, claims, compliance, fraud detection, client interaction). Human-in-the-loop is integrated via tiered access control (role-based data visibility and decision influence); an orchestrator enforces regulatory admissibility and institutional coherence (Solvency II, AI Act, IDD). Protocol layer: asynchronous execution over dual-layer MCP + A2A messaging, enabling compliant, auditable multi-agent system design.
摘要
面向受监管保险企业的形式化多智能体架构（奥地利/德国 Solvency II 与 AI Act 语境），将经济理论与制度设计结合：以 Arrow 风险汇集理论形式化不确定性下的风险转换，用纳什均衡建模决策 agent 间的策略互动，用委托-代理理论处理信息不对称下的激励对齐。保险企业被建模为偿付能力、法律、ESG、运营约束下的优化实体，按职能分解为资本管理、承保、理赔、合规、反欺诈、客户交互等专用 agent；human-in-the-loop 通过分级访问控制实现基于角色的差异化数据可见性与决策影响；orchestrator agent 监督 agent 间协调，在 Solvency II、AI Act、保险分销指令等框架下强制监管可采性与制度一致性。协议层采用异步执行与双层通信基础设施（MCP + A2A 消息），支持系统化设计合规、可审计的多智能体系统。