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Multi-Agent AI Architecture for Regulated Insurers: A generic AI framework under Solvency II and the AI Act in Austria and Germany

Walter Kurz

arXiv:2609.27636v2q-fin.GNcs.MAq-fin.RM

TL;DR

Task-specific insurance AI typically optimises local metrics without modelling institutional objectives or regulatory constraints. This paper proposes a theoretical multi-agent framework that represents regulated insurers as constrained optimisation systems and maps their functions to coordinated agents. Its supported conclusion is a foundation for auditable, compliant AI architecture in regulated institutional settings.

  • Problem

    Existing insurance AI focuses on task-specific applications and local performance metrics rather than systemic modelling of insurer behaviour with institutional objectives and regulatory constraints.

    Source: §1.3 · p. 2

  • Method

    The paper formalises profit-driven insurance firms as constrained optimisation systems and maps their functions to bounded-rationality agents operating under shared institutional constraints.

    Source: §1.4 · p. 3, §2 · pp. 3, 5

  • Results

    The framework provides a structural basis for compliant, auditable AI systems by embedding regulatory, operational, and governance constraints into decision logic.

    Source: §6 · p. 13

  • Takeaways & Limitations

    The architecture frames regulated insurers as distributed decision systems whose agents operate within shared legal, financial, and ESG boundaries under system-level coordination.

    Source: §6 · p. 13

    The analysis is limited to profit-oriented private insurers, excluding the distinct statutory and member-benefit objectives of public-law and mutual insurers.

    Source: §1.2 · p. 2

Abstract

from arXiv · show

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.

1 Introduction

The paper develops a theoretical framework for modelling profit-oriented private insurers in Austria and Germany as constrained optimisation systems implemented through a decentralised multi-agent architecture. It addresses the limits of task-specific insurance AI by formalising firm functions and mapping them to agents.

1 Introduction

  • The paper treats insurers as constrained optimisation systems suitable for decentralised multi-agent implementation, deliberately avoiding task-level AI applications.Its focus is profit-oriented private insurers and objectives consistent with utility or surplus maximisation under bounded rationality.Source: §1 · p. 1

1.1 Institutional Scope: Insurance Markets in Austria and Germany

  • Austria and Germany share Solvency II and harmonised EU supervision, but differ in ownership patterns: German insurers include substantial mutual and public-law sectors, while Austria has stronger private joint-stock concentration.These institutional similarities support joint treatment, while market differences remain relevant.Source: §1.1 · p. 2

1.2 Taxonomy of Insurance Firms

  • Because public-law, mutual, and joint-stock insurers have different mandates and objectives, the analysis focuses on profit-oriented private insurers subject to solvency and compliance constraints.Public-law insurers have statutory duties and limited discretion, while mutuals may prioritise member benefits over profit.Source: §1.2 · p. 2

1.3 Related Work

  • The framework draws on Arrow’s risk pooling, principal-agent theory, and Nash equilibrium to represent risk transfer, information asymmetry, and strategic interaction in insurer behaviour.Organisational theory also supports decomposing operations into semi-autonomous subfunctions under compliance and incentive constraints.Source: §1.3 · p. 2
  • Prior insurance AI work focuses on operational tasks and local performance metrics, leaving systemic modelling of insurer behaviour and its institutional objectives and regulatory constraints unaddressed.Examples include fraud detection, customer profiling, claims triage, and document processing.Source: §1.3 · p. 2

1.4 Contribution

  • The framework formalises profit-driven insurers’ core functions as economically grounded constrained optimisation problems mapped to bounded-rationality agents with shared utility goals.It is proposed as a foundation for architecture-level AI implementation rather than task-oriented applications.Source: §1.4 · p. 3

2 Formal Modelling of Insurance Firm Functions

The section models insurance firms as constrained optimisation systems and combines risk pooling, incentive compatibility, and strategic equilibrium in a unified decision architecture. Its constraints include capital, regulation, ESG, operations, and interactions among decision units.

2 Formal Modelling of Insurance Firm Functions

  • The firm maximises surplus or expected utility subject to capital, risk, legal, ESG, and operational constraints, including Solvency II, IDD, AI Act, GDPR, and sustainability requirements.Capital adequacy requires expected liabilities not to exceed available capital; risk exposure is bounded by a Solvency II threshold.Source: §2 · p. 3
  • The Lagrangian representation treats binding constraints as implicit costs, with multipliers expressing the shadow price of marginally relaxing each constraint.This makes the opportunity costs of legal, financial, and institutional boundaries explicit in expected utility.Source: §2 · p. 4
  • Arrow’s framework represents insurance as risk pooling that transforms individual uncertainty into collective stability while maintaining insurer solvency and improving insureds’ expected utility.The model uses a strictly concave utility function to reflect risk aversion and pooled liabilities.Source: §2 · p. 4
  • Principal-agent theory adds informational constraints and incentive compatibility to account for private information and delegated decisions within contracts and firm governance.Nash equilibrium adds strategic consistency where firms’ decisions depend on competitors’ strategies, including premium setting and capital allocation.Source: §2 · pp. 4–5
  • A unified formulation treats the firm as interdependent decision units whose local actions and utilities contribute to a shared objective, subject to capital, incentive, strategic, and external admissibility constraints.The model provides a formal basis for decomposing the firm into agents operating under shared rules and limits.Source: §2 · p. 5

3 Functional Decomposition and Agent Architecture

The architecture decomposes regulated insurers into autonomous functional agents coordinated by an orchestrator under firm-level constraints. It integrates role-based human access and describes capital management and underwriting as distinct decision functions.

3 Functional Decomposition and Agent Architecture

  • A decentralised multi-agent architecture assigns modular, goal-directed roles to insurer functions and uses an orchestrator to enforce global regulatory and strategic logic.The paper argues that confidentiality, auditability, and institution-specific objectives motivate proprietary infrastructure.Source: §3 · pp. 5–6
  • Human-in-the-loop agents use tiered access that varies data visibility and decision authority by user role, while LLMs can provide employee-, client-, shareholder-, and regulator-facing communication.Some agents track governance and compliance rather than directly optimising financial flows.Source: §3 · p. 6
  • The model assumes a profit-oriented insurer that maximises financial surplus subject to internal and regulatory constraints.This scope is narrower than insurers with statutory or mutual objectives.Source: §3 · p. 6

Capital Management Agent

  • The capital management agent allocates capital, reserves, and liquidity to maintain solvency and support growth, balancing safety against the opportunity cost of holding capital.Its solvency constraint can use a predefined risk threshold, with 99.5% given as an example for Solvency II.Source: Capital Management Agent · p. 6

Underwriting Agent

  • The underwriting agent accepts, prices, and classifies applications using observable indicators under information asymmetry, while limiting individual and aggregate risk exposure.Its objective is expected underwriting profit subject to expected-loss and capital constraints.Source: Underwriting Agent · pp. 6–7

Claims Handling Agent

The claims agent assesses and settles claims under uncertainty, balancing contractual payouts and processing costs with fraud screening. A parallel fraud agent flags high-risk cases for further review without directly determining financial outcomes.

Claims Handling Agent

  • The claims agent assesses, verifies, and settles uncertain claims while balancing legitimate payment, contractual payout limits, delay costs, and fraud screening.Payout depends on latent claim type; the fraud score threshold triggers additional screening.Source: Claims Handling Agent · p. 7

Fraud Detection Agent

  • The fraud agent uses probabilistic models, anomaly detection, or learned patterns to flag anomalous or inconsistent claims and applications for review, modifying other agents’ decision space rather than directly changing financial outcomes.Its objective accounts for expected true and false positives, with a penalty parameter discouraging excessive false-positive filtering.Source: Fraud Detection Agent · pp. 7–8

Compliance and Legal Agent

The compliance agent filters internal actions for legal and ethical admissibility, while the employee interface mediates human access through role-based controls and auditability.

Compliance and Legal Agent

  • The compliance and legal agent checks internal decisions and product features against regulations and governance norms, including Solvency II, the IDD, GDPR, the AI Act, and ESG disclosure rules.It constrains other agents’ action spaces rather than directly optimising profit.Source: Compliance and Legal Agent · p. 8
  • The agent screens proposed actions and treats an action as admissible only after legal and ethical review; violations of legal, environmental, social, or governance rules trigger exclusion.ESG-S&G includes procedural fairness, transparency, stakeholder inclusion, and decision traceability.Source: Compliance and Legal Agent · p. 8

Employee Interface Agent

  • The employee interface tailors access to agent outputs to each employee’s organisational role while enforcing data visibility, governance, and data-protection controls.Role authorisation, tier-based data isolation, and logging, timestamping, and auditability constrain responses.Source: Employee Interface Agent · p. 8
  • Employee queries are filtered and recorded, with access tiers restricting each person to information authorised for their role.The system is designed to protect sensitive data while keeping interactions traceable.Source: Employee Interface Agent · pp. 8–9

Stakeholder Interface Agent

The stakeholder interface assigns external users visibility tiers and resolves their queries within lawful, contractual, and purpose-specific boundaries.

Stakeholder Interface Agent

  • The stakeholder interface assigns visibility tiers according to each external actor’s contractual position, regulatory entitlement, or information rights.It aims to provide relevant, interpretable information without exceeding confidentiality or regulatory limits.Source: Stakeholder Interface Agent · p. 9
  • Stakeholder queries use filtered agent context and are authenticated, restricted against excess data exposure, and made traceable for supervision.Responses are adapted to jurisdiction, stakeholder contract, and purpose.Source: Stakeholder Interface Agent · p. 9
  • Access reflects legal rights, contractual roles, and regulatory status: policyholders can see personal contract and claim information, regulators compliance data and audit trails, and shareholders profitability and capital information.A role registry and smart-contract enforcement dynamically govern access rights.Source: Stakeholder Interface Agent · p. 9

Client Service Agent

The client service agent provides policyholders with personalised, contract-linked information while limiting disclosures to their entitlements and recording exchanges for audit.

Client Service Agent

  • The client service agent handles policyholder questions about coverage, claim progress, premium adjustments, renewals, and contractual terms within legal, contractual, and data-protection boundaries.Its objective is to improve customer clarity and decision-readiness.Source: Client Service Agent · pp. 9–10
  • Responses draw on permitted queries under the active contract, personalised policy attributes, and dynamic operational information such as claim updates or payment schedules.The LLM interface turns structured internal data into accessible, legally compliant answers, and exchanges are auditable.Source: Client Service Agent · p. 10
  • Access is limited to currently entitled policyholders and varies with policy type, status, and applicable legal protections.Examples include reimbursement timelines during an open health claim and premium comparisons near property-policy renewal.Source: Client Service Agent · p. 10

Client Acquisition Agent

Client acquisition pre-qualifies prospects for underwriting within marketing and fairness constraints, while the system orchestrator validates coordination and institutional consistency across agents.

Client Acquisition Agent

  • The client acquisition agent maps prospect profiles to product recommendations and pre-filters applications for underwriting relevance, subject to marketing compliance and fairness requirements.It operates upstream of underwriting and supports lead generation, suitability screening, and pre-contractual information.Source: Client Acquisition Agent · p. 10
  • The acquisition agent prepares prospects for underwriting through pre-application guidance while seeking transparent, equitable, and regulatorily compliant processes.Its interfaces support product comparison, consent dialogue, and onboarding FAQs.Source: Client Acquisition Agent · p. 10

System Orchestrator Agent

  • The system orchestrator checks whether independently optimised local decisions collectively satisfy the organisation’s legal, financial, and operational constraints, then authorises or rejects execution based on validation.It does not perform direct optimisation.Source: System Orchestrator Agent · pp. 10–11
  • The orchestrator propagates constraints across agents, checks consistency of role permissions, and detects cross-agent conflicts such as contradictory objectives or mutual constraint violations.It may override, veto, or delay decisions that would introduce institutional incoherence, constraint violations, or regulatory breaches.Source: System Orchestrator Agent · p. 11

4 Unified Agent System and Communication Logic

The unified optimisation structure brings internal-agent decisions and human-facing queries under one system-wide admissibility and institutional-alignment check. Interface agents apply role-based data controls and route outputs through the orchestrator.

4 Unified Agent System and Communication Logic

  • The orchestrator checks that internal actions and interface queries are jointly admissible, non-contradictory, and institutionally consistent before execution.The global constraint requires actions and queries to belong to the system's valid set.Source: §4 · pp. 11–12
  • Employee, stakeholder, and client interface agents enforce role-based data filtering and audit logs, then route outputs through the orchestrator.The orchestrator validates resulting actions and queries against the firm's institutional constraints.Source: §4 · pp. 11–12
  • Internal agents maximise expected utility from their actions, while human-facing agents return informational or operational utility from filtered context.Internal actions are restricted to legally, environmentally, socially, and operationally permitted domains.Source: §4 · p. 11

5 Protocols and Agent Integration in Enterprise AI

The paper combines asynchronous coordination with complementary communication and context protocols for heterogeneous enterprise agents. Its proposed insurance architecture uses this dual-protocol approach with persistent decision memory to address institutional and regulatory demands.

5 Protocols and Agent Integration in Enterprise AI

  • Asynchronous coordination decouples data availability from execution, mitigating latency and fragility in heterogeneous systems with partial observability and uneven real-time data access.The passage notes that communication delays can grow non-linearly with message size and agent count.Source: §5 · p. 12
  • A2A standardises messaging and interoperability, while MCP supports semantic coherence, context propagation, and persistent memory.Together, the protocols provide technical interoperability and contextual alignment across environments.Source: §5 · p. 12
  • The proposed insurance architecture combines asynchronous operation with explainable, persistent decision memory to meet institutional and regulatory demands.The passage identifies protocol-level design as central to enterprise AI.Source: §5 · pp. 12–13

6 Conclusion

The conclusion presents a formal architecture for regulated insurers that embeds institutional and regulatory constraints in firm decision logic. It decomposes functions into bounded-rationality agents coordinated by an orchestrator and supported by protocol infrastructure.

6 Conclusion

  • The framework models regulated insurers as structured decision systems constrained by solvency, legal admissibility, ESG obligations, and internal feasibility.It grounds the architecture in firm theory, constrained optimisation, and institutional logic.Source: §6 · p. 13
  • Capital management, underwriting, claims, and compliance are represented as bounded-rationality agents operating within shared constraints.Their local decision models optimise within admissible legal, financial, and ESG boundaries.Source: §6 · p. 13
  • An orchestrator validates inter-agent alignment, propagates global constraints, and maintains institutional consistency.Asynchronous coordination, memory persistence, and MCP/A2A interoperability support communication across heterogeneous modules.Source: §6 · p. 13
  • The architecture embeds regulatory, operational, and governance constraints in decision logic rather than treating compliance as an external layer.The paper presents this as a foundation for auditable, compliant AI systems in high-stakes institutional domains.Source: §6 · p. 13

Declarations

The declarations provide article metadata, data and code availability, and disclosures concerning AI-tool use, funding, and conflicts of interest.

Declarations

  • The article is a 2025 research article by W. Kurz, published in Swissi AI Journal.The listed article identifier is SAIJ-qzvrl4bwy7y2.Source: Declarations · p. 15
  • The research received no external funding, and the author declares no conflicts of interest.Source: Declarations · p. 15
  • Supporting data and code are available from the corresponding author on reasonable request.Source: Declarations · p. 15
  • Generative AI tools were used for editorial work only, and the author states responsibility for the article's content.The stated uses include language editing and formatting.Source: Declarations · p. 15
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