AI Agent Governance for Reinsurance Claims Recovery

Why Claims Recovery Agents Require Governance Rigor

Reinsurers are introducing AI agents into claims recovery workflows to handle data extraction, treaty interpretation, subrogation analysis, and reconciliation across claims and financial systems. These tasks require agents to read policy and treaty documents, query claims management platforms, and in some cases interact with financial systems to identify or validate recoverable amounts. This pattern places AI agents in a position functionally similar to a system-to-system integration or a privileged service account: a non-human identity acting autonomously across sensitive data and financial workflows. General agentic AI security guidance, including OWASP's work on LLM applications, identifies excessive agency (unnecessary permissions, functionality, or autonomy granted to an agent) as a distinct risk category. In a claims recovery context, this translates to a concrete operational question: does the agent have standing access to more claims data, treaty terms, or financial systems than a specific task requires, and can that access be constrained and observed at runtime rather than assumed to be safe by design.

The Governance Gap in Ungoverned Agent Access

Claims recovery workflows typically span multiple systems: a claims management platform, a policy or treaty administration system, and financial reconciliation tools. When an AI agent is granted broad, standing credentials to move across these systems without per-action authorization, several gaps emerge. There is no consistent enforcement point verifying that a specific tool call, such as querying a treaty database or flagging a recoverable, falls within the agent's intended scope. There is no reliable record of what data the agent accessed, what it concluded, and what action it took, undermining after-the-fact review. NIST's Zero Trust Architecture guidance (SP 800-207) addresses this class of problem for general information systems, establishing that access decisions should be made per-request based on identity, context, and least privilege rather than granted as standing, session-level access. That principle applies directly to agents making repeated tool calls across claims systems, though it was not written with AI agents in mind and must be extended deliberately rather than assumed to transfer automatically.

Implementation Approach

Governance and Regulatory Considerations

The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers directs insurers to maintain documented AI governance programs, including monitoring and testing of AI-driven decisions. It applies broadly to insurer use of AI, including claims, but does not specify technical controls for autonomous agent tool-calling or reinsurance recovery workflows. Where the bulletin has been adopted by a state, governance leaders should map agent-specific controls (identity scoping, audit logging, approval checkpoints) into existing AI governance program documentation rather than treating agent oversight as a separate compliance track. Audit trail expectations under general IT control frameworks such as NIST SP 800-53 can be extended to agent actions, but organizations must independently determine whether the resulting logs are sufficient for regulatory examination or reinsurance counterparty audit, since no supplied standard confirms a reinsurance-specific audit benchmark for AI agents. Accountability for agent-initiated recovery determinations, including who approves an action and who is responsible for an erroneous one, remains a governance design decision that no technical control resolves on its own.

Practical Guardrails for Claims Recovery Agents

• Treat each AI agent as a privileged, tool-using identity requiring the same access rigor as a system-to-system integration
• Scope access to the specific task and data field, not the full claims or treaty platform
• Enforce authorization at each tool call rather than relying on upstream prompt restrictions
• Log tool invocations with enough detail to reconstruct a specific recovery decision after the fact
• Require human approval for financially consequential actions such as initiating a recovery or adjusting a reserve
• Rotate and review agent credentials on the same cadence as other privileged non-human identities

Where Runtime Governance Tooling Fits

The control patterns described here (distinct agent identity, least-privilege scoping, per-call policy enforcement, and centralized audit logging) are architectural requirements independent of any specific vendor. Trussed AI provides runtime governance and security for enterprise AI agents, including agent identity management, permission scoping, tool approval workflows, and audit logging, applied at the point where an agent calls a tool or system rather than only at prompt design time. For governance leaders evaluating how to operationalize these controls across claims, treaty, and financial systems, runtime enforcement is the layer that determines whether policy is actually applied at the moment an agent acts, rather than documented as intent.