Governing large language models in bank loan servicing hardship conversations: runtime policy enforcement and examiner-ready evidence

Why hardship conversations need interaction-level control

Hardship conversations are a difficult place for a language model. A borrower asking about forbearance, modification or payment relief is often under financial stress, is sharing sensitive financial and personal details, and is relying on the answer to make a decision. Four risk points follow from that: the model makes a commitment the bank has not authorized, it gives inaccurate or misleading statements about relief options or eligibility, it exposes or mishandles sensitive borrower data, or it fails to hand a case to a person when human judgment is required.

The CFPB has said that automated customer service tools, including those built on large language models, may provide incorrect information and raise privacy and security risks. It has also stated that ECOA and the prohibition on unfair, deceptive or abusive acts or practices apply to servicing and debt collection, including the options companies provide to struggling consumers. Its June 2023 chatbot report found that effectiveness wanes as problems become more complex. These are agency statements from 2023 and 2024, not new rules, but they describe the same exposure a risk leader has to manage in hardship workflows.

Reviewing transcripts after the fact tells the bank what happened. It does not stop a non-compliant response from reaching a borrower. That gap is the reason to add a control that acts while the interaction is in progress.

Pre-deployment, runtime and after-the-fact controls

The revised interagency model risk management guidance, issued April 17, 2026 (OCC Bulletin 2026-13, with a matching FDIC press release), covers model development and use, validation and monitoring, governance and controls, and considerations for vendor and other third-party products. It describes effective practice as including model testing, validation of conceptual soundness, outcomes analysis, and clear policies, roles and responsibilities. The Federal Reserve notes that outcomes analysis and validation may identify material errors or persistent deviations outside established performance thresholds.

Those elements map naturally onto three control timings. This mapping is a design inference, not a supervisory requirement.

Before deployment, the bank tests and validates the model for its intended hardship use, documents that use and its limits, and classifies each use case as assist or automate. After the fact, the bank runs outcomes analysis and monitoring against thresholds it sets, including complaint and escalation trends. These controls find systematic deviations, but they work on completed interactions.

Runtime control is the third category. Policy is evaluated in the execution path, so a response can be allowed, redacted, blocked or held for human approval before the borrower sees it. Of the three timings, it is the only one that can change the outcome of the interaction in progress. It also depends on the other two, because the policies it enforces come from validation findings and monitoring results.

Map each risk point to a policy action

Start by inventorying hardship and loss mitigation use cases, then write policy for each risk point rather than for the model in general. The following mapping is a starting design for the bank's own policy owners, compliance and counsel to refine. Escalation triggers should be explicit enough that a reviewer can tell afterward why a case was or was not routed to a person.

• Unauthorized commitments: Block or escalate for human approval any response that promises approval, terms, fee treatment or timelines the assistant is not authorized to give.
• Inaccurate relief or eligibility statements: Block responses that state eligibility or option details outside approved content, and route unresolved questions to a servicer.
• Sensitive data exposure: Redact account, identity and financial details that are not needed for the step in progress, in prompts, tool calls and responses.
• Vulnerability or ambiguity cues: Escalate for human approval or handoff when the borrower signals distress, confusion, a dispute, or a request the policy does not cover.
• Routine informational responses: Allow general, approved explanations of process and next steps, and record that the policy permitted them.

Evaluating runtime enforcement and where it stops

When assessing any runtime enforcement approach for LLM-assisted servicing, ask five questions. Does it act before the response reaches the borrower, with the four actions above? Does it cover every model, tool and channel in the servicing workflow, including vendor and third-party models, since the guidance extends to vendor products? Does it produce a per-interaction record of the policy applied, the decision, redactions and approvals? Can retention and data residency be configured? Can it run in the bank's own cloud, and what data leaves that environment?

The limits matter as much as the capabilities. Interaction-level enforcement does not establish that a model is conceptually sound, and it does not replace testing, validation, outcomes analysis or governance. It does not by itself show that the bank's use of automated tools avoids prohibited bias, a question the CFPB has said can attach to the decision to use such tools under ECOA. It also does not make the bank's loss mitigation process compliant. Regulation X, 12 CFR 1024.41, sets procedures that begin with concepts such as a complete loss mitigation application, and counsel should confirm which obligations apply to each workflow. Enforcement can help hold a response to those decisions, but the decisions themselves belong to the bank.

Assign clear roles for policy ownership, exception handling and reviewer authority, and bring ECOA and UDAAP review into model approval. The NIST AI RMF, a voluntary reference, treats governance as cross-cutting across its other functions, which fits this approach.

Where Trussed fits

Trussed enforces policy in the execution path across models, agents, tools and MCP servers, with actions that include allow, redact, block and escalate for human approval. Its scope is individual interactions and tool calls. Enforcement generates audit-ready evidence, and deployment can be self-managed in the customer cloud with configurable retention and data residency. Trussed does not replace model validation, outcomes analysis or a bank's loss mitigation controls.

Insurance, a regulated financial sector, is where Trussed is deepest. Trussed has not claimed bank or other financial-services deployments, so a bank should evaluate it against the criteria above. General operating proof includes 18+ months of continuous production in a regulated enterprise, 99.95% uptime on live production traffic, and 100+ models under one policy plane.