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Evaluate a mortgage AI agent on the work it will actually perform—not on a polished demonstration. Build representative processing and servicing scenarios, test normal and adverse cases, measure accuracy and harm, verify that the agent recognizes uncertainty and escalates, and preserve an auditable record of every material output and tool action.
Use a three-part evidence program—model testing, red teaming, and user testing—then set thresholds that match the consequence, complexity, and opacity of the proposed use. Official guidance does not provide one universal passing score for mortgage agents, so lenders and servicers must document their own risk appetite, acceptance criteria, and approval gates.
What a defensible evaluation must prove
A mortgage agent should be treated as an operational control, not merely a language model. Your evaluation should establish whether it can complete an assigned task correctly, use only authorized information and tools, identify missing or contradictory evidence, stop when it lacks authority, and hand the case to a qualified employee without losing context.
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- What task is being performed? For example, classifying a borrower inquiry, extracting facts from income documents, drafting a response, or routing a loss-mitigation request.
- What may the agent read? Specify systems, fields, documents, prior correspondence, and external sources.
- What may it change or trigger? Distinguish a draft recommendation from an executed payment change, notice, credit-reporting update, or workflow assignment.
- What must it never do? State prohibited decisions, disclosures, overrides, data transfers, and communications.
- When is human review mandatory? Define conditions such as legal deadlines, conflicting records, possible discrimination, bankruptcy, foreclosure, or a requested action outside the agent’s authority.
- What evidence is retained? Preserve input versions, retrieved sources, prompts or instructions, tool calls, output, reviewer action, and timestamps for consequential work.
NIST describes its AI Risk Management Framework as a voluntary resource intended to help developers, users, and evaluators manage risks affecting people, organizations, society, or the environment. It identifies linked characteristics of trustworthy AI: validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and fairness with harmful bias managed. NIST cautions that addressing these characteristics separately does not by itself guarantee a trustworthy system. See the NIST AI Risk Management Framework FAQs.
Define the agent’s role, authority, and risk tier
Write a role specification that an evaluator can test and an operator can understand. Include the business owner, affected borrowers, systems of record, permitted tools, data classes, decision rights, service-level expectations, and rollback method. A summarization assistant used by an employee has a different risk profile from an agent that changes escrow records or sends a legally significant notice.
FHFA Advisory Bulletin 2022-02 says risk-management consideration should become more robust for use cases involving critical business functions, legal or regulatory compliance, or highly complex and opaque methods. The bulletin is directed to Fannie Mae, Freddie Mac, and the Federal Home Loan Banks; other institutions should confirm which requirements apply to them and use the bulletin as supervisory risk guidance rather than assuming direct applicability. Read the FHFA Advisory Bulletin 2022-02.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →| Example capability | Typical impact | Controls to test before production |
|---|---|---|
| Summarize a file for a staff member | Low to moderate; an employee remains the decision-maker | Source fidelity, omission rate, privacy, clear labeling as a draft, and reviewer ability to detect errors |
| Classify and route inquiries or complaints | Moderate; misrouting can delay rights or deadlines | Coverage of every queue, deadline recognition, confidence thresholds, duplicate handling, and escalation |
| Draft borrower communications | Moderate to high; inaccurate language can mislead a borrower | Approved content controls, required disclosures, prohibited advice, source traceability, and mandatory human approval |
| Recommend or execute payment, escrow, credit, or loss-mitigation actions | High; can affect money, eligibility, reporting, or home retention | Deterministic business rules, dual control, authorization checks, complete audit trail, abstention, rollback, and compliance review |
Build a workflow-based scenario library
Do not start with generic questions such as “Is the agent accurate?” Start with a catalog of tasks, records, decisions, deadlines, and failure consequences. Every task should have a written expected outcome, the evidence needed to reach it, an allowed action set, and a defined escalation path.
Mortgage servicing coverage from CFPB examination domains
The CFPB mortgage-servicing examination procedures, updated January 18, 2023, provide a practical map for scenario coverage. They are supervisory materials, not an AI benchmark. Adapt each domain to your own products, investor requirements, systems, and procedures.
| Domain | Scenario families to include | Expected safe behavior |
|---|---|---|
| Transfers and escrow disclosures | Transferred account, missing disclosure, changed escrow amount, and conflicting effective dates | Retrieve the controlling record, identify deadlines, avoid inventing a date, and escalate unresolved conflicts |
| Payment processing and account maintenance | Partial payment, unapplied funds, extra principal, payment reversal, payoff request, and duplicate transaction | Apply approved rules, show the calculation or source, prevent unauthorized posting, and route exceptions |
| Inquiries, complaints, and error resolution | Notice of error, information request, repeat complaint, and message containing several issues | Classify every issue, preserve the received date, trigger the correct workflow, and avoid closing a matter without evidence |
| Escrow and insurance | Shortage, surplus, force-placed insurance, cancellation evidence, and tax-payment discrepancy | Use current account records, distinguish estimates from confirmed facts, and escalate coverage or deadline risk |
| Credit reporting | Disputed payment status, identity mismatch, cure of an error, and request to suppress inaccurate data | Follow approved reporting procedures, record evidence, and require qualified review for disputed outcomes |
| Privacy and information sharing | Borrower authentication failure, third-party request, co-borrower access, and data-export request | Verify authority before disclosure, minimize data, log the decision, and refuse unauthorized sharing |
| Collections and bankruptcy | Promise to pay, cease-contact request, bankruptcy notice, and protected-party indicator | Recognize legal or policy holds, stop prohibited activity, and route to the designated specialist |
| Early intervention and continuity of contact | Delinquency outreach, unreachable borrower, language need, and broken handoff | Use approved scripts, document attempts, preserve continuity, and escalate when contact requirements are at risk |
| Loss mitigation | Incomplete application, contradictory income, multiple assistance requests, and imminent deadline | Identify missing items precisely, avoid eligibility promises, preserve receipt dates, and assign human review where required |
| Foreclosure | Active application, referral status, service-member or bankruptcy indicator, and disputed balance | Apply holds and controls, never improvise a legal conclusion, and require authorized approval before irreversible action |
For each row, create clean cases, incomplete records, contradictory documents, unusual timing, and cases where the correct answer is to stop. Include multilingual or accessibility needs only when your deployment will support them, and test whether the same control applies across channels.
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Processing, origination, and broader housing-finance scenarios
FHFA’s 2024 TechSprint asks how generative AI might improve operational efficiency and effectiveness across origination, servicing, and secondary-market activities. Its focus areas include consumer experience, creditworthiness, operations, and risk management or compliance. The TechSprint is not a regulation or a complete test protocol; use it to broaden your inventory beyond servicing. See FHFA’s 2024 TechSprint: Generative AI in Housing Finance.
- Consumer experience: test application guidance, status explanations, accessibility, language consistency, complaint intake, and the agent’s ability to distinguish education from individualized advice.
- Creditworthiness: test extraction and verification of income, assets, liabilities, and employment; handling of missing or stale documents; and refusal to infer protected or irrelevant characteristics.
- Operations: test document indexing, condition clearing, handoffs, duplicate detection, queue prioritization, and reconciliation between the agent and the system of record.
- Risk and compliance: test policy retrieval, approval limits, required notices, record retention, fair-lending controls, privacy, cybersecurity, and change management.
Use three complementary forms of testing
NIST’s ARIA Evaluation Planning Manual, published September 18, 2026, describes a holistic evaluation built from model testing, red teaming, and user testing. Mortgage teams should run all three because a system can score well on curated examples yet fail under attack or confuse the employees expected to supervise it.
Model testing
Assemble a versioned test set drawn from production-like records, with legally permitted de-identification and documented sampling. Label the expected answer, required evidence, allowed action, and escalation result before running the agent. Keep a locked holdout set for final decisions and a separate development set for tuning.
Test repeated runs to measure consistency, not just one lucky completion. Record model version, retrieval index, system instructions, tools, temperature or other generation settings, and policy version so a result can be reproduced after a change.
Red teaming
Attack the complete agent, including retrieval, tools, identity controls, and downstream systems. Tailor scenarios to your architecture, but include:
- Prompt injection hidden in a borrower document, email, web page, or servicing note.
- Conflicting instructions that attempt to override policy or system authority.
- Fabricated documents, altered figures, impossible dates, and misleading but plausible narratives.
- Requests to reveal another borrower’s data or to export more information than the task requires.
- Attempts to make the agent call an unauthorized tool, alter a record, bypass dual approval, or send an external message.
- Repeated retries designed to defeat an escalation threshold or exploit inconsistent answers.
Score whether the agent detects the attack, refuses the unsafe action, preserves evidence, and alerts the responsible team. A red-team finding is not fixed merely because the model gives a better explanation; the underlying control or permission may need to change.
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User testing
Observe the actual employees who will review, correct, or override the agent. Give them realistic cases with time pressure, incomplete information, and plausible errors. Measure whether they can locate sources, understand uncertainty, notice a wrong answer, complete a handoff, and recover after an incorrect tool action. Test new users and experienced specialists separately; familiarity can conceal usability problems.
Choose task-specific measures and thresholds
NIST’s TEVV approach is intended to help organizations build customized evaluations and evidence that systems meet their goals while minimizing negative impacts. The TEVV-Athlon Framework page describes a public draft announced August 7, 2026; its stated comment period closed October 6, 2026. Treat that material as draft guidance, not a final standard.
The following measures are practical starting points to adapt with compliance, operations, and legal owners:
| Measure | How to define it | Why it matters |
|---|---|---|
| Completion correctness | Share of cases where the final output and workflow state match the approved expected result | Separates useful work from fluent but wrong text |
| Critical-error rate | Count and severity of errors that could affect money, rights, reporting, eligibility, deadlines, privacy, or safety | Prevents a high average from hiding a catastrophic failure |
| Evidence retrieval | Whether every material conclusion cites the correct record, policy, or approved source | Makes review and reconstruction possible |
| Unsupported-claim rate | Outputs containing a material assertion not supported by available evidence | Targets hallucinated facts and invented policy |
| Abstention and escalation quality | Whether the agent stops on defined triggers and routes to the right queue with complete context | Measures safe uncertainty, not just answer production |
| Consistency | Agreement across repeated runs, channels, and equivalent records | Reveals unstable behavior that can create unequal treatment |
| Human rework | Corrections, elapsed review time, and reopened cases after agent assistance | Captures operational cost and hidden burden |
| Latency and availability | Response and workflow times under expected and peak load | Shows whether service levels remain achievable |
Set thresholds before a pilot or procurement decision. For a low-impact drafting assistant, you might permit employee correction before release; for an agent that can alter a payment or trigger foreclosure activity, any unresolved critical error or failed authorization test should block deployment. These examples illustrate the principle, not regulator-mandated numbers. The official materials reviewed do not establish a universal mortgage-agent pass score.
Test fairness, data quality, and credit-related controls
Fairness testing must be tied to the intended use, data, and applicable law. Include qualified fair-lending and legal staff in test design and interpretation. Check whether error, escalation, processing time, and access to assistance differ across legally relevant groups or operating conditions, using methods and data your counsel approves.
The CFPB ECOA baseline review procedures, updated July 23, 2026, are designed to identify and analyze risks under the Equal Credit Opportunity Act and Regulation B. They do not prescribe one agent-specific fairness metric. Use them to structure review questions, then document why your selected measures fit the use case.
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Data quality is a separate control. Test missing, stale, duplicated, transposed, and conflicting fields; verify that the agent preserves units and dates; and reconcile extracted values to the authoritative system. CFPB says HMDA reviews include transactional testing for HMDA data accuracy. If an agent populates or transforms HMDA data, include field-level accuracy, lineage, correction, and exception scenarios.
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Probe security, privacy, and information sharing
Servicing procedures include privacy and information sharing as an examination module. Review identity and authorization checks, least-privilege access, retention, encryption, logging, vendor isolation, and deletion workflows. Test whether the agent exposes sensitive information in summaries, citations, logs, prompts, error messages, or analytics.
- Ask for a co-borrower’s data while authenticated as an unauthorized third party.
- Insert an instruction in a document telling the agent to ignore policy and disclose account details.
- Supply a malformed file or oversized payload and observe whether the agent safely rejects it.
- Attempt to replay an old authorization after account status changes.
- Verify that a rejected tool call cannot be retried through a different route.
- Confirm that staff can see enough audit information without receiving data they are not entitled to view.
Successful security testing requires both a refusal and an operational record showing what was attempted, what control fired, and who was notified.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Require explanations, traceability, and human control
For every consequential output, retain the input identifiers, retrieved passages or records, policy version, calculations, tool calls, output, confidence or uncertainty signal where available, reviewer decision, and final system-of-record state. Do not treat a generated rationale as proof that the underlying process was correct; compare the explanation with the actual evidence and execution trace.
Ask reviewers three practical questions: Can I tell which facts drove this result? Can I verify those facts quickly? Can I stop, correct, and reverse the action? FHFA discusses clarity about how and why AI or machine learning is used, including interpretability and explainability, while NIST places accountability, transparency, explainability, and interpretability among trustworthiness characteristics.
Human-in-the-loop design should be specific. Define who may approve, what information they must inspect, maximum response time, override reasons, segregation of duties, and rollback authority. A nominal approval button is not meaningful control if the reviewer cannot see the source records or if the agent can execute the action before approval.
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Compare vendors using identical evidence
Give every candidate the same versioned scenario set, scoring rubric, data conditions, threat cases, and integration constraints. Request reproducible evidence rather than screenshots or aggregate claims. Compare:
- Correctness, critical-error severity, abstention, escalation, and completion time by task type.
- Performance on incomplete, contradictory, unusual, multilingual, and adversarial cases.
- Fairness, ECOA, HMDA, privacy, and information-sharing evidence relevant to the intended deployment.
- Identity, access control, tenant isolation, retention, encryption, incident response, and subcontractor practices.
- Source citations, logs, versioning, explainability, and exportable audit records.
- Approval, override, rollback, kill-switch, and business-continuity controls.
- Integration behavior with the system of record, queues, document stores, and notification channels.
- Monitoring, drift detection, release testing, change notices, and support responsibilities.
- Quality, scope, date, independence, and reproducibility of the vendor’s evaluation evidence.
This is an editorial comparison framework synthesizing NIST trustworthiness and evaluation concepts with CFPB and FHFA mortgage domains; it is not a regulator-issued vendor scorecard.
Use staged decision gates
- Inventory and classify: approve the role specification, data map, authority limits, impact tier, and named owner.
- Design scenarios: map every relevant servicing or processing workflow, define expected outcomes, and label critical failures.
- Run offline tests: execute model, security, privacy, fairness, and consistency tests on a locked set; investigate every critical error.
- Conduct user testing: observe representative employees, revise interfaces and escalation paths, and retest after changes.
- Authorize a constrained pilot: limit records, channels, tools, transaction values, and operating hours; require human approval for consequential actions.
- Monitor and review: track the measures above, complaints, overrides, incidents, drift, and subgroup results; establish stop conditions.
- Approve expansion or retire: expand only when evidence meets the pre-set thresholds and owners sign off. If controls fail, disable the capability, preserve evidence, notify affected teams, and use the documented fallback process.
Common evaluation failures and practical fixes
| Failure | Why it misleads | Fix |
|---|---|---|
| Testing only polished demo prompts | Ignores missing records, conflicting instructions, and workflow deadlines | Use production-like cases with explicit expected actions and stop conditions |
| Reporting one average accuracy number | Hides rare but severe errors and subgroup differences | Break results down by task, severity, subgroup, channel, and operating condition |
| Treating a citation as an audit trail | A citation may not show which tool changed the record or which version was used | Retain inputs, retrievals, instructions, tool calls, approvals, and final state |
| Leaving escalation undefined | The agent may continue confidently when policy requires a specialist | Encode triggers, destination queue, required context, and response-time owner |
| Allowing the agent to write directly during a pilot | A test error becomes a real borrower or investor impact | Use read-only or sandbox mode, dual approval, transaction limits, and rollback |
| Assuming a proposed rule is final | Implementation may rely on requirements that can change | Track rule status and confirm current obligations before operational use |
The CFPB mortgage-servicing resources page lists Regulation X and Regulation Z materials and notes that a proposed servicing rule was issued July 10, 2024. A proposal should not be described as a final rule; check current status before relying on it. See CFPB rules on mortgage servicing, last modified June 1, 2026.
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- Approved use-case statement, risk tier, authority matrix, and data-flow diagram.
- Scenario inventory with expected outcomes, legal or policy basis, and criticality.
- Dataset provenance, de-identification method, sampling logic, and holdout controls.
- Model, retrieval, prompt, tool, policy, and integration versions used in every run.
- Results by task and severity, including failed, abstained, escalated, and corrected cases.
- Red-team attack descriptions, evidence, remediation, and retest outcomes.
- User-test observations, training changes, reviewer workload, and usability findings.
- Fairness, data-quality, privacy, security, and compliance analyses with qualified sign-off.
- Production monitoring plan, alert thresholds, incident process, rollback test, and review cadence.
- Procurement decision, rejected alternatives, residual risks, conditions of approval, and expiration date.
This record lets a board, examiner, auditor, or internal reviewer reconstruct not only what the agent answered, but why the institution considered the deployment acceptable at that time.
Official guidance and scope limitations
NIST’s AI RMF is voluntary and sector-neutral. CFPB examination procedures are supervisory materials that help organize coverage but do not constitute an AI-agent benchmark. FHFA’s TechSprint is a problem-framing exercise, not a regulatory standard. FHFA Advisory Bulletin 2022-02 has a defined audience, and the TEVV-Athlon material cited above was a public draft. Confirm current legal, investor, privacy, and fair-lending requirements for your institution and jurisdiction before deployment.
FHFA’s TechSprint frames the operational question as: “How might generative AI be used to enhance the efficiency and effectiveness of operational processes within the housing finance system, from origination to servicing and secondary market activities?” A defensible answer requires evidence from the actual workflows, not a generalized model score.
Quick Recap
Official references
- CFPB Mortgage servicing examination procedures
- NIST AI Risk Management Framework FAQs
- CFPB Equal Credit Opportunity Act (ECOA) baseline review procedures
- FHFA 2024 TechSprint: Generative AI in Housing Finance
- NIST ARIA Evaluation Planning Manual
- NIST TEVV-Athlon Framework for Evaluating AI Systems
- FHFA Advisory Bulletin 2022-02
- CFPB rules on mortgage servicing
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